| "Lossless" Compression of Deep Neural Networks: A High-dimensional Neural Tangent Kernel Approach |
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✅ |
✅ |
❌ |
❌ |
❌ |
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3 |
| $\alpha$-ReQ : Assessing Representation Quality in Self-Supervised Learning by measuring eigenspectrum decay |
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❌ |
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6 |
| $k$-Sliced Mutual Information: A Quantitative Study of Scalability with Dimension |
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✅ |
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2 |
| (De-)Randomized Smoothing for Decision Stump Ensembles |
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6 |
| (Optimal) Online Bipartite Matching with Degree Information |
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5 |
| 360-MLC: Multi-view Layout Consistency for Self-training and Hyper-parameter Tuning |
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3 |
| 3D Concept Grounding on Neural Fields |
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✅ |
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1 |
| 3DB: A Framework for Debugging Computer Vision Models |
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3 |
| 3DILG: Irregular Latent Grids for 3D Generative Modeling |
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4 |
| 4D Unsupervised Object Discovery |
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❌ |
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5 |
| A Best-of-Both-Worlds Algorithm for Bandits with Delayed Feedback |
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1 |
| A Boosting Approach to Reinforcement Learning |
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3 |
| A Causal Analysis of Harm |
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0 |
| A Character-Level Length-Control Algorithm for Non-Autoregressive Sentence Summarization |
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❌ |
✅ |
5 |
| A Characterization of Semi-Supervised Adversarially Robust PAC Learnability |
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❌ |
❌ |
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1 |
| A Classification of $G$-invariant Shallow Neural Networks |
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3 |
| A Closer Look at Learned Optimization: Stability, Robustness, and Inductive Biases |
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❌ |
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4 |
| A Closer Look at Offline RL Agents |
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3 |
| A Closer Look at Prototype Classifier for Few-shot Image Classification |
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3 |
| A Closer Look at Weakly-Supervised Audio-Visual Source Localization |
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❌ |
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❌ |
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3 |
| A Closer Look at the Adversarial Robustness of Deep Equilibrium Models |
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✅ |
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3 |
| A Combinatorial Perspective on the Optimization of Shallow ReLU Networks |
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4 |
| A Communication-Efficient Distributed Gradient Clipping Algorithm for Training Deep Neural Networks |
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6 |
| A Communication-efficient Algorithm with Linear Convergence for Federated Minimax Learning |
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3 |
| A Conditional Randomization Test for Sparse Logistic Regression in High-Dimension |
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❌ |
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❌ |
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4 |
| A Consistent and Differentiable Lp Canonical Calibration Error Estimator |
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❌ |
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4 |
| A Consolidated Cross-Validation Algorithm for Support Vector Machines via Data Reduction |
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❌ |
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6 |
| A Continuous Time Framework for Discrete Denoising Models |
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5 |
| A Contrastive Framework for Neural Text Generation |
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5 |
| A Coupled Design of Exploiting Record Similarity for Practical Vertical Federated Learning |
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❌ |
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6 |
| A Damped Newton Method Achieves Global $\mathcal O \left(\frac{1}{k^2}\right)$ and Local Quadratic Convergence Rate |
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4 |
| A Data-Augmentation Is Worth A Thousand Samples: Analytical Moments And Sampling-Free Training |
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❌ |
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2 |
| A Deep Learning Dataloader with Shared Data Preparation |
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4 |
| A Deep Reinforcement Learning Framework for Column Generation |
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❌ |
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6 |
| A Differentiable Semantic Metric Approximation in Probabilistic Embedding for Cross-Modal Retrieval |
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✅ |
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❌ |
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5 |
| A Differentially Private Linear-Time fPTAS for the Minimum Enclosing Ball Problem |
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✅ |
❌ |
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❌ |
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3 |
| A Direct Approximation of AIXI Using Logical State Abstractions |
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✅ |
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3 |
| A Fast Post-Training Pruning Framework for Transformers |
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6 |
| A Fast Scale-Invariant Algorithm for Non-negative Least Squares with Non-negative Data |
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5 |
| A Few Expert Queries Suffices for Sample-Efficient RL with Resets and Linear Value Approximation |
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1 |
| A Fourier Approach to Mixture Learning |
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1 |
| A General Framework for Auditing Differentially Private Machine Learning |
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3 |
| A Geometric Perspective on Variational Autoencoders |
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5 |
| A Kernelised Stein Statistic for Assessing Implicit Generative Models |
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4 |
| A Lagrangian Duality Approach to Active Learning |
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3 |
| A Lower Bound of Hash Codes' Performance |
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6 |
| A Mean-Field Game Approach to Cloud Resource Management with Function Approximation |
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3 |
| A Mixture Of Surprises for Unsupervised Reinforcement Learning |
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4 |
| A Multi-Resolution Framework for U-Nets with Applications to Hierarchical VAEs |
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6 |
| A Multilabel Classification Framework for Approximate Nearest Neighbor Search |
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3 |
| A Near-Optimal Best-of-Both-Worlds Algorithm for Online Learning with Feedback Graphs |
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1 |
| A Near-Optimal Primal-Dual Method for Off-Policy Learning in CMDP |
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1 |
| A Neural Corpus Indexer for Document Retrieval |
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6 |
| A Neural Pre-Conditioning Active Learning Algorithm to Reduce Label Complexity |
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4 |
| A New Family of Generalization Bounds Using Samplewise Evaluated CMI |
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5 |
| A Non-Asymptotic Moreau Envelope Theory for High-Dimensional Generalized Linear Models |
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1 |
| A Non-asymptotic Analysis of Non-parametric Temporal-Difference Learning |
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3 |
| A PAC-Bayesian Generalization Bound for Equivariant Networks |
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2 |
| A Policy-Guided Imitation Approach for Offline Reinforcement Learning |
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6 |
| A Practical, Progressively-Expressive GNN |
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6 |
| A Probabilistic Graph Coupling View of Dimension Reduction |
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3 |
| A Projection-free Algorithm for Constrained Stochastic Multi-level Composition Optimization |
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2 |
| A Quadrature Rule combining Control Variates and Adaptive Importance Sampling |
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4 |
| A Quantitative Geometric Approach to Neural-Network Smoothness |
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5 |
| A Reduction to Binary Approach for Debiasing Multiclass Datasets |
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6 |
| A Regret-Variance Trade-Off in Online Learning |
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1 |
| A Reparametrization-Invariant Sharpness Measure Based on Information Geometry |
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2 |
| A Robust Phased Elimination Algorithm for Corruption-Tolerant Gaussian Process Bandits |
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2 |
| A Rotated Hyperbolic Wrapped Normal Distribution for Hierarchical Representation Learning |
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4 |
| A Scalable Deterministic Global Optimization Algorithm for Training Optimal Decision Tree |
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7 |
| A Simple Approach to Automated Spectral Clustering |
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4 |
| A Simple Decentralized Cross-Entropy Method |
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5 |
| A Simple and Optimal Policy Design for Online Learning with Safety against Heavy-tailed Risk |
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3 |
| A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits |
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2 |
| A Single-timescale Analysis for Stochastic Approximation with Multiple Coupled Sequences |
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0 |
| A Solver-free Framework for Scalable Learning in Neural ILP Architectures |
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2 |
| A Spectral Approach to Item Response Theory |
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3 |
| A Statistical Online Inference Approach in Averaged Stochastic Approximation |
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3 |
| A Stochastic Linearized Augmented Lagrangian Method for Decentralized Bilevel Optimization |
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2 |
| A Theoretical Framework for Inference Learning |
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4 |
| A Theoretical Study on Solving Continual Learning |
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5 |
| A Theoretical Understanding of Gradient Bias in Meta-Reinforcement Learning |
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4 |
| A Theoretical View on Sparsely Activated Networks |
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3 |
| A Theory of PAC Learnability under Transformation Invariances |
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0 |
| A Transformer-Based Object Detector with Coarse-Fine Crossing Representations |
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5 |
| A Unified Analysis of Federated Learning with Arbitrary Client Participation |
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4 |
| A Unified Analysis of Mixed Sample Data Augmentation: A Loss Function Perspective |
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3 |
| A Unified Convergence Theorem for Stochastic Optimization Methods |
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0 |
| A Unified Diversity Measure for Multiagent Reinforcement Learning |
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2 |
| A Unified Framework for Alternating Offline Model Training and Policy Learning |
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5 |
| A Unified Framework for Deep Symbolic Regression |
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5 |
| A Unified Hard-Constraint Framework for Solving Geometrically Complex PDEs |
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4 |
| A Unified Model for Multi-class Anomaly Detection |
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4 |
| A Unified Sequence Interface for Vision Tasks |
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6 |
| A Unifying Framework for Online Optimization with Long-Term Constraints |
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1 |
| A Unifying Framework of Off-Policy General Value Function Evaluation |
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4 |
| A Universal Error Measure for Input Predictions Applied to Online Graph Problems |
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3 |
| A Variant of Anderson Mixing with Minimal Memory Size |
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7 |
| A Variational Edge Partition Model for Supervised Graph Representation Learning |
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3 |
| A Win-win Deal: Towards Sparse and Robust Pre-trained Language Models |
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3 |
| A composable machine-learning approach for steady-state simulations on high-resolution grids |
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3 |
| A consistently adaptive trust-region method |
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5 |
| A contrastive rule for meta-learning |
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3 |
| A framework for bilevel optimization that enables stochastic and global variance reduction algorithms |
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4 |
| A general approximation lower bound in $L^p$ norm, with applications to feed-forward neural networks |
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0 |
| A gradient estimator via L1-randomization for online zero-order optimization with two point feedback |
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✅ |
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2 |
| A gradient sampling method with complexity guarantees for Lipschitz functions in high and low dimensions |
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1 |
| A permutation-free kernel two-sample test |
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4 |
| A sharp NMF result with applications in network modeling |
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❌ |
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2 |
| A simple but strong baseline for online continual learning: Repeated Augmented Rehearsal |
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4 |
| A theory of weight distribution-constrained learning |
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3 |
| A time-resolved theory of information encoding in recurrent neural networks |
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❌ |
✅ |
2 |
| A2: Efficient Automated Attacker for Boosting Adversarial Training |
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5 |
| ACIL: Analytic Class-Incremental Learning with Absolute Memorization and Privacy Protection |
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4 |
| AD-DROP: Attribution-Driven Dropout for Robust Language Model Fine-Tuning |
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❌ |
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6 |
| ALIFE: Adaptive Logit Regularizer and Feature Replay for Incremental Semantic Segmentation |
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✅ |
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6 |
| ALMA: Hierarchical Learning for Composite Multi-Agent Tasks |
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3 |
| AMP: Automatically Finding Model Parallel Strategies with Heterogeneity Awareness |
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4 |
| APG: Adaptive Parameter Generation Network for Click-Through Rate Prediction |
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❌ |
✅ |
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4 |
| ASPiRe: Adaptive Skill Priors for Reinforcement Learning |
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❌ |
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6 |
| ATD: Augmenting CP Tensor Decomposition by Self Supervision |
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7 |
| AUTOMATA: Gradient Based Data Subset Selection for Compute-Efficient Hyper-parameter Tuning |
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✅ |
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❌ |
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5 |
| AVLEN: Audio-Visual-Language Embodied Navigation in 3D Environments |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| AZ-whiteness test: a test for signal uncorrelation on spatio-temporal graphs |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Accelerated Linearized Laplace Approximation for Bayesian Deep Learning |
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✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Accelerated Primal-Dual Gradient Method for Smooth and Convex-Concave Saddle-Point Problems with Bilinear Coupling |
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❌ |
❌ |
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1 |
| Accelerated Projected Gradient Algorithms for Sparsity Constrained Optimization Problems |
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3 |
| Accelerated Training of Physics-Informed Neural Networks (PINNs) using Meshless Discretizations |
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3 |
| Accelerating Certified Robustness Training via Knowledge Transfer |
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5 |
| Accelerating SGD for Highly Ill-Conditioned Huge-Scale Online Matrix Completion |
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3 |
| Accelerating Sparse Convolution with Column Vector-Wise Sparsity |
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4 |
| Acceleration in Distributed Sparse Regression |
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5 |
| Action-modulated midbrain dopamine activity arises from distributed control policies |
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2 |
| Active Bayesian Causal Inference |
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3 |
| Active Exploration for Inverse Reinforcement Learning |
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2 |
| Active Labeling: Streaming Stochastic Gradients |
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3 |
| Active Learning Helps Pretrained Models Learn the Intended Task |
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3 |
| Active Learning Polynomial Threshold Functions |
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1 |
| Active Learning Through a Covering Lens |
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4 |
| Active Learning for Multiple Target Models |
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3 |
| Active Learning of Classifiers with Label and Seed Queries |
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1 |
| Active Learning with Neural Networks: Insights from Nonparametric Statistics |
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1 |
| Active Learning with Safety Constraints |
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4 |
| Active Ranking without Strong Stochastic Transitivity |
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4 |
| Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation |
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5 |
| AdaFocal: Calibration-aware Adaptive Focal Loss |
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5 |
| Adam Can Converge Without Any Modification On Update Rules |
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3 |
| AdaptFormer: Adapting Vision Transformers for Scalable Visual Recognition |
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5 |
| Adaptation Accelerating Sampling-based Bayesian Inference in Attractor Neural Networks |
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2 |
| Adapting Self-Supervised Vision Transformers by Probing Attention-Conditioned Masking Consistency |
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5 |
| Adapting to Online Label Shift with Provable Guarantees |
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3 |
| Adaptive Data Debiasing through Bounded Exploration |
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4 |
| Adaptive Distribution Calibration for Few-Shot Learning with Hierarchical Optimal Transport |
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5 |
| Adaptive Interest for Emphatic Reinforcement Learning |
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3 |
| Adaptive Multi-stage Density Ratio Estimation for Learning Latent Space Energy-based Model |
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2 |
| Adaptive Oracle-Efficient Online Learning |
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1 |
| Adaptive Sampling for Discovery |
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4 |
| Adaptive Stochastic Variance Reduction for Non-convex Finite-Sum Minimization |
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3 |
| Adaptively Exploiting d-Separators with Causal Bandits |
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3 |
| Additive MIL: Intrinsically Interpretable Multiple Instance Learning for Pathology |
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5 |
| Addressing Leakage in Concept Bottleneck Models |
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6 |
| Adjoint-aided inference of Gaussian process driven differential equations |
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4 |
| Adv-Attribute: Inconspicuous and Transferable Adversarial Attack on Face Recognition |
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4 |
| Advancing Model Pruning via Bi-level Optimization |
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7 |
| Adversarial Attack on Attackers: Post-Process to Mitigate Black-Box Score-Based Query Attacks |
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6 |
| Adversarial Auto-Augment with Label Preservation: A Representation Learning Principle Guided Approach |
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5 |
| Adversarial Reprogramming Revisited |
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5 |
| Adversarial Robustness is at Odds with Lazy Training |
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5 |
| Adversarial Style Augmentation for Domain Generalized Urban-Scene Segmentation |
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5 |
| Adversarial Task Up-sampling for Meta-learning |
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5 |
| Adversarial Training with Complementary Labels: On the Benefit of Gradually Informative Attacks |
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6 |
| Adversarial Unlearning: Reducing Confidence Along Adversarial Directions |
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5 |
| Adversarial training for high-stakes reliability |
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5 |
| Adversarially Robust Learning: A Generic Minimax Optimal Learner and Characterization |
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1 |
| AgraSSt: Approximate Graph Stein Statistics for Interpretable Assessment of Implicit Graph Generators |
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4 |
| Agreement-on-the-line: Predicting the Performance of Neural Networks under Distribution Shift |
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3 |
| Algorithms and Hardness for Learning Linear Thresholds from Label Proportions |
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3 |
| Algorithms that Approximate Data Removal: New Results and Limitations |
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5 |
| Algorithms with Prediction Portfolios |
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2 |
| Align then Fusion: Generalized Large-scale Multi-view Clustering with Anchor Matching Correspondences |
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5 |
| Aligning individual brains with fused unbalanced Gromov Wasserstein |
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6 |
| Alignment-guided Temporal Attention for Video Action Recognition |
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4 |
| All Politics is Local: Redistricting via Local Fairness |
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3 |
| Alleviating "Posterior Collapse'' in Deep Topic Models via Policy Gradient |
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4 |
| Alleviating Adversarial Attacks on Variational Autoencoders with MCMC |
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5 |
| Alleviating the Sample Selection Bias in Few-shot Learning by Removing Projection to the Centroid |
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4 |
| Alternating Mirror Descent for Constrained Min-Max Games |
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0 |
| Amortized Inference for Causal Structure Learning |
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5 |
| Amortized Inference for Heterogeneous Reconstruction in Cryo-EM |
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3 |
| Amortized Mixing Coupling Processes for Clustering |
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4 |
| Amortized Projection Optimization for Sliced Wasserstein Generative Models |
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4 |
| Amortized Proximal Optimization |
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6 |
| Amplifying Membership Exposure via Data Poisoning |
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6 |
| An $\alpha$-No-Regret Algorithm For Graphical Bilinear Bandits |
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2 |
| An $\alpha$-regret analysis of Adversarial Bilateral Trade |
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1 |
| An Adaptive Deep RL Method for Non-Stationary Environments with Piecewise Stable Context |
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3 |
| An Adaptive Kernel Approach to Federated Learning of Heterogeneous Causal Effects |
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4 |
| An Algorithm for Learning Switched Linear Dynamics from Data |
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4 |
| An Analysis of Ensemble Sampling |
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1 |
| An Analytical Theory of Curriculum Learning in Teacher-Student Networks |
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2 |
| An Asymptotically Optimal Batched Algorithm for the Dueling Bandit Problem |
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4 |
| An Embarrassingly Simple Approach to Semi-Supervised Few-Shot Learning |
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6 |
| An Empirical Study on Disentanglement of Negative-free Contrastive Learning |
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2 |
| An In-depth Study of Stochastic Backpropagation |
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6 |
| An Information-Theoretic Framework for Deep Learning |
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0 |
| An Investigation into Whitening Loss for Self-supervised Learning |
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5 |
| An efficient graph generative model for navigating ultra-large combinatorial synthesis libraries |
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4 |
| An empirical analysis of compute-optimal large language model training |
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3 |
| Analyzing Data-Centric Properties for Graph Contrastive Learning |
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1 |
| Analyzing Lottery Ticket Hypothesis from PAC-Bayesian Theory Perspective |
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3 |
| Analyzing Sharpness along GD Trajectory: Progressive Sharpening and Edge of Stability |
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1 |
| Anchor-Changing Regularized Natural Policy Gradient for Multi-Objective Reinforcement Learning |
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5 |
| AniFaceGAN: Animatable 3D-Aware Face Image Generation for Video Avatars |
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4 |
| AnimeSR: Learning Real-World Super-Resolution Models for Animation Videos |
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4 |
| Annihilation of Spurious Minima in Two-Layer ReLU Networks |
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0 |
| Anonymized Histograms in Intermediate Privacy Models |
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1 |
| Anonymous Bandits for Multi-User Systems |
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2 |
| Anticipating Performativity by Predicting from Predictions |
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2 |
| Antigen-Specific Antibody Design and Optimization with Diffusion-Based Generative Models for Protein Structures |
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3 |
| Anytime-Valid Inference For Multinomial Count Data |
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0 |
| Approaching Quartic Convergence Rates for Quasi-Stochastic Approximation with Application to Gradient-Free Optimization |
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2 |
| Approximate Euclidean lengths and distances beyond Johnson-Lindenstrauss |
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2 |
| Approximate Secular Equations for the Cubic Regularization Subproblem |
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4 |
| Approximate Value Equivalence |
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2 |
| Approximation with CNNs in Sobolev Space: with Applications to Classification |
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0 |
| Archimedes Meets Privacy: On Privately Estimating Quantiles in High Dimensions Under Minimal Assumptions |
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| Are All Losses Created Equal: A Neural Collapse Perspective |
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4 |
| Are AlphaZero-like Agents Robust to Adversarial Perturbations? |
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4 |
| Are Defenses for Graph Neural Networks Robust? |
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5 |
| Are GANs overkill for NLP? |
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1 |
| Are Two Heads the Same as One? Identifying Disparate Treatment in Fair Neural Networks |
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3 |
| Are You Stealing My Model? Sample Correlation for Fingerprinting Deep Neural Networks |
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4 |
| Are all Frames Equal? Active Sparse Labeling for Video Action Detection |
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4 |
| Ask4Help: Learning to Leverage an Expert for Embodied Tasks |
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3 |
| Assaying Out-Of-Distribution Generalization in Transfer Learning |
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5 |
| Assistive Teaching of Motor Control Tasks to Humans |
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5 |
| Associating Objects and Their Effects in Video through Coordination Games |
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3 |
| Association Graph Learning for Multi-Task Classification with Category Shifts |
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3 |
| Asymmetric Temperature Scaling Makes Larger Networks Teach Well Again |
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6 |
| Asymptotic Behaviors of Projected Stochastic Approximation: A Jump Diffusion Perspective |
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3 |
| Asymptotic Properties for Bayesian Neural Network in Besov Space |
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2 |
| Asymptotically Unbiased Instance-wise Regularized Partial AUC Optimization: Theory and Algorithm |
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3 |
| Asymptotics of $\ell_2$ Regularized Network Embeddings |
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7 |
| Asymptotics of smoothed Wasserstein distances in the small noise regime |
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0 |
| Asynchronous Actor-Critic for Multi-Agent Reinforcement Learning |
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5 |
| Asynchronous SGD Beats Minibatch SGD Under Arbitrary Delays |
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1 |
| AttCAT: Explaining Transformers via Attentive Class Activation Tokens |
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2 |
| Attention-based Neural Cellular Automata |
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5 |
| Attracting and Dispersing: A Simple Approach for Source-free Domain Adaptation |
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4 |
| Audio-Driven Co-Speech Gesture Video Generation |
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4 |
| Augmentations in Hypergraph Contrastive Learning: Fabricated and Generative |
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3 |
| Augmented RBMLE-UCB Approach for Adaptive Control of Linear Quadratic Systems |
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3 |
| Augmenting Online Algorithms with $\varepsilon$-Accurate Predictions |
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1 |
| AutoLink: Self-supervised Learning of Human Skeletons and Object Outlines by Linking Keypoints |
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5 |
| AutoML Two-Sample Test |
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| AutoMS: Automatic Model Selection for Novelty Detection with Error Rate Control |
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| AutoMTL: A Programming Framework for Automating Efficient Multi-Task Learning |
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| AutoST: Towards the Universal Modeling of Spatio-temporal Sequences |
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5 |
| Autoformalization with Large Language Models |
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4 |
| Autoinverse: Uncertainty Aware Inversion of Neural Networks |
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3 |
| Automatic Differentiation of Programs with Discrete Randomness |
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| Automatic differentiation of nonsmooth iterative algorithms |
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4 |
| Autoregressive Perturbations for Data Poisoning |
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| Autoregressive Search Engines: Generating Substrings as Document Identifiers |
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4 |
| Average Sensitivity of Euclidean k-Clustering |
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1 |
| BEER: Fast $O(1/T)$ Rate for Decentralized Nonconvex Optimization with Communication Compression |
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| BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework |
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| BILCO: An Efficient Algorithm for Joint Alignment of Time Series |
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5 |
| BMU-MoCo: Bidirectional Momentum Update for Continual Video-Language Modeling |
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| BOME! Bilevel Optimization Made Easy: A Simple First-Order Approach |
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5 |
| BR-SNIS: Bias Reduced Self-Normalized Importance Sampling |
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3 |
| BYOL-Explore: Exploration by Bootstrapped Prediction |
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3 |
| Back Razor: Memory-Efficient Transfer Learning by Self-Sparsified Backpropagation |
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6 |
| BadPrompt: Backdoor Attacks on Continuous Prompts |
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5 |
| BagFlip: A Certified Defense Against Data Poisoning |
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3 |
| Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization |
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2 |
| Batch Bayesian Optimization on Permutations using the Acquisition Weighted Kernel |
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4 |
| Batch Bayesian optimisation via density-ratio estimation with guarantees |
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2 |
| Batch Multi-Fidelity Active Learning with Budget Constraints |
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2 |
| Batch size-invariance for policy optimization |
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4 |
| Batch-Size Independent Regret Bounds for Combinatorial Semi-Bandits with Probabilistically Triggered Arms or Independent Arms |
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3 |
| BayesPCN: A Continually Learnable Predictive Coding Associative Memory |
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| Bayesian Active Learning with Fully Bayesian Gaussian Processes |
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5 |
| Bayesian Clustering of Neural Spiking Activity Using a Mixture of Dynamic Poisson Factor Analyzers |
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| Bayesian Optimistic Optimization: Optimistic Exploration for Model-based Reinforcement Learning |
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5 |
| Bayesian Optimization over Discrete and Mixed Spaces via Probabilistic Reparameterization |
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5 |
| Bayesian Persuasion for Algorithmic Recourse |
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3 |
| Bayesian Risk Markov Decision Processes |
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4 |
| Bayesian Spline Learning for Equation Discovery of Nonlinear Dynamics with Quantified Uncertainty |
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| Bayesian inference via sparse Hamiltonian flows |
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5 |
| Behavior Transformers: Cloning $k$ modes with one stone |
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3 |
| Bellman Residual Orthogonalization for Offline Reinforcement Learning |
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| Benchopt: Reproducible, efficient and collaborative optimization benchmarks |
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6 |
| Benefits of Additive Noise in Composing Classes with Bounded Capacity |
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4 |
| Benefits of Permutation-Equivariance in Auction Mechanisms |
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2 |
| Benign Overfitting in Two-layer Convolutional Neural Networks |
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| Benign Underfitting of Stochastic Gradient Descent |
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| Benign, Tempered, or Catastrophic: Toward a Refined Taxonomy of Overfitting |
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| Bessel Equivariant Networks for Inversion of Transmission Effects in Multi-Mode Optical Fibres |
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5 |
| Best of Both Worlds Model Selection |
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| Better Best of Both Worlds Bounds for Bandits with Switching Costs |
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1 |
| Better SGD using Second-order Momentum |
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6 |
| Better Uncertainty Calibration via Proper Scores for Classification and Beyond |
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5 |
| Between Stochastic and Adversarial Online Convex Optimization: Improved Regret Bounds via Smoothness |
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| Beyond Adult and COMPAS: Fair Multi-Class Prediction via Information Projection |
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6 |
| Beyond IID: data-driven decision-making in heterogeneous environments |
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| Beyond L1: Faster and Better Sparse Models with skglm |
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4 |
| Beyond Mahalanobis Distance for Textual OOD Detection |
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4 |
| Beyond Not-Forgetting: Continual Learning with Backward Knowledge Transfer |
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4 |
| Beyond Rewards: a Hierarchical Perspective on Offline Multiagent Behavioral Analysis |
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4 |
| Beyond Separability: Analyzing the Linear Transferability of Contrastive Representations to Related Subpopulations |
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| Beyond Time-Average Convergence: Near-Optimal Uncoupled Online Learning via Clairvoyant Multiplicative Weights Update |
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| Beyond accuracy: generalization properties of bio-plausible temporal credit assignment rules |
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| Beyond black box densities: Parameter learning for the deviated components |
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| Beyond neural scaling laws: beating power law scaling via data pruning |
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| Beyond spectral gap: the role of the topology in decentralized learning |
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| Beyond the Best: Distribution Functional Estimation in Infinite-Armed Bandits |
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| Beyond the Return: Off-policy Function Estimation under User-specified Error-measuring Distributions |
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3 |
| Bezier Gaussian Processes for Tall and Wide Data |
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| Bi-directional Weakly Supervised Knowledge Distillation for Whole Slide Image Classification |
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| BiMLP: Compact Binary Architectures for Vision Multi-Layer Perceptrons |
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| BiT: Robustly Binarized Multi-distilled Transformer |
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| Bidirectional Learning for Offline Infinite-width Model-based Optimization |
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| BinauralGrad: A Two-Stage Conditional Diffusion Probabilistic Model for Binaural Audio Synthesis |
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| Biological Learning of Irreducible Representations of Commuting Transformations |
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5 |
| Biologically Inspired Dynamic Thresholds for Spiking Neural Networks |
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| Biologically plausible solutions for spiking networks with efficient coding |
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| Biologically-Plausible Determinant Maximization Neural Networks for Blind Separation of Correlated Sources |
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| Biologically-plausible backpropagation through arbitrary timespans via local neuromodulators |
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| Bivariate Causal Discovery for Categorical Data via Classification with Optimal Label Permutation |
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| Black-Box Generalization: Stability of Zeroth-Order Learning |
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| Black-box coreset variational inference |
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4 |
| Blackbox Attacks via Surrogate Ensemble Search |
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5 |
| Blessing of Depth in Linear Regression: Deeper Models Have Flatter Landscape Around the True Solution |
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3 |
| Block-Recurrent Transformers |
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| Boosting Barely Robust Learners: A New Perspective on Adversarial Robustness |
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4 |
| Boosting Out-of-distribution Detection with Typical Features |
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| Boosting the Performance of Generic Deep Neural Network Frameworks with Log-supermodular CRFs |
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| Boosting the Transferability of Adversarial Attacks with Reverse Adversarial Perturbation |
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| Bootstrapped Transformer for Offline Reinforcement Learning |
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| Bounded-Regret MPC via Perturbation Analysis: Prediction Error, Constraints, and Nonlinearity |
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| Bounding and Approximating Intersectional Fairness through Marginal Fairness |
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| Brain Network Transformer |
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| Branch & Learn for Recursively and Iteratively Solvable Problems in Predict+Optimize |
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| Bridge the Gap Between Architecture Spaces via A Cross-Domain Predictor |
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| Bridging Central and Local Differential Privacy in Data Acquisition Mechanisms |
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| Bridging the Gap Between Vision Transformers and Convolutional Neural Networks on Small Datasets |
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| Bridging the Gap between Object and Image-level Representations for Open-Vocabulary Detection |
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| Bridging the Gap from Asymmetry Tricks to Decorrelation Principles in Non-contrastive Self-supervised Learning |
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| Bridging the Gap: Unifying the Training and Evaluation of Neural Network Binary Classifiers |
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| Bring Your Own Algorithm for Optimal Differentially Private Stochastic Minimax Optimization |
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| Bringing Image Scene Structure to Video via Frame-Clip Consistency of Object Tokens |
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| Brownian Noise Reduction: Maximizing Privacy Subject to Accuracy Constraints |
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| Byzantine Spectral Ranking |
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| Byzantine-tolerant federated Gaussian process regression for streaming data |
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| C-Mixup: Improving Generalization in Regression |
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| C2FAR: Coarse-to-Fine Autoregressive Networks for Precise Probabilistic Forecasting |
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| CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point Clouds |
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| CARD: Classification and Regression Diffusion Models |
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| CASA: Category-agnostic Skeletal Animal Reconstruction |
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| CATER: Intellectual Property Protection on Text Generation APIs via Conditional Watermarks |
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| CCCP is Frank-Wolfe in disguise |
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| CEBaB: Estimating the Causal Effects of Real-World Concepts on NLP Model Behavior |
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| CEIP: Combining Explicit and Implicit Priors for Reinforcement Learning with Demonstrations |
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| CHIMLE: Conditional Hierarchical IMLE for Multimodal Conditional Image Synthesis |
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| CLEAR: Generative Counterfactual Explanations on Graphs |
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| CLIPDraw: Exploring Text-to-Drawing Synthesis through Language-Image Encoders |
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| CLOOB: Modern Hopfield Networks with InfoLOOB Outperform CLIP |
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| COLD Decoding: Energy-based Constrained Text Generation with Langevin Dynamics |
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| CS-Shapley: Class-wise Shapley Values for Data Valuation in Classification |
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| CUP: Critic-Guided Policy Reuse |
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| Cache-Augmented Inbatch Importance Resampling for Training Recommender Retriever |
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| CageNeRF: Cage-based Neural Radiance Field for Generalized 3D Deformation and Animation |
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| CalFAT: Calibrated Federated Adversarial Training with Label Skewness |
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| Calibrated Data-Dependent Constraints with Exact Satisfaction Guarantees |
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| Can Adversarial Training Be Manipulated By Non-Robust Features? |
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| Can Hybrid Geometric Scattering Networks Help Solve the Maximum Clique Problem? |
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| Can Push-forward Generative Models Fit Multimodal Distributions? |
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| Capturing Failures of Large Language Models via Human Cognitive Biases |
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✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Capturing Graphs with Hypo-Elliptic Diffusions |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| CascadeXML: Rethinking Transformers for End-to-end Multi-resolution Training in Extreme Multi-label Classification |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Category-Level 6D Object Pose Estimation in the Wild: A Semi-Supervised Learning Approach and A New Dataset |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Causal Discovery in Linear Latent Variable Models Subject to Measurement Error |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Causal Identification under Markov equivalence: Calculus, Algorithm, and Completeness |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Causal Inference with Non-IID Data using Linear Graphical Models |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Causality Preserving Chaotic Transformation and Classification using Neurochaos Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Causality-driven Hierarchical Structure Discovery for Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Causally motivated multi-shortcut identification and removal |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Censored Quantile Regression Neural Networks for Distribution-Free Survival Analysis |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Certifying Robust Graph Classification under Orthogonal Gromov-Wasserstein Threats |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Certifying Some Distributional Fairness with Subpopulation Decomposition |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Chain of Thought Imitation with Procedure Cloning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Chain-of-Thought Prompting Elicits Reasoning in Large Language Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Challenging Common Assumptions in Convex Reinforcement Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Change-point Detection for Sparse and Dense Functional Data in General Dimensions |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Chaotic Dynamics are Intrinsic to Neural Network Training with SGD |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Chaotic Regularization and Heavy-Tailed Limits for Deterministic Gradient Descent |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Characterization of Excess Risk for Locally Strongly Convex Population Risk |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Characterizing Datapoints via Second-Split Forgetting |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Characterizing the Ventral Visual Stream with Response-Optimized Neural Encoding Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Chefs' Random Tables: Non-Trigonometric Random Features |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Chroma-VAE: Mitigating Shortcut Learning with Generative Classifiers |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Chromatic Correlation Clustering, Revisited |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Class-Aware Adversarial Transformers for Medical Image Segmentation |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Class-Dependent Label-Noise Learning with Cycle-Consistency Regularization |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| ClimbQ: Class Imbalanced Quantization Enabling Robustness on Efficient Inferences |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Clipped Stochastic Methods for Variational Inequalities with Heavy-Tailed Noise |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Cluster Randomized Designs for One-Sided Bipartite Experiments |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Cluster and Aggregate: Face Recognition with Large Probe Set |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Co-Modality Graph Contrastive Learning for Imbalanced Node Classification |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| CoNSoLe: Convex Neural Symbolic Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| CoNT: Contrastive Neural Text Generation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| CoPur: Certifiably Robust Collaborative Inference via Feature Purification |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Coarse-to-Fine Vision-Language Pre-training with Fusion in the Backbone |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| CodeRL: Mastering Code Generation through Pretrained Models and Deep Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Coded Residual Transform for Generalizable Deep Metric Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| CogView2: Faster and Better Text-to-Image Generation via Hierarchical Transformers |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Collaborative Decision Making Using Action Suggestions |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Collaborative Learning by Detecting Collaboration Partners |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Collaborative Learning of Discrete Distributions under Heterogeneity and Communication Constraints |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Collaborative Linear Bandits with Adversarial Agents: Near-Optimal Regret Bounds |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| ComENet: Towards Complete and Efficient Message Passing for 3D Molecular Graphs |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
5 |
| ComGAN: Unsupervised Disentanglement and Segmentation via Image Composition |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Combinatorial Bandits with Linear Constraints: Beyond Knapsacks and Fairness |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Combining Explicit and Implicit Regularization for Efficient Learning in Deep Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Communication Acceleration of Local Gradient Methods via an Accelerated Primal-Dual Algorithm with an Inexact Prox |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Communication Efficient Distributed Learning for Kernelized Contextual Bandits |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Communication Efficient Federated Learning for Generalized Linear Bandits |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Communication-Efficient Topologies for Decentralized Learning with $O(1)$ Consensus Rate |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| Communication-efficient distributed eigenspace estimation with arbitrary node failures |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Composite Feature Selection Using Deep Ensembles |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Composition Theorems for Interactive Differential Privacy |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Compositional Generalization in Unsupervised Compositional Representation Learning: A Study on Disentanglement and Emergent Language |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Compositional generalization through abstract representations in human and artificial neural networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Compressible-composable NeRF via Rank-residual Decomposition |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Computationally Efficient Horizon-Free Reinforcement Learning for Linear Mixture MDPs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Concentration of Data Encoding in Parameterized Quantum Circuits |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Concept Activation Regions: A Generalized Framework For Concept-Based Explanations |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Concept Embedding Models: Beyond the Accuracy-Explainability Trade-Off |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Concrete Score Matching: Generalized Score Matching for Discrete Data |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Conditional Diffusion Process for Inverse Halftoning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Conditional Independence Testing with Heteroskedastic Data and Applications to Causal Discovery |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Conditional Meta-Learning of Linear Representations |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Confidence-based Reliable Learning under Dual Noises |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Confident Adaptive Language Modeling |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Confident Approximate Policy Iteration for Efficient Local Planning in $q^\pi$-realizable MDPs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Conformal Frequency Estimation with Sketched Data |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Conformal Off-Policy Prediction in Contextual Bandits |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Conformal Prediction with Temporal Quantile Adjustments |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Conformalized Fairness via Quantile Regression |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| ConfounderGAN: Protecting Image Data Privacy with Causal Confounder |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Conservative Dual Policy Optimization for Efficient Model-Based Reinforcement Learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Consistency of Constrained Spectral Clustering under Graph Induced Fair Planted Partitions |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Consistent Interpolating Ensembles via the Manifold-Hilbert Kernel |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Consistent Sufficient Explanations and Minimal Local Rules for explaining the decision of any classifier or regressor |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Constants of motion network |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Constrained GPI for Zero-Shot Transfer in Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Constrained Langevin Algorithms with L-mixing External Random Variables |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Constrained Predictive Coding as a Biologically Plausible Model of the Cortical Hierarchy |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Constrained Stochastic Nonconvex Optimization with State-dependent Markov Data |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Constrained Update Projection Approach to Safe Policy Optimization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Constraining Gaussian Processes to Systems of Linear Ordinary Differential Equations |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
3 |
| Contact-aware Human Motion Forecasting |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Context-Based Dynamic Pricing with Partially Linear Demand Model |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Contextual Bandits with Knapsacks for a Conversion Model |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Contextual Dynamic Pricing with Unknown Noise: Explore-then-UCB Strategy and Improved Regrets |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Contextual Squeeze-and-Excitation for Efficient Few-Shot Image Classification |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Continual Learning In Environments With Polynomial Mixing Times |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Continual Learning with Evolving Class Ontologies |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Continual learning: a feature extraction formalization, an efficient algorithm, and fundamental obstructions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Continuous Deep Q-Learning in Optimal Control Problems: Normalized Advantage Functions Analysis |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Continuous MDP Homomorphisms and Homomorphic Policy Gradient |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Continuously Tempered PDMP samplers |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Contrastive Adapters for Foundation Model Group Robustness |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Contrastive Graph Structure Learning via Information Bottleneck for Recommendation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Contrastive Language-Image Pre-Training with Knowledge Graphs |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Contrastive Learning as Goal-Conditioned Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Contrastive Neural Ratio Estimation |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Contrastive and Non-Contrastive Self-Supervised Learning Recover Global and Local Spectral Embedding Methods |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Controllable 3D Face Synthesis with Conditional Generative Occupancy Fields |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Controllable Text Generation with Neurally-Decomposed Oracle |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Controlled Sparsity via Constrained Optimization or: How I Learned to Stop Tuning Penalties and Love Constraints |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Convergence beyond the over-parameterized regime using Rayleigh quotients |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Convergence for score-based generative modeling with polynomial complexity |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Convergent Representations of Computer Programs in Human and Artificial Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Convexity Certificates from Hessians |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Convolutional Neural Networks on Graphs with Chebyshev Approximation, Revisited |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Cooperative Distribution Alignment via JSD Upper Bound |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Coordinate Linear Variance Reduction for Generalized Linear Programming |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Coordinates Are NOT Lonely - Codebook Prior Helps Implicit Neural 3D representations |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Coreset for Line-Sets Clustering |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Coresets for Relational Data and The Applications |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
5 |
| Coresets for Vertical Federated Learning: Regularized Linear Regression and $K$-Means Clustering |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Coresets for Wasserstein Distributionally Robust Optimization Problems |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Cost-Sensitive Self-Training for Optimizing Non-Decomposable Metrics |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Cost-efficient Gaussian tensor network embeddings for tensor-structured inputs |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Could Giant Pre-trained Image Models Extract Universal Representations? |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Counterfactual Fairness with Partially Known Causal Graph |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Counterfactual Neural Temporal Point Process for Estimating Causal Influence of Misinformation on Social Media |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Counterfactual Temporal Point Processes |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Counterfactual harm |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| CoupAlign: Coupling Word-Pixel with Sentence-Mask Alignments for Referring Image Segmentation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| CroCo: Self-Supervised Pre-training for 3D Vision Tasks by Cross-View Completion |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Cross Aggregation Transformer for Image Restoration |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Cross-Image Context for Single Image Inpainting |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Cross-Linked Unified Embedding for cross-modality representation learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Cross-modal Learning for Image-Guided Point Cloud Shape Completion |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| CryptoGCN: Fast and Scalable Homomorphically Encrypted Graph Convolutional Network Inference |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Cryptographic Hardness of Learning Halfspaces with Massart Noise |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Curious Exploration via Structured World Models Yields Zero-Shot Object Manipulation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Curriculum Reinforcement Learning using Optimal Transport via Gradual Domain Adaptation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| CyCLIP: Cyclic Contrastive Language-Image Pretraining |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| DAGMA: Learning DAGs via M-matrices and a Log-Determinant Acyclicity Characterization |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
5 |
| DARE: Disentanglement-Augmented Rationale Extraction |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| DASCO: Dual-Generator Adversarial Support Constrained Offline Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| DENSE: Data-Free One-Shot Federated Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| DGD^2: A Linearly Convergent Distributed Algorithm For High-dimensional Statistical Recovery |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| DHRL: A Graph-Based Approach for Long-Horizon and Sparse Hierarchical Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| DIMES: A Differentiable Meta Solver for Combinatorial Optimization Problems |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| DISCO: Adversarial Defense with Local Implicit Functions |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| DMAP: a Distributed Morphological Attention Policy for learning to locomote with a changing body |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| DNA: Proximal Policy Optimization with a Dual Network Architecture |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| DOMINO: Decomposed Mutual Information Optimization for Generalized Context in Meta-Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| DOPE: Doubly Optimistic and Pessimistic Exploration for Safe Reinforcement Learning |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| DP-PCA: Statistically Optimal and Differentially Private PCA |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| DPM-Solver: A Fast ODE Solver for Diffusion Probabilistic Model Sampling in Around 10 Steps |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| DReS-FL: Dropout-Resilient Secure Federated Learning for Non-IID Clients via Secret Data Sharing |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| DTG-SSOD: Dense Teacher Guidance for Semi-Supervised Object Detection |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| D^2NeRF: Self-Supervised Decoupling of Dynamic and Static Objects from a Monocular Video |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| DaDA: Distortion-aware Domain Adaptation for Unsupervised Semantic Segmentation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Dance of SNN and ANN: Solving binding problem by combining spike timing and reconstructive attention |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Data Augmentation MCMC for Bayesian Inference from Privatized Data |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Data Augmentation for Compositional Data: Advancing Predictive Models of the Microbiome |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Data Distributional Properties Drive Emergent In-Context Learning in Transformers |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Data augmentation for efficient learning from parametric experts |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Data-Driven Conditional Robust Optimization |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Data-Driven Offline Decision-Making via Invariant Representation Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Data-Efficient Augmentation for Training Neural Networks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Data-Efficient Pipeline for Offline Reinforcement Learning with Limited Data |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Data-Efficient Structured Pruning via Submodular Optimization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Data-IQ: Characterizing subgroups with heterogeneous outcomes in tabular data |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| DataMUX: Data Multiplexing for Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Dataset Distillation using Neural Feature Regression |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Dataset Distillation via Factorization |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Dataset Inference for Self-Supervised Models |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| DeVRF: Fast Deformable Voxel Radiance Fields for Dynamic Scenes |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Debiased Causal Tree: Heterogeneous Treatment Effects Estimation with Unmeasured Confounding |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Debiased Machine Learning without Sample-Splitting for Stable Estimators |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Debiased Self-Training for Semi-Supervised Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Debiased, Longitudinal and Coordinated Drug Recommendation through Multi-Visit Clinic Records |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Debiasing Graph Neural Networks via Learning Disentangled Causal Substructure |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Debugging and Explaining Metric Learning Approaches: An Influence Function Based Perspective |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Decentralized Gossip-Based Stochastic Bilevel Optimization over Communication Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Decentralized Local Stochastic Extra-Gradient for Variational Inequalities |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Decentralized Training of Foundation Models in Heterogeneous Environments |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Decentralized, Communication- and Coordination-free Learning in Structured Matching Markets |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Deciding What to Model: Value-Equivalent Sampling for Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Decision Trees with Short Explainable Rules |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Decision-Focused Learning without Decision-Making: Learning Locally Optimized Decision Losses |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Decision-based Black-box Attack Against Vision Transformers via Patch-wise Adversarial Removal |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Decomposable Non-Smooth Convex Optimization with Nearly-Linear Gradient Oracle Complexity |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Decomposed Knowledge Distillation for Class-Incremental Semantic Segmentation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Decomposing NeRF for Editing via Feature Field Distillation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Deconfounded Representation Similarity for Comparison of Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Decoupled Context Processing for Context Augmented Language Modeling |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Decoupled Self-supervised Learning for Graphs |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Decoupling Classifier for Boosting Few-shot Object Detection and Instance Segmentation |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Decoupling Features in Hierarchical Propagation for Video Object Segmentation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Decoupling Knowledge from Memorization: Retrieval-augmented Prompt Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Deep Active Learning by Leveraging Training Dynamics |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Deep Architecture Connectivity Matters for Its Convergence: A Fine-Grained Analysis |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Deep Attentive Belief Propagation: Integrating Reasoning and Learning for Solving Constraint Optimization Problems |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Deep Bidirectional Language-Knowledge Graph Pretraining |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Deep Combinatorial Aggregation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Deep Compression of Pre-trained Transformer Models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Deep Counterfactual Estimation with Categorical Background Variables |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
3 |
| Deep Differentiable Logic Gate Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Deep Ensembles Work, But Are They Necessary? |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Deep Equilibrium Approaches to Diffusion Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Deep Fourier Up-Sampling |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| Deep Generalized Schrödinger Bridge |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Deep Generative Model for Periodic Graphs |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Deep Hierarchical Planning from Pixels |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Deep Learning Methods for Proximal Inference via Maximum Moment Restriction |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Deep Model Reassembly |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Deep Multi-Modal Structural Equations For Causal Effect Estimation With Unstructured Proxies |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Deep Surrogate Assisted Generation of Environments |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Deep invariant networks with differentiable augmentation layers |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| DeepFoids: Adaptive Bio-Inspired Fish Simulation with Deep Reinforcement Learning |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| DeepInteraction: 3D Object Detection via Modality Interaction |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| DeepMed: Semiparametric Causal Mediation Analysis with Debiased Deep Learning |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
4 |
| DeepTOP: Deep Threshold-Optimal Policy for MDPs and RMABs |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Defending Against Adversarial Attacks via Neural Dynamic System |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Defining and Characterizing Reward Gaming |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Degradation-Aware Unfolding Half-Shuffle Transformer for Spectral Compressive Imaging |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Deliberated Domain Bridging for Domain Adaptive Semantic Segmentation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Delving into Out-of-Distribution Detection with Vision-Language Representations |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Delving into Sequential Patches for Deepfake Detection |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Denoising Diffusion Restoration Models |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Dense Interspecies Face Embedding |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Density-driven Regularization for Out-of-distribution Detection |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Depth is More Powerful than Width with Prediction Concatenation in Deep Forest |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Descent Steps of a Relation-Aware Energy Produce Heterogeneous Graph Neural Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| DetCLIP: Dictionary-Enriched Visual-Concept Paralleled Pre-training for Open-world Detection |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Detecting Abrupt Changes in Sequential Pairwise Comparison Data |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Detection and Localization of Changes in Conditional Distributions |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Deterministic Langevin Monte Carlo with Normalizing Flows for Bayesian Inference |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| DevFly: Bio-Inspired Development of Binary Connections for Locality Preserving Sparse Codes |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| DiSC: Differential Spectral Clustering of Features |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Diagnosing failures of fairness transfer across distribution shift in real-world medical settings |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Diagonal State Spaces are as Effective as Structured State Spaces |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Dict-TTS: Learning to Pronounce with Prior Dictionary Knowledge for Text-to-Speech |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Differentiable Analog Quantum Computing for Optimization and Control |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Differentiable hierarchical and surrogate gradient search for spiking neural networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Differentially Private Covariance Revisited |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Differentially Private Generalized Linear Models Revisited |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Differentially Private Graph Learning via Sensitivity-Bounded Personalized PageRank |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Differentially Private Learning Needs Hidden State (Or Much Faster Convergence) |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Differentially Private Learning with Margin Guarantees |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Differentially Private Linear Sketches: Efficient Implementations and Applications |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Differentially Private Model Compression |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Differentially Private Online-to-batch for Smooth Losses |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Diffusion Curvature for Estimating Local Curvature in High Dimensional Data |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Diffusion Models as Plug-and-Play Priors |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Diffusion Visual Counterfactual Explanations |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Diffusion-LM Improves Controllable Text Generation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Diffusion-based Molecule Generation with Informative Prior Bridges |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| DigGAN: Discriminator gradIent Gap Regularization for GAN Training with Limited Data |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Direct Advantage Estimation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Discovered Policy Optimisation |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Discovering Design Concepts for CAD Sketches |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Discovering and Overcoming Limitations of Noise-engineered Data-free Knowledge Distillation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Discovery of Single Independent Latent Variable |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Discrete Compositional Representations as an Abstraction for Goal Conditioned Reinforcement Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Discrete-Convex-Analysis-Based Framework for Warm-Starting Algorithms with Predictions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Disentangling Causal Effects from Sets of Interventions in the Presence of Unobserved Confounders |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Disentangling Transfer in Continual Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Disentangling the Predictive Variance of Deep Ensembles through the Neural Tangent Kernel |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Distilled Gradient Aggregation: Purify Features for Input Attribution in the Deep Neural Network |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Distilling Representations from GAN Generator via Squeeze and Span |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Distinguishing Learning Rules with Brain Machine Interfaces |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Distinguishing discrete and continuous behavioral variability using warped autoregressive HMMs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Distributed Distributionally Robust Optimization with Non-Convex Objectives |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Distributed Influence-Augmented Local Simulators for Parallel MARL in Large Networked Systems |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Distributed Inverse Constrained Reinforcement Learning for Multi-agent Systems |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Distributed Learning of Conditional Quantiles in the Reproducing Kernel Hilbert Space |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Distributed Methods with Compressed Communication for Solving Variational Inequalities, with Theoretical Guarantees |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Distributed Online Convex Optimization with Compressed Communication |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Distributed Optimization for Overparameterized Problems: Achieving Optimal Dimension Independent Communication Complexity |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Distribution-Informed Neural Networks for Domain Adaptation Regression |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Distributional Convergence of the Sliced Wasserstein Process |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Distributional Reinforcement Learning for Risk-Sensitive Policies |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
4 |
| Distributional Reward Estimation for Effective Multi-agent Deep Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Distributionally Adaptive Meta Reinforcement Learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Distributionally Robust Optimization via Ball Oracle Acceleration |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Distributionally Robust Optimization with Data Geometry |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Distributionally robust weighted k-nearest neighbors |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| DivBO: Diversity-aware CASH for Ensemble Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Diverse Weight Averaging for Out-of-Distribution Generalization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Diversified Recommendations for Agents with Adaptive Preferences |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Diversity vs. Recognizability: Human-like generalization in one-shot generative models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Divert More Attention to Vision-Language Tracking |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Divide and Contrast: Source-free Domain Adaptation via Adaptive Contrastive Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Do Current Multi-Task Optimization Methods in Deep Learning Even Help? |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Do Residual Neural Networks discretize Neural Ordinary Differential Equations? |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Does GNN Pretraining Help Molecular Representation? |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Does Momentum Change the Implicit Regularization on Separable Data? |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Does Self-supervised Learning Really Improve Reinforcement Learning from Pixels? |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Domain Adaptation meets Individual Fairness. And they get along. |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Domain Adaptation under Open Set Label Shift |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Domain Generalization by Learning and Removing Domain-specific Features |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Domain Generalization without Excess Empirical Risk |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Don't Pour Cereal into Coffee: Differentiable Temporal Logic for Temporal Action Segmentation |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Don't Roll the Dice, Ask Twice: The Two-Query Distortion of Matching Problems and Beyond |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Double Bubble, Toil and Trouble: Enhancing Certified Robustness through Transitivity |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Double Check Your State Before Trusting It: Confidence-Aware Bidirectional Offline Model-Based Imagination |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Doubly Robust Counterfactual Classification |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Doubly-Asynchronous Value Iteration: Making Value Iteration Asynchronous in Actions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Draft-and-Revise: Effective Image Generation with Contextual RQ-Transformer |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Drawing out of Distribution with Neuro-Symbolic Generative Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| DreamShard: Generalizable Embedding Table Placement for Recommender Systems |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| DropCov: A Simple yet Effective Method for Improving Deep Architectures |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Dual-Curriculum Contrastive Multi-Instance Learning for Cancer Prognosis Analysis with Whole Slide Images |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Dual-discriminative Graph Neural Network for Imbalanced Graph-level Anomaly Detection |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| DualCoOp: Fast Adaptation to Multi-Label Recognition with Limited Annotations |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Dynamic Fair Division with Partial Information |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Dynamic Graph Neural Networks Under Spatio-Temporal Distribution Shift |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Dynamic Inverse Reinforcement Learning for Characterizing Animal Behavior |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Dynamic Learning in Large Matching Markets |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Dynamic Pricing with Monotonicity Constraint under Unknown Parametric Demand Model |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Dynamic Sparse Network for Time Series Classification: Learning What to “See” |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Dynamic Tensor Product Regression |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Dynamic pricing and assortment under a contextual MNL demand |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Dynamics of SGD with Stochastic Polyak Stepsizes: Truly Adaptive Variants and Convergence to Exact Solution |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| E-MAPP: Efficient Multi-Agent Reinforcement Learning with Parallel Program Guidance |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| EAGER: Asking and Answering Questions for Automatic Reward Shaping in Language-guided RL |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| EF-BV: A Unified Theory of Error Feedback and Variance Reduction Mechanisms for Biased and Unbiased Compression in Distributed Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| EGSDE: Unpaired Image-to-Image Translation via Energy-Guided Stochastic Differential Equations |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| ELASTIC: Numerical Reasoning with Adaptive Symbolic Compiler |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| ELIAS: End-to-End Learning to Index and Search in Large Output Spaces |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| ELIGN: Expectation Alignment as a Multi-Agent Intrinsic Reward |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| ESCADA: Efficient Safety and Context Aware Dose Allocation for Precision Medicine |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| EZNAS: Evolving Zero-Cost Proxies For Neural Architecture Scoring |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Early Stage Convergence and Global Convergence of Training Mildly Parameterized Neural Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Earthformer: Exploring Space-Time Transformers for Earth System Forecasting |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| EcoFormer: Energy-Saving Attention with Linear Complexity |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Effective Adaptation in Multi-Task Co-Training for Unified Autonomous Driving |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Effective Backdoor Defense by Exploiting Sensitivity of Poisoned Samples |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Effective Dimension in Bandit Problems under Censorship |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Effectiveness of Vision Transformer for Fast and Accurate Single-Stage Pedestrian Detection |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Effects of Data Geometry in Early Deep Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Efficiency Ordering of Stochastic Gradient Descent |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Efficient Active Learning with Abstention |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Efficient Adversarial Training without Attacking: Worst-Case-Aware Robust Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Efficient Aggregated Kernel Tests using Incomplete $U$-statistics |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Efficient Architecture Search for Diverse Tasks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Efficient Dataset Distillation using Random Feature Approximation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Efficient Frameworks for Generalized Low-Rank Matrix Bandit Problems |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Efficient Graph Similarity Computation with Alignment Regularization |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Efficient Knowledge Distillation from Model Checkpoints |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Efficient Meta Reinforcement Learning for Preference-based Fast Adaptation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Efficient Methods for Non-stationary Online Learning |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Efficient Multi-agent Communication via Self-supervised Information Aggregation |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Efficient Non-Parametric Optimizer Search for Diverse Tasks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Efficient Phi-Regret Minimization in Extensive-Form Games via Online Mirror Descent |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Efficient Risk-Averse Reinforcement Learning |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Efficient Sampling on Riemannian Manifolds via Langevin MCMC |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Efficient Scheduling of Data Augmentation for Deep Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Efficient Spatially Sparse Inference for Conditional GANs and Diffusion Models |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Efficient Submodular Optimization under Noise: Local Search is Robust |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Efficient Training of Low-Curvature Neural Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Efficient and Effective Augmentation Strategy for Adversarial Training |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Efficient and Effective Multi-task Grouping via Meta Learning on Task Combinations |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Efficient and Effective Optimal Transport-Based Biclustering |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Efficient and Modular Implicit Differentiation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Efficient and Near-Optimal Smoothed Online Learning for Generalized Linear Functions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Efficient and Stable Fully Dynamic Facility Location |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Efficient coding, channel capacity, and the emergence of retinal mosaics |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient identification of informative features in simulation-based inference |
❌ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Efficient learning of nonlinear prediction models with time-series privileged information |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| EfficientFormer: Vision Transformers at MobileNet Speed |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Efficiently Computing Local Lipschitz Constants of Neural Networks via Bound Propagation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Efficiently Factorizing Boolean Matrices using Proximal Gradient Descent |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Egocentric Video-Language Pretraining |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| ElasticMVS: Learning elastic part representation for self-supervised multi-view stereopsis |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Eliciting Thinking Hierarchy without a Prior |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Elucidating the Design Space of Diffusion-Based Generative Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Embed and Emulate: Learning to estimate parameters of dynamical systems with uncertainty quantification |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Embodied Scene-aware Human Pose Estimation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Embrace the Gap: VAEs Perform Independent Mechanism Analysis |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Embracing Consistency: A One-Stage Approach for Spatio-Temporal Video Grounding |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Emergence of Hierarchical Layers in a Single Sheet of Self-Organizing Spiking Neurons |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Emergent Communication: Generalization and Overfitting in Lewis Games |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Emergent Graphical Conventions in a Visual Communication Game |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Empirical Gateaux Derivatives for Causal Inference |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Empirical Phase Diagram for Three-layer Neural Networks with Infinite Width |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| End-to-end Algorithm Synthesis with Recurrent Networks: Extrapolation without Overthinking |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| End-to-end Stochastic Optimization with Energy-based Model |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| End-to-end Symbolic Regression with Transformers |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Energy-Based Contrastive Learning of Visual Representations |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Enhance the Visual Representation via Discrete Adversarial Training |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Enhanced Bilevel Optimization via Bregman Distance |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Enhanced Latent Space Blind Model for Real Image Denoising via Alternative Optimization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Enhanced Meta Reinforcement Learning via Demonstrations in Sparse Reward Environments |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Enhancing Safe Exploration Using Safety State Augmentation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Ensemble of Averages: Improving Model Selection and Boosting Performance in Domain Generalization |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Entropy-Driven Mixed-Precision Quantization for Deep Network Design |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Environment Diversification with Multi-head Neural Network for Invariant Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Envy-free Policy Teaching to Multiple Agents |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| EpiGRAF: Rethinking training of 3D GANs |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Equivariant Graph Hierarchy-Based Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Equivariant Networks for Crystal Structures |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Equivariant Networks for Zero-Shot Coordination |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Error Analysis of Tensor-Train Cross Approximation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Error Correction Code Transformer |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Escaping Saddle Points for Effective Generalization on Class-Imbalanced Data |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Escaping Saddle Points with Bias-Variance Reduced Local Perturbed SGD for Communication Efficient Nonconvex Distributed Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Escaping from the Barren Plateau via Gaussian Initializations in Deep Variational Quantum Circuits |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Estimating Noise Transition Matrix with Label Correlations for Noisy Multi-Label Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Estimating and Explaining Model Performance When Both Covariates and Labels Shift |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Estimating graphical models for count data with applications to single-cell gene network |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Estimating the Arc Length of the Optimal ROC Curve and Lower Bounding the Maximal AUC |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Estimation of Entropy in Constant Space with Improved Sample Complexity |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Evaluated CMI Bounds for Meta Learning: Tightness and Expressiveness |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Evaluating Graph Generative Models with Contrastively Learned Features |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Evaluating Latent Space Robustness and Uncertainty of EEG-ML Models under Realistic Distribution Shifts |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Evaluating Robustness to Dataset Shift via Parametric Robustness Sets |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Evaluation beyond Task Performance: Analyzing Concepts in AlphaZero in Hex |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| EvenNet: Ignoring Odd-Hop Neighbors Improves Robustness of Graph Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Evolution of Neural Tangent Kernels under Benign and Adversarial Training |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Exact Shape Correspondence via 2D graph convolution |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Exact Solutions of a Deep Linear Network |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Exact learning dynamics of deep linear networks with prior knowledge |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Expansion and Shrinkage of Localization for Weakly-Supervised Semantic Segmentation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Expectation-Maximization Contrastive Learning for Compact Video-and-Language Representations |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Expected Frequency Matrices of Elections: Computation, Geometry, and Preference Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Expected Improvement for Contextual Bandits |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Expediting Large-Scale Vision Transformer for Dense Prediction without Fine-tuning |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Experimental Design for Linear Functionals in Reproducing Kernel Hilbert Spaces |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Explain My Surprise: Learning Efficient Long-Term Memory by predicting uncertain outcomes |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Explainability Via Causal Self-Talk |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Explainable Reinforcement Learning via Model Transforms |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Explaining Preferences with Shapley Values |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
5 |
| Explicable Policy Search |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Explicit Tradeoffs between Adversarial and Natural Distributional Robustness |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Exploit Reward Shifting in Value-Based Deep-RL: Optimistic Curiosity-Based Exploration and Conservative Exploitation via Linear Reward Shaping |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Exploitability Minimization in Games and Beyond |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Exploiting Semantic Relations for Glass Surface Detection |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Exploiting the Relationship Between Kendall's Rank Correlation and Cosine Similarity for Attribution Protection |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Exploration via Elliptical Episodic Bonuses |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Exploration via Planning for Information about the Optimal Trajectory |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Exploration-Guided Reward Shaping for Reinforcement Learning under Sparse Rewards |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Exploring Example Influence in Continual Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Exploring Figure-Ground Assignment Mechanism in Perceptual Organization |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Exploring Length Generalization in Large Language Models |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Exploring evolution-aware & -free protein language models as protein function predictors |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Exploring the Algorithm-Dependent Generalization of AUPRC Optimization with List Stability |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Exploring the Latent Space of Autoencoders with Interventional Assays |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Exploring the Limits of Domain-Adaptive Training for Detoxifying Large-Scale Language Models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Exploring the Whole Rashomon Set of Sparse Decision Trees |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Exploring through Random Curiosity with General Value Functions |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Exponential Family Model-Based Reinforcement Learning via Score Matching |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Exponential Separations in Symmetric Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Exponentially Improving the Complexity of Simulating the Weisfeiler-Lehman Test with Graph Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Exposing and Exploiting Fine-Grained Block Structures for Fast and Accurate Sparse Training |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Extra-Newton: A First Approach to Noise-Adaptive Accelerated Second-Order Methods |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Extracting computational mechanisms from neural data using low-rank RNNs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Extrapolation and Spectral Bias of Neural Nets with Hadamard Product: a Polynomial Net Study |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Extrapolative Continuous-time Bayesian Neural Network for Fast Training-free Test-time Adaptation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| FIRE: Semantic Field of Words Represented as Non-Linear Functions |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| FNeVR: Neural Volume Rendering for Face Animation |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| FOF: Learning Fourier Occupancy Field for Monocular Real-time Human Reconstruction |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| FP8 Quantization: The Power of the Exponent |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| FR: Folded Rationalization with a Unified Encoder |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Factored Adaptation for Non-Stationary Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Factored DRO: Factored Distributionally Robust Policies for Contextual Bandits |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Factorized-FL: Personalized Federated Learning with Parameter Factorization & Similarity Matching |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Factuality Enhanced Language Models for Open-Ended Text Generation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fair Bayes-Optimal Classifiers Under Predictive Parity |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Fair Infinitesimal Jackknife: Mitigating the Influence of Biased Training Data Points Without Refitting |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Fair Rank Aggregation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Fair Ranking with Noisy Protected Attributes |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Fair Wrapping for Black-box Predictions |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Fair and Efficient Allocations Without Obvious Manipulations |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Fair and Optimal Decision Trees: A Dynamic Programming Approach |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| FairVFL: A Fair Vertical Federated Learning Framework with Contrastive Adversarial Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Fairness Reprogramming |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Fairness Transferability Subject to Bounded Distribution Shift |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Fairness in Federated Learning via Core-Stability |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Fairness without Demographics through Knowledge Distillation |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Falconn++: A Locality-sensitive Filtering Approach for Approximate Nearest Neighbor Search |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Falsification before Extrapolation in Causal Effect Estimation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Fast Algorithms for Packing Proportional Fairness and its Dual |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Fast Bayesian Coresets via Subsampling and Quasi-Newton Refinement |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Fast Bayesian Estimation of Point Process Intensity as Function of Covariates |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Fast Bayesian Inference with Batch Bayesian Quadrature via Kernel Recombination |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Fast Distance Oracles for Any Symmetric Norm |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Fast Instrument Learning with Faster Rates |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Fast Mixing of Stochastic Gradient Descent with Normalization and Weight Decay |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fast Neural Kernel Embeddings for General Activations |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Fast Stochastic Composite Minimization and an Accelerated Frank-Wolfe Algorithm under Parallelization |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Fast Vision Transformers with HiLo Attention |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Faster Deep Reinforcement Learning with Slower Online Network |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Faster Linear Algebra for Distance Matrices |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Faster Stochastic Algorithms for Minimax Optimization under Polyak-{\L}ojasiewicz Condition |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Faster and Scalable Algorithms for Densest Subgraph and Decomposition |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| FasterRisk: Fast and Accurate Interpretable Risk Scores |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Fault-Aware Neural Code Rankers |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| FeLMi : Few shot Learning with hard Mixup |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Feature Learning in $L_2$-regularized DNNs: Attraction/Repulsion and Sparsity |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Feature-Proxy Transformer for Few-Shot Segmentation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| FedAvg with Fine Tuning: Local Updates Lead to Representation Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| FedPop: A Bayesian Approach for Personalised Federated Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| FedRolex: Model-Heterogeneous Federated Learning with Rolling Sub-Model Extraction |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| FedSR: A Simple and Effective Domain Generalization Method for Federated Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Federated Learning from Pre-Trained Models: A Contrastive Learning Approach |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Federated Submodel Optimization for Hot and Cold Data Features |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Few-Shot Audio-Visual Learning of Environment Acoustics |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Few-Shot Continual Active Learning by a Robot |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Few-Shot Fast-Adaptive Anomaly Detection |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Few-Shot Non-Parametric Learning with Deep Latent Variable Model |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Few-Shot Parameter-Efficient Fine-Tuning is Better and Cheaper than In-Context Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Few-shot Image Generation via Adaptation-Aware Kernel Modulation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Few-shot Learning for Feature Selection with Hilbert-Schmidt Independence Criterion |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Few-shot Relational Reasoning via Connection Subgraph Pretraining |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Few-shot Task-agnostic Neural Architecture Search for Distilling Large Language Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| FiLM-Ensemble: Probabilistic Deep Learning via Feature-wise Linear Modulation |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| FiLM: Frequency improved Legendre Memory Model for Long-term Time Series Forecasting |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Finding Correlated Equilibrium of Constrained Markov Game: A Primal-Dual Approach |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Finding Differences Between Transformers and ConvNets Using Counterfactual Simulation Testing |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Finding Optimal Arms in Non-stochastic Combinatorial Bandits with Semi-bandit Feedback and Finite Budget |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Finding Second-Order Stationary Points in Nonconvex-Strongly-Concave Minimax Optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Finding and Listing Front-door Adjustment Sets |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Fine-Grained Analysis of Stability and Generalization for Modern Meta Learning Algorithms |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fine-Grained Semantically Aligned Vision-Language Pre-Training |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Fine-Tuning Pre-Trained Language Models Effectively by Optimizing Subnetworks Adaptively |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Fine-tuning Language Models over Slow Networks using Activation Quantization with Guarantees |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Fine-tuning language models to find agreement among humans with diverse preferences |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Finite Sample Analysis Of Dynamic Regression Parameter Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Finite-Sample Maximum Likelihood Estimation of Location |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Finite-Time Analysis of Adaptive Temporal Difference Learning with Deep Neural Networks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Finite-Time Last-Iterate Convergence for Learning in Multi-Player Games |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| First Contact: Unsupervised Human-Machine Co-Adaptation via Mutual Information Maximization |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| First Hitting Diffusion Models for Generating Manifold, Graph and Categorical Data |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| First is Better Than Last for Language Data Influence |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| First-Order Algorithms for Min-Max Optimization in Geodesic Metric Spaces |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Fixed-Distance Hamiltonian Monte Carlo |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Flamingo: a Visual Language Model for Few-Shot Learning |
✅ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| FlashAttention: Fast and Memory-Efficient Exact Attention with IO-Awareness |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
5 |
| Flexible Diffusion Modeling of Long Videos |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Flexible Neural Image Compression via Code Editing |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| FlowHMM: Flow-based continuous hidden Markov models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Flowification: Everything is a normalizing flow |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Focal Modulation Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Follow-the-Perturbed-Leader for Adversarial Markov Decision Processes with Bandit Feedback |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Forecasting Human Trajectory from Scene History |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Formalizing Consistency and Coherence of Representation Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Formulating Robustness Against Unforeseen Attacks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Forward-Backward Latent State Inference for Hidden Continuous-Time semi-Markov Chains |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Foundation Posteriors for Approximate Probabilistic Inference |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| FourierFormer: Transformer Meets Generalized Fourier Integral Theorem |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| FourierNets enable the design of highly non-local optical encoders for computational imaging |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Frank-Wolfe-based Algorithms for Approximating Tyler's M-estimator |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| FreGAN: Exploiting Frequency Components for Training GANs under Limited Data |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Free Probability for predicting the performance of feed-forward fully connected neural networks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Friendly Noise against Adversarial Noise: A Powerful Defense against Data Poisoning Attack |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| From Gradient Flow on Population Loss to Learning with Stochastic Gradient Descent |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Fully Convolutional One-Stage 3D Object Detection on LiDAR Range Images |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Fully Sparse 3D Object Detection |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Function Classes for Identifiable Nonlinear Independent Component Analysis |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Functional Ensemble Distillation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Functional Indirection Neural Estimator for Better Out-of-distribution Generalization |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fused Orthogonal Alternating Least Squares for Tensor Clustering |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Fuzzy Learning Machine |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| GAGA: Deciphering Age-path of Generalized Self-paced Regularizer |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| GAL: Gradient Assisted Learning for Decentralized Multi-Organization Collaborations |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| GALOIS: Boosting Deep Reinforcement Learning via Generalizable Logic Synthesis |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| GAMA: Generative Adversarial Multi-Object Scene Attacks |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| GAPX: Generalized Autoregressive Paraphrase-Identification X |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| GAR: Generalized Autoregression for Multi-Fidelity Fusion |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| GAUDI: A Neural Architect for Immersive 3D Scene Generation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| GBA: A Tuning-free Approach to Switch between Synchronous and Asynchronous Training for Recommendation Models |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| GENIE: Higher-Order Denoising Diffusion Solvers |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| GET3D: A Generative Model of High Quality 3D Textured Shapes Learned from Images |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| GLIF: A Unified Gated Leaky Integrate-and-Fire Neuron for Spiking Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| GLIPv2: Unifying Localization and Vision-Language Understanding |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| GMMSeg: Gaussian Mixture based Generative Semantic Segmentation Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| GPT3.int8(): 8-bit Matrix Multiplication for Transformers at Scale |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| GRASP: Navigating Retrosynthetic Planning with Goal-driven Policy |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| GREED: A Neural Framework for Learning Graph Distance Functions |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| GStarX: Explaining Graph Neural Networks with Structure-Aware Cooperative Games |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| GT-GAN: General Purpose Time Series Synthesis with Generative Adversarial Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| GULP: a prediction-based metric between representations |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Gaussian Copula Embeddings |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| GenSDF: Two-Stage Learning of Generalizable Signed Distance Functions |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| GenerSpeech: Towards Style Transfer for Generalizable Out-Of-Domain Text-to-Speech |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| General Cutting Planes for Bound-Propagation-Based Neural Network Verification |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Generalised Implicit Neural Representations |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Generalised Mutual Information for Discriminative Clustering |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Generalization Analysis of Message Passing Neural Networks on Large Random Graphs |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Generalization Analysis on Learning with a Concurrent Verifier |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Generalization Bounds for Estimating Causal Effects of Continuous Treatments |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Generalization Bounds for Gradient Methods via Discrete and Continuous Prior |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Generalization Bounds for Stochastic Gradient Descent via Localized $\varepsilon$-Covers |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Generalization Bounds with Minimal Dependency on Hypothesis Class via Distributionally Robust Optimization |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
3 |
| Generalization Error Bounds on Deep Learning with Markov Datasets |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Generalization Gap in Amortized Inference |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Generalization Properties of NAS under Activation and Skip Connection Search |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Generalization for multiclass classification with overparameterized linear models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Generalized Delayed Feedback Model with Post-Click Information in Recommender Systems |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Generalized Laplacian Eigenmaps |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
4 |
| Generalized One-shot Domain Adaptation of Generative Adversarial Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Generalized Variational Inference in Function Spaces: Gaussian Measures meet Bayesian Deep Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Generalizing Bayesian Optimization with Decision-theoretic Entropies |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| Generalizing Consistent Multi-Class Classification with Rejection to be Compatible with Arbitrary Losses |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Generalizing Goal-Conditioned Reinforcement Learning with Variational Causal Reasoning |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Generating Long Videos of Dynamic Scenes |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Generating Training Data with Language Models: Towards Zero-Shot Language Understanding |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Generating multivariate time series with COmmon Source CoordInated GAN (COSCI-GAN) |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Generative Neural Articulated Radiance Fields |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Generative Status Estimation and Information Decoupling for Image Rain Removal |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Generative Time Series Forecasting with Diffusion, Denoise, and Disentanglement |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Generative Visual Prompt: Unifying Distributional Control of Pre-Trained Generative Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Generative multitask learning mitigates target-causing confounding |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Generic bounds on the approximation error for physics-informed (and) operator learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Geo-Neus: Geometry-Consistent Neural Implicit Surfaces Learning for Multi-view Reconstruction |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Geo-SIC: Learning Deformable Geometric Shapes in Deep Image Classifiers |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Geodesic Graph Neural Network for Efficient Graph Representation Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Geodesic Self-Attention for 3D Point Clouds |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Geometric Knowledge Distillation: Topology Compression for Graph Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Geometric Order Learning for Rank Estimation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Geometry-aware Two-scale PIFu Representation for Human Reconstruction |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Get More at Once: Alternating Sparse Training with Gradient Correction |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| GhostNetV2: Enhance Cheap Operation with Long-Range Attention |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Giga-scale Kernel Matrix-Vector Multiplication on GPU |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Giving Feedback on Interactive Student Programs with Meta-Exploration |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| GlanceNets: Interpretable, Leak-proof Concept-based Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Global Convergence and Stability of Stochastic Gradient Descent |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Global Convergence of Direct Policy Search for State-Feedback $\mathcal{H}_\infty$ Robust Control: A Revisit of Nonsmooth Synthesis with Goldstein Subdifferential |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Global Convergence of Federated Learning for Mixed Regression |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Global Linear and Local Superlinear Convergence of IRLS for Non-Smooth Robust Regression |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
4 |
| Global Normalization for Streaming Speech Recognition in a Modular Framework |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Global Optimal K-Medoids Clustering of One Million Samples |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Globally Convergent Policy Search for Output Estimation |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Globally Gated Deep Linear Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Gold-standard solutions to the Schrödinger equation using deep learning: How much physics do we need? |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| GraB: Finding Provably Better Data Permutations than Random Reshuffling |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| Gradient Descent Is Optimal Under Lower Restricted Secant Inequality And Upper Error Bound |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Gradient Descent: The Ultimate Optimizer |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Gradient Estimation with Discrete Stein Operators |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
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5 |
| Gradient Methods Provably Converge to Non-Robust Networks |
❌ |
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❌ |
❌ |
✅ |
1 |
| Gradient flow dynamics of shallow ReLU networks for square loss and orthogonal inputs |
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✅ |
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❌ |
✅ |
2 |
| Gradient-Free Methods for Deterministic and Stochastic Nonsmooth Nonconvex Optimization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Graph Coloring via Neural Networks for Haplotype Assembly and Viral Quasispecies Reconstruction |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Graph Convolution Network based Recommender Systems: Learning Guarantee and Item Mixture Powered Strategy |
❌ |
❌ |
✅ |
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❌ |
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✅ |
3 |
| Graph Few-shot Learning with Task-specific Structures |
❌ |
✅ |
✅ |
✅ |
❌ |
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✅ |
4 |
| Graph Learning Assisted Multi-Objective Integer Programming |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Graph Neural Network Bandits |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Graph Neural Networks are Dynamic Programmers |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Graph Neural Networks with Adaptive Readouts |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Graph Reordering for Cache-Efficient Near Neighbor Search |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Graph Scattering beyond Wavelet Shackles |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Graph Self-supervised Learning with Accurate Discrepancy Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
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✅ |
4 |
| GraphDE: A Generative Framework for Debiased Learning and Out-of-Distribution Detection on Graphs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| GraphQNTK: Quantum Neural Tangent Kernel for Graph Data |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Graphein - a Python Library for Geometric Deep Learning and Network Analysis on Biomolecular Structures and Interaction Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Green Hierarchical Vision Transformer for Masked Image Modeling |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Grounded Reinforcement Learning: Learning to Win the Game under Human Commands |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Grounded Video Situation Recognition |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Grounding Aleatoric Uncertainty for Unsupervised Environment Design |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Group Meritocratic Fairness in Linear Contextual Bandits |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Grow and Merge: A Unified Framework for Continuous Categories Discovery |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Guaranteed Conservation of Momentum for Learning Particle-based Fluid Dynamics |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| HF-NeuS: Improved Surface Reconstruction Using High-Frequency Details |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| HSDF: Hybrid Sign and Distance Field for Modeling Surfaces with Arbitrary Topologies |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| HSurf-Net: Normal Estimation for 3D Point Clouds by Learning Hyper Surfaces |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| HUMANISE: Language-conditioned Human Motion Generation in 3D Scenes |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| HUMUS-Net: Hybrid Unrolled Multi-scale Network Architecture for Accelerated MRI Reconstruction |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| HYPRO: A Hybridly Normalized Probabilistic Model for Long-Horizon Prediction of Event Sequences |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Hamiltonian Latent Operators for content and motion disentanglement in image sequences |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Hand-Object Interaction Image Generation |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Handcrafted Backdoors in Deep Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Hardness in Markov Decision Processes: Theory and Practice |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Hardness of Noise-Free Learning for Two-Hidden-Layer Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Harmonizing the object recognition strategies of deep neural networks with humans |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Heatmap Distribution Matching for Human Pose Estimation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Hedging as Reward Augmentation in Probabilistic Graphical Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Heterogeneous Skill Learning for Multi-agent Tasks |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Hidden Progress in Deep Learning: SGD Learns Parities Near the Computational Limit |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Hiding Images in Deep Probabilistic Models |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| HierSpeech: Bridging the Gap between Text and Speech by Hierarchical Variational Inference using Self-supervised Representations for Speech Synthesis |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Hierarchical Normalization for Robust Monocular Depth Estimation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Hierarchical Agglomerative Graph Clustering in Poly-Logarithmic Depth |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Hierarchical Channel-spatial Encoding for Communication-efficient Collaborative Learning |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
4 |
| Hierarchical Graph Transformer with Adaptive Node Sampling |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Hierarchical Lattice Layer for Partially Monotone Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Hierarchical classification at multiple operating points |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| High-Order Pooling for Graph Neural Networks with Tensor Decomposition |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| High-dimensional Additive Gaussian Processes under Monotonicity Constraints |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
5 |
| High-dimensional Asymptotics of Feature Learning: How One Gradient Step Improves the Representation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| High-dimensional limit theorems for SGD: Effective dynamics and critical scaling |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Hilbert Distillation for Cross-Dimensionality Networks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Holomorphic Equilibrium Propagation Computes Exact Gradients Through Finite Size Oscillations |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Homomorphic Matrix Completion |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| HorNet: Efficient High-Order Spatial Interactions with Recursive Gated Convolutions |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| House of Cans: Covert Transmission of Internal Datasets via Capacity-Aware Neuron Steganography |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| How Mask Matters: Towards Theoretical Understandings of Masked Autoencoders |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| How Powerful are K-hop Message Passing Graph Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| How Sampling Impacts the Robustness of Stochastic Neural Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| How and Why to Manipulate Your Own Agent: On the Incentives of Users of Learning Agents |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| How to talk so AI will learn: Instructions, descriptions, and autonomy |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Hub-Pathway: Transfer Learning from A Hub of Pre-trained Models |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Human-AI Collaborative Bayesian Optimisation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Human-AI Shared Control via Policy Dissection |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Human-Robotic Prosthesis as Collaborating Agents for Symmetrical Walking |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| HumanLiker: A Human-like Object Detector to Model the Manual Labeling Process |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Hybrid Neural Autoencoders for Stimulus Encoding in Visual and Other Sensory Neuroprostheses |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Hyper-Representations as Generative Models: Sampling Unseen Neural Network Weights |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| HyperDomainNet: Universal Domain Adaptation for Generative Adversarial Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| HyperMiner: Topic Taxonomy Mining with Hyperbolic Embedding |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| HyperTree Proof Search for Neural Theorem Proving |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Hyperbolic Embedding Inference for Structured Multi-Label Prediction |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Hyperbolic Feature Augmentation via Distribution Estimation and Infinite Sampling on Manifolds |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Hyperparameter Sensitivity in Deep Outlier Detection: Analysis and a Scalable Hyper-Ensemble Solution |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Hypothesis Testing for Differentially Private Linear Regression |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| I2DFormer: Learning Image to Document Attention for Zero-Shot Image Classification |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| I2Q: A Fully Decentralized Q-Learning Algorithm |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| IM-Loss: Information Maximization Loss for Spiking Neural Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| IMED-RL: Regret optimal learning of ergodic Markov decision processes |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| INRAS: Implicit Neural Representation for Audio Scenes |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Identifiability and generalizability from multiple experts in Inverse Reinforcement Learning |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Identifiability of deep generative models without auxiliary information |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Identification, Amplification and Measurement: A bridge to Gaussian Differential Privacy |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Identifying good directions to escape the NTK regime and efficiently learn low-degree plus sparse polynomials |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| If Influence Functions are the Answer, Then What is the Question? |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Imbalance Trouble: Revisiting Neural-Collapse Geometry |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Imitating Past Successes can be Very Suboptimal |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Implications of Model Indeterminacy for Explanations of Automated Decisions |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Implicit Bias of Gradient Descent on Reparametrized Models: On Equivalence to Mirror Descent |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Implicit Neural Representations with Levels-of-Experts |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Implicit Regularization or Implicit Conditioning? Exact Risk Trajectories of SGD in High Dimensions |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Implicit Warping for Animation with Image Sets |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Improved Algorithms for Neural Active Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Improved Bounds on Neural Complexity for Representing Piecewise Linear Functions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Improved Convergence Rate of Stochastic Gradient Langevin Dynamics with Variance Reduction and its Application to Optimization |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Improved Coresets for Euclidean $k$-Means |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Improved Differential Privacy for SGD via Optimal Private Linear Operators on Adaptive Streams |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Improved Feature Distillation via Projector Ensemble |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Improved Fine-Tuning by Better Leveraging Pre-Training Data |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Improved Imaging by Invex Regularizers with Global Optima Guarantees |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Improved Regret Analysis for Variance-Adaptive Linear Bandits and Horizon-Free Linear Mixture MDPs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Improved Utility Analysis of Private CountSketch |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Improved techniques for deterministic l2 robustness |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Improving 3D-aware Image Synthesis with A Geometry-aware Discriminator |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Improving Barely Supervised Learning by Discriminating Unlabeled Samples with Super-Class |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Improving Certified Robustness via Statistical Learning with Logical Reasoning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Improving Diffusion Models for Inverse Problems using Manifold Constraints |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Improving GANs with A Dynamic Discriminator |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Improving Generative Adversarial Networks via Adversarial Learning in Latent Space |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Improving Intrinsic Exploration with Language Abstractions |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Improving Multi-Task Generalization via Regularizing Spurious Correlation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Improving Neural Ordinary Differential Equations with Nesterov's Accelerated Gradient Method |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Improving Out-of-Distribution Generalization by Adversarial Training with Structured Priors |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Improving Policy Learning via Language Dynamics Distillation |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
2 |
| Improving Self-Supervised Learning by Characterizing Idealized Representations |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Improving Task-Specific Generalization in Few-Shot Learning via Adaptive Vicinal Risk Minimization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Improving Transformer with an Admixture of Attention Heads |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Improving Variational Autoencoders with Density Gap-based Regularization |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Improving Zero-Shot Generalization in Offline Reinforcement Learning using Generalized Similarity Functions |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| In Defense of the Unitary Scalarization for Deep Multi-Task Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| In Differential Privacy, There is Truth: on Vote-Histogram Leakage in Ensemble Private Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| In What Ways Are Deep Neural Networks Invariant and How Should We Measure This? |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| In the Eye of the Beholder: Robust Prediction with Causal User Modeling |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Incentivizing Combinatorial Bandit Exploration |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Inception Transformer |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Incorporating Bias-aware Margins into Contrastive Loss for Collaborative Filtering |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Increasing Confidence in Adversarial Robustness Evaluations |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspaces |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Incrementality Bidding via Reinforcement Learning under Mixed and Delayed Rewards |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Independence Testing for Bounded Degree Bayesian Networks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Independence Testing-Based Approach to Causal Discovery under Measurement Error and Linear Non-Gaussian Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Inducing Equilibria via Incentives: Simultaneous Design-and-Play Ensures Global Convergence |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Inducing Neural Collapse in Imbalanced Learning: Do We Really Need a Learnable Classifier at the End of Deep Neural Network? |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Inductive Logical Query Answering in Knowledge Graphs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Inference and Sampling for Archimax Copulas |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Infinite Recommendation Networks: A Data-Centric Approach |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Infinite-Fidelity Coregionalization for Physical Simulation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Influencing Long-Term Behavior in Multiagent Reinforcement Learning |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
5 |
| Information bottleneck theory of high-dimensional regression: relevancy, efficiency and optimality |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Information-Theoretic GAN Compression with Variational Energy-based Model |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Information-Theoretic Safe Exploration with Gaussian Processes |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Inherently Explainable Reinforcement Learning in Natural Language |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Injecting Domain Knowledge from Empirical Interatomic Potentials to Neural Networks for Predicting Material Properties |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| InsNet: An Efficient, Flexible, and Performant Insertion-based Text Generation Model |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| InsPro: Propagating Instance Query and Proposal for Online Video Instance Segmentation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Insights into Pre-training via Simpler Synthetic Tasks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Instability and Local Minima in GAN Training with Kernel Discriminators |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Instance-Based Uncertainty Estimation for Gradient-Boosted Regression Trees |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Instance-Dependent Near-Optimal Policy Identification in Linear MDPs via Online Experiment Design |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Instance-based Learning for Knowledge Base Completion |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
4 |
| Instance-optimal PAC Algorithms for Contextual Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Integral Probability Metrics PAC-Bayes Bounds |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Interaction Modeling with Multiplex Attention |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Interaction-Grounded Learning with Action-Inclusive Feedback |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Intermediate Prototype Mining Transformer for Few-Shot Semantic Segmentation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Interpolation and Regularization for Causal Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Interpreting Operation Selection in Differentiable Architecture Search: A Perspective from Influence-Directed Explanations |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Interventions, Where and How? Experimental Design for Causal Models at Scale |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Intra-agent speech permits zero-shot task acquisition |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Intrinsic dimensionality estimation using Normalizing Flows |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Introspective Learning : A Two-Stage approach for Inference in Neural Networks |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
4 |
| Invariance Learning based on Label Hierarchy |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Invariance-Aware Randomized Smoothing Certificates |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Invariant and Transportable Representations for Anti-Causal Domain Shifts |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Inverse Design for Fluid-Structure Interactions using Graph Network Simulators |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
❌ |
3 |
| Inverse Game Theory for Stackelberg Games: the Blessing of Bounded Rationality |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Invertible Monotone Operators for Normalizing Flows |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Iron: Private Inference on Transformers |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Is $L^2$ Physics Informed Loss Always Suitable for Training Physics Informed Neural Network? |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Is Integer Arithmetic Enough for Deep Learning Training? |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Is Out-of-Distribution Detection Learnable? |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Is Sortition Both Representative and Fair? |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Is a Modular Architecture Enough? |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
2 |
| Is this the Right Neighborhood? Accurate and Query Efficient Model Agnostic Explanations |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Iso-Dream: Isolating and Leveraging Noncontrollable Visual Dynamics in World Models |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Isometric 3D Adversarial Examples in the Physical World |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Iterative Feature Matching: Toward Provable Domain Generalization with Logarithmic Environments |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Iterative Scene Graph Generation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Iterative Structural Inference of Directed Graphs |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| JAWS: Auditing Predictive Uncertainty Under Covariate Shift |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Joint Entropy Search For Maximally-Informed Bayesian Optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Joint Entropy Search for Multi-Objective Bayesian Optimization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Joint Learning of 2D-3D Weakly Supervised Semantic Segmentation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Jump Self-attention: Capturing High-order Statistics in Transformers |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| K-LITE: Learning Transferable Visual Models with External Knowledge |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| KERPLE: Kernelized Relative Positional Embedding for Length Extrapolation |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| KSD Aggregated Goodness-of-fit Test |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Kernel Interpolation with Sparse Grids |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Kernel Memory Networks: A Unifying Framework for Memory Modeling |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Kernel Multimodal Continuous Attention |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Kernel similarity matching with Hebbian networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Keypoint-Guided Optimal Transport with Applications in Heterogeneous Domain Adaptation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Knowledge Distillation Improves Graph Structure Augmentation for Graph Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Knowledge Distillation from A Stronger Teacher |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Knowledge Distillation: Bad Models Can Be Good Role Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Knowledge-Aware Bayesian Deep Topic Model |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| LAMP: Extracting Text from Gradients with Language Model Priors |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| LAPO: Latent-Variable Advantage-Weighted Policy Optimization for Offline Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| LASSIE: Learning Articulated Shapes from Sparse Image Ensemble via 3D Part Discovery |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| LBD: Decouple Relevance and Observation for Individual-Level Unbiased Learning to Rank |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| LDSA: Learning Dynamic Subtask Assignment in Cooperative Multi-Agent Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| LECO: Learnable Episodic Count for Task-Specific Intrinsic Reward |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| LGDN: Language-Guided Denoising Network for Video-Language Modeling |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| LIFT: Language-Interfaced Fine-Tuning for Non-language Machine Learning Tasks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| LION: Latent Point Diffusion Models for 3D Shape Generation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| LISA: Learning Interpretable Skill Abstractions from Language |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| LOG: Active Model Adaptation for Label-Efficient OOD Generalization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| LOT: Layer-wise Orthogonal Training on Improving l2 Certified Robustness |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| LST: Ladder Side-Tuning for Parameter and Memory Efficient Transfer Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| LTMD: Learning Improvement of Spiking Neural Networks with Learnable Thresholding Neurons and Moderate Dropout |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Label Noise in Adversarial Training: A Novel Perspective to Study Robust Overfitting |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Label-Aware Global Consistency for Multi-Label Learning with Single Positive Labels |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Label-invariant Augmentation for Semi-Supervised Graph Classification |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Langevin Autoencoders for Learning Deep Latent Variable Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Language Conditioned Spatial Relation Reasoning for 3D Object Grounding |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Language Models with Image Descriptors are Strong Few-Shot Video-Language Learners |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Laplacian Autoencoders for Learning Stochastic Representations |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Large Language Models are Zero-Shot Reasoners |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Large-Scale Differentiable Causal Discovery of Factor Graphs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
4 |
| Large-Scale Retrieval for Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Large-batch Optimization for Dense Visual Predictions: Training Faster R-CNN in 4.2 Minutes |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Large-scale Optimization of Partial AUC in a Range of False Positive Rates |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| LasUIE: Unifying Information Extraction with Latent Adaptive Structure-aware Generative Language Model |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Last-Iterate Convergence of Optimistic Gradient Method for Monotone Variational Inequalities |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Latency-aware Spatial-wise Dynamic Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Latent Hierarchical Causal Structure Discovery with Rank Constraints |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Latent Planning via Expansive Tree Search |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Layer Freezing & Data Sieving: Missing Pieces of a Generic Framework for Sparse Training |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Lazy and Fast Greedy MAP Inference for Determinantal Point Process |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Learn to Explain: Multimodal Reasoning via Thought Chains for Science Question Answering |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learn what matters: cross-domain imitation learning with task-relevant embeddings |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learnable Polyphase Sampling for Shift Invariant and Equivariant Convolutional Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Learning (Very) Simple Generative Models Is Hard |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Learning Active Camera for Multi-Object Navigation |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Articulated Rigid Body Dynamics with Lagrangian Graph Neural Network |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
5 |
| Learning Audio-Visual Dynamics Using Scene Graphs for Audio Source Separation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning Best Combination for Efficient N:M Sparsity |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Learning Bipartite Graphs: Heavy Tails and Multiple Components |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning Causally Invariant Representations for Out-of-Distribution Generalization on Graphs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Chaotic Dynamics in Dissipative Systems |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Concept Credible Models for Mitigating Shortcuts |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning Consistency-Aware Unsigned Distance Functions Progressively from Raw Point Clouds |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Contrastive Embedding in Low-Dimensional Space |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning Debiased Classifier with Biased Committee |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning Deep Input-Output Stable Dynamics |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Learning Dense Object Descriptors from Multiple Views for Low-shot Category Generalization |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning Distinct and Representative Modes for Image Captioning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Distributed and Fair Policies for Network Load Balancing as Markov Potential Game |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| Learning Distributions Generated by Single-Layer ReLU Networks in the Presence of Arbitrary Outliers |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Dynamical Systems via Koopman Operator Regression in Reproducing Kernel Hilbert Spaces |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning Efficient Vision Transformers via Fine-Grained Manifold Distillation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Energy Networks with Generalized Fenchel-Young Losses |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning Enhanced Representation for Tabular Data via Neighborhood Propagation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Learning Equivariant Segmentation with Instance-Unique Querying |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning Expressive Meta-Representations with Mixture of Expert Neural Processes |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Learning Fractional White Noises in Neural Stochastic Differential Equations |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Learning General World Models in a Handful of Reward-Free Deployments |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Learning Generalizable Models for Vehicle Routing Problems via Knowledge Distillation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning Generalizable Part-based Feature Representation for 3D Point Clouds |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Generalized Policy Automata for Relational Stochastic Shortest Path Problems |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Learning Graph-embedded Key-event Back-tracing for Object Tracking in Event Clouds |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Individualized Treatment Rules with Many Treatments: A Supervised Clustering Approach Using Adaptive Fusion |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning Infinite-Horizon Average-Reward Restless Multi-Action Bandits via Index Awareness |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning Interface Conditions in Domain Decomposition Solvers |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Learning Invariant Graph Representations for Out-of-Distribution Generalization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning Latent Seasonal-Trend Representations for Time Series Forecasting |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning Manifold Dimensions with Conditional Variational Autoencoders |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Mixed Multinomial Logits with Provable Guarantees |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Modular Simulations for Homogeneous Systems |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning Multi-resolution Functional Maps with Spectral Attention for Robust Shape Matching |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning NP-Hard Multi-Agent Assignment Planning using GNN: Inference on a Random Graph and Provable Auction-Fitted Q-learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Neural Acoustic Fields |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning Neural Set Functions Under the Optimal Subset Oracle |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning Optical Flow from Continuous Spike Streams |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Optimal Flows for Non-Equilibrium Importance Sampling |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Learning Options via Compression |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning Partial Equivariances From Data |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Physical Dynamics with Subequivariant Graph Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning Physics Constrained Dynamics Using Autoencoders |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Predictions for Algorithms with Predictions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning Probabilistic Models from Generator Latent Spaces with Hat EBM |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Learning Recourse on Instance Environment to Enhance Prediction Accuracy |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning Representations via a Robust Behavioral Metric for Deep Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Robust Dynamics through Variational Sparse Gating |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning Robust Rule Representations for Abstract Reasoning via Internal Inferences |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Learning State-Aware Visual Representations from Audible Interactions |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Structure from the Ground up---Hierarchical Representation Learning by Chunking |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Learning Substructure Invariance for Out-of-Distribution Molecular Representations |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning Superpoint Graph Cut for 3D Instance Segmentation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning Symmetric Rules with SATNet |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning Tractable Probabilistic Models from Inconsistent Local Estimates |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning Two-Player Markov Games: Neural Function Approximation and Correlated Equilibrium |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning Viewpoint-Agnostic Visual Representations by Recovering Tokens in 3D Space |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Learning a Condensed Frame for Memory-Efficient Video Class-Incremental Learning |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Learning and Covering Sums of Independent Random Variables with Unbounded Support |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning dynamics of deep linear networks with multiple pathways |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning from Distributed Users in Contextual Linear Bandits Without Sharing the Context |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning from Few Samples: Transformation-Invariant SVMs with Composition and Locality at Multiple Scales |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning from Future: A Novel Self-Training Framework for Semantic Segmentation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning from Label Proportions by Learning with Label Noise |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| Learning from Stochastically Revealed Preference |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning from a Sample in Online Algorithms |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning in Congestion Games with Bandit Feedback |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning in Observable POMDPs, without Computationally Intractable Oracles |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning interacting dynamical systems with latent Gaussian process ODEs |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Learning low-dimensional generalizable natural features from retina using a U-net |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning on Arbitrary Graph Topologies via Predictive Coding |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning on the Edge: Online Learning with Stochastic Feedback Graphs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning single-index models with shallow neural networks |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning sparse features can lead to overfitting in neural networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning the Structure of Large Networked Systems Obeying Conservation Laws |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning to Accelerate Partial Differential Equations via Latent Global Evolution |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning to Attack Federated Learning: A Model-based Reinforcement Learning Attack Framework |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning to Branch with Tree MDPs |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
3 |
| Learning to Break the Loop: Analyzing and Mitigating Repetitions for Neural Text Generation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning to Compare Nodes in Branch and Bound with Graph Neural Networks |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Learning to Configure Computer Networks with Neural Algorithmic Reasoning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning to Constrain Policy Optimization with Virtual Trust Region |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning to Discover and Detect Objects |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning to Drop Out: An Adversarial Approach to Training Sequence VAEs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning to Find Proofs and Theorems by Learning to Refine Search Strategies: The Case of Loop Invariant Synthesis |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning to Follow Instructions in Text-Based Games |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning to Generate Inversion-Resistant Model Explanations |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Learning to Mitigate AI Collusion on Economic Platforms |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
3 |
| Learning to Navigate Wikipedia by Taking Random Walks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning to Re-weight Examples with Optimal Transport for Imbalanced Classification |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning to Reason with Neural Networks: Generalization, Unseen Data and Boolean Measures |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning to Reconstruct Missing Data from Spatiotemporal Graphs with Sparse Observations |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Learning to Sample and Aggregate: Few-shot Reasoning over Temporal Knowledge Graphs |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning to Scaffold: Optimizing Model Explanations for Teaching |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning to Share in Networked Multi-Agent Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Learning with convolution and pooling operations in kernel methods |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Learning with little mixing |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Learning-Augmented Algorithms for Online Linear and Semidefinite Programming |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
❌ |
4 |
| Learning-based Motion Planning in Dynamic Environments Using GNNs and Temporal Encoding |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Left Heavy Tails and the Effectiveness of the Policy and Value Networks in DNN-based best-first search for Sokoban Planning |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Less-forgetting Multi-lingual Fine-tuning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Let Images Give You More: Point Cloud Cross-Modal Training for Shape Analysis |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Lethal Dose Conjecture on Data Poisoning |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
4 |
| Leveraging Factored Action Spaces for Efficient Offline Reinforcement Learning in Healthcare |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Leveraging Inter-Layer Dependency for Post -Training Quantization |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Leveraging the Hints: Adaptive Bidding in Repeated First-Price Auctions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| LieGG: Studying Learned Lie Group Generators |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Lifelong Neural Predictive Coding: Learning Cumulatively Online without Forgetting |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Lifting Weak Supervision To Structured Prediction |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Linear Label Ranking with Bounded Noise |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Linear tree shap |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Lipschitz Bandits with Batched Feedback |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| List-Decodable Sparse Mean Estimation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| List-Decodable Sparse Mean Estimation via Difference-of-Pairs Filtering |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Listen to Interpret: Post-hoc Interpretability for Audio Networks with NMF |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| LiteTransformerSearch: Training-free Neural Architecture Search for Efficient Language Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| LobsDICE: Offline Learning from Observation via Stationary Distribution Correction Estimation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Local Bayesian optimization via maximizing probability of descent |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Local Identifiability of Deep ReLU Neural Networks: the Theory |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Local Latent Space Bayesian Optimization over Structured Inputs |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Local Linear Convergence of Gradient Methods for Subspace Optimization via Strict Complementarity |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Local Metric Learning for Off-Policy Evaluation in Contextual Bandits with Continuous Actions |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Local Spatiotemporal Representation Learning for Longitudinally-consistent Neuroimage Analysis |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Local-Global MCMC kernels: the best of both worlds |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Locally Hierarchical Auto-Regressive Modeling for Image Generation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Locating and Editing Factual Associations in GPT |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Log-Concave and Multivariate Canonical Noise Distributions for Differential Privacy |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Log-Linear-Time Gaussian Processes Using Binary Tree Kernels |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Log-Polar Space Convolution Layers |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| LogiGAN: Learning Logical Reasoning via Adversarial Pre-training |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Logical Activation Functions: Logit-space equivalents of Probabilistic Boolean Operators |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Logical Credal Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Long-Form Video-Language Pre-Training with Multimodal Temporal Contrastive Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Look Around and Refer: 2D Synthetic Semantics Knowledge Distillation for 3D Visual Grounding |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Look More but Care Less in Video Recognition |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Look where you look! Saliency-guided Q-networks for generalization in visual Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Losses Can Be Blessings: Routing Self-Supervised Speech Representations Towards Efficient Multilingual and Multitask Speech Processing |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Lost in Latent Space: Examining failures of disentangled models at combinatorial generalisation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Lottery Tickets on a Data Diet: Finding Initializations with Sparse Trainable Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Low-Rank Modular Reinforcement Learning via Muscle Synergy |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Low-rank Optimal Transport: Approximation, Statistics and Debiasing |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Low-rank lottery tickets: finding efficient low-rank neural networks via matrix differential equations |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Lower Bounds and Nearly Optimal Algorithms in Distributed Learning with Communication Compression |
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❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Lower Bounds on Randomly Preconditioned Lasso via Robust Sparse Designs |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Luckiness in Multiscale Online Learning |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| M$^4$I: Multi-modal Models Membership Inference |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| M2N: Mesh Movement Networks for PDE Solvers |
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✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| MABSplit: Faster Forest Training Using Multi-Armed Bandits |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| MACE: Higher Order Equivariant Message Passing Neural Networks for Fast and Accurate Force Fields |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| MACK: Multimodal Aligned Conceptual Knowledge for Unpaired Image-text Matching |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| MAgNet: Mesh Agnostic Neural PDE Solver |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| MAtt: A Manifold Attention Network for EEG Decoding |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| MCL-GAN: Generative Adversarial Networks with Multiple Specialized Discriminators |
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✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| MCMAE: Masked Convolution Meets Masked Autoencoders |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| MCVD - Masked Conditional Video Diffusion for Prediction, Generation, and Interpolation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| MEMO: Test Time Robustness via Adaptation and Augmentation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| MExMI: Pool-based Active Model Extraction Crossover Membership Inference |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| MGNNI: Multiscale Graph Neural Networks with Implicit Layers |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| MORA: Improving Ensemble Robustness Evaluation with Model Reweighing Attack |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| MOVE: Unsupervised Movable Object Segmentation and Detection |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Make Sharpness-Aware Minimization Stronger: A Sparsified Perturbation Approach |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Make Some Noise: Reliable and Efficient Single-Step Adversarial Training |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Make an Omelette with Breaking Eggs: Zero-Shot Learning for Novel Attribute Synthesis |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Making Look-Ahead Active Learning Strategies Feasible with Neural Tangent Kernels |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Making Sense of Dependence: Efficient Black-box Explanations Using Dependence Measure |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Manifold Interpolating Optimal-Transport Flows for Trajectory Inference |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Margin-Based Few-Shot Class-Incremental Learning with Class-Level Overfitting Mitigation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Markov Chain Score Ascent: A Unifying Framework of Variational Inference with Markovian Gradients |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Markovian Interference in Experiments |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Marksman Backdoor: Backdoor Attacks with Arbitrary Target Class |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Mask Matching Transformer for Few-Shot Segmentation |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Mask-based Latent Reconstruction for Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| MaskPlace: Fast Chip Placement via Reinforced Visual Representation Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| MaskTune: Mitigating Spurious Correlations by Forcing to Explore |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Masked Autoencoders As Spatiotemporal Learners |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Masked Autoencoders that Listen |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Masked Autoencoding for Scalable and Generalizable Decision Making |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Masked Generative Adversarial Networks are Data-Efficient Generation Learners |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Masked Prediction: A Parameter Identifiability View |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Matching in Multi-arm Bandit with Collision |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Matrix Multiplicative Weights Updates in Quantum Zero-Sum Games: Conservation Laws & Recurrence |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Matryoshka Representation Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Max-Min Off-Policy Actor-Critic Method Focusing on Worst-Case Robustness to Model Misspecification |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
5 |
| Maximizing Revenue under Market Shrinkage and Market Uncertainty |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Maximizing and Satisficing in Multi-armed Bandits with Graph Information |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Maximum Class Separation as Inductive Bias in One Matrix |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Maximum Common Subgraph Guided Graph Retrieval: Late and Early Interaction Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| Maximum Likelihood Training of Implicit Nonlinear Diffusion Model |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
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4 |
| Maximum a posteriori natural scene reconstruction from retinal ganglion cells with deep denoiser priors |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Maximum-Likelihood Inverse Reinforcement Learning with Finite-Time Guarantees |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Mean Estimation in High-Dimensional Binary Markov Gaussian Mixture Models |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Mean Estimation with User-level Privacy under Data Heterogeneity |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Measures of Information Reflect Memorization Patterns |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Measuring Data Reconstruction Defenses in Collaborative Inference Systems |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Measuring and Reducing Model Update Regression in Structured Prediction for NLP |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Memorization Without Overfitting: Analyzing the Training Dynamics of Large Language Models |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
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3 |
| Memorization and Optimization in Deep Neural Networks with Minimum Over-parameterization |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Memory Efficient Continual Learning with Transformers |
✅ |
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✅ |
✅ |
❌ |
❌ |
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4 |
| Memory safe computations with XLA compiler |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Merging Models with Fisher-Weighted Averaging |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Mesoscopic modeling of hidden spiking neurons |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Meta Reinforcement Learning with Finite Training Tasks - a Density Estimation Approach |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Meta-Auto-Decoder for Solving Parametric Partial Differential Equations |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Meta-Complementing the Semantics of Short Texts in Neural Topic Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Meta-DMoE: Adapting to Domain Shift by Meta-Distillation from Mixture-of-Experts |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Meta-Learning Dynamics Forecasting Using Task Inference |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Meta-Learning with Self-Improving Momentum Target |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Meta-Query-Net: Resolving Purity-Informativeness Dilemma in Open-set Active Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Meta-Reinforcement Learning with Self-Modifying Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Meta-Reward-Net: Implicitly Differentiable Reward Learning for Preference-based Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Meta-ticket: Finding optimal subnetworks for few-shot learning within randomly initialized neural networks |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
5 |
| MetaMask: Revisiting Dimensional Confounder for Self-Supervised Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| MetaTeacher: Coordinating Multi-Model Domain Adaptation for Medical Image Classification |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| MetricFormer: A Unified Perspective of Correlation Exploring in Similarity Learning |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Micro and Macro Level Graph Modeling for Graph Variational Auto-Encoders |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Mildly Conservative Q-Learning for Offline Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| MinVIS: A Minimal Video Instance Segmentation Framework without Video-based Training |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Mind Reader: Reconstructing complex images from brain activities |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Mind the Gap: Understanding the Modality Gap in Multi-modal Contrastive Representation Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| Mingling Foresight with Imagination: Model-Based Cooperative Multi-Agent Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Minimax Optimal Algorithms for Fixed-Budget Best Arm Identification |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Minimax Optimal Fixed-Budget Best Arm Identification in Linear Bandits |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Minimax Optimal Online Imitation Learning via Replay Estimation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Minimax Regret for Cascading Bandits |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
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5 |
| Minimax-Optimal Multi-Agent RL in Markov Games With a Generative Model |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Mining Multi-Label Samples from Single Positive Labels |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Mining Unseen Classes via Regional Objectness: A Simple Baseline for Incremental Segmentation |
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✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Mirror Descent Maximizes Generalized Margin and Can Be Implemented Efficiently |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Mirror Descent with Relative Smoothness in Measure Spaces, with application to Sinkhorn and EM |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Mismatched No More: Joint Model-Policy Optimization for Model-Based RL |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
3 |
| Missing Data Imputation and Acquisition with Deep Hierarchical Models and Hamiltonian Monte Carlo |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Misspecified Phase Retrieval with Generative Priors |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Mix and Reason: Reasoning over Semantic Topology with Data Mixing for Domain Generalization |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Mixture-of-Experts with Expert Choice Routing |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| MoCoDA: Model-based Counterfactual Data Augmentation |
❌ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| MoGDE: Boosting Mobile Monocular 3D Object Detection with Ground Depth Estimation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| MoVQ: Modulating Quantized Vectors for High-Fidelity Image Generation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Model Preserving Compression for Neural Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Model-Based Imitation Learning for Urban Driving |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Model-Based Offline Reinforcement Learning with Pessimism-Modulated Dynamics Belief |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Model-Based Opponent Modeling |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Model-based Lifelong Reinforcement Learning with Bayesian Exploration |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Model-based RL with Optimistic Posterior Sampling: Structural Conditions and Sample Complexity |
✅ |
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✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Model-based Safe Deep Reinforcement Learning via a Constrained Proximal Policy Optimization Algorithm |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Modeling Human Exploration Through Resource-Rational Reinforcement Learning |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Modeling Transitivity and Cyclicity in Directed Graphs via Binary Code Box Embeddings |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Modeling the Machine Learning Multiverse |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Models Out of Line: A Fourier Lens on Distribution Shift Robustness |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Moderate-fitting as a Natural Backdoor Defender for Pre-trained Language Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Modular Flows: Differential Molecular Generation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Module-Aware Optimization for Auxiliary Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
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5 |
| Molecule Generation by Principal Subgraph Mining and Assembling |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Moment Distributionally Robust Tree Structured Prediction |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Momentum Adversarial Distillation: Handling Large Distribution Shifts in Data-Free Knowledge Distillation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Momentum Aggregation for Private Non-convex ERM |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| MonoSDF: Exploring Monocular Geometric Cues for Neural Implicit Surface Reconstruction |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Monocular Dynamic View Synthesis: A Reality Check |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Monte Carlo Augmented Actor-Critic for Sparse Reward Deep Reinforcement Learning from Suboptimal Demonstrations |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
3 |
| Monte Carlo Tree Descent for Black-Box Optimization |
✅ |
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✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Monte Carlo Tree Search based Variable Selection for High Dimensional Bayesian Optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| MorphTE: Injecting Morphology in Tensorized Embeddings |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Most Activation Functions Can Win the Lottery Without Excessive Depth |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Motion Transformer with Global Intention Localization and Local Movement Refinement |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Movement Penalized Bayesian Optimization with Application to Wind Energy Systems |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| MsSVT: Mixed-scale Sparse Voxel Transformer for 3D Object Detection on Point Clouds |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Muffliato: Peer-to-Peer Privacy Amplification for Decentralized Optimization and Averaging |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Multi-Agent Reinforcement Learning is a Sequence Modeling Problem |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Multi-Class $H$-Consistency Bounds |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Multi-Fidelity Best-Arm Identification |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Multi-Game Decision Transformers |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Multi-Granularity Cross-modal Alignment for Generalized Medical Visual Representation Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Multi-Instance Causal Representation Learning for Instance Label Prediction and Out-of-Distribution Generalization |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Multi-Lingual Acquisition on Multimodal Pre-training for Cross-modal Retrieval |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Multi-Objective Deep Learning with Adaptive Reference Vectors |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Multi-Sample Training for Neural Image Compression |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Multi-Scale Adaptive Network for Single Image Denoising |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Multi-agent Dynamic Algorithm Configuration |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Multi-agent Performative Prediction with Greedy Deployment and Consensus Seeking Agents |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Multi-block Min-max Bilevel Optimization with Applications in Multi-task Deep AUC Maximization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Multi-block-Single-probe Variance Reduced Estimator for Coupled Compositional Optimization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Multi-dataset Training of Transformers for Robust Action Recognition |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Multi-fidelity Monte Carlo: a pseudo-marginal approach |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Multi-layer State Evolution Under Random Convolutional Design |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multi-modal Grouping Network for Weakly-Supervised Audio-Visual Video Parsing |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multi-objective Deep Data Generation with Correlated Property Control |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multi-view Subspace Clustering on Topological Manifold |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| MultiGuard: Provably Robust Multi-label Classification against Adversarial Examples |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| MultiScan: Scalable RGBD scanning for 3D environments with articulated objects |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Multiagent Q-learning with Sub-Team Coordination |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Multiclass Learnability Beyond the PAC Framework: Universal Rates and Partial Concept Classes |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Multimodal Contrastive Learning with LIMoE: the Language-Image Mixture of Experts |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Multitasking Models are Robust to Structural Failure: A Neural Model for Bilingual Cognitive Reserve |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Multivariate Time-Series Forecasting with Temporal Polynomial Graph Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Multiview Human Body Reconstruction from Uncalibrated Cameras |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Museformer: Transformer with Fine- and Coarse-Grained Attention for Music Generation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Mutual Information Divergence: A Unified Metric for Multimodal Generative Models |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| M³ViT: Mixture-of-Experts Vision Transformer for Efficient Multi-task Learning with Model-Accelerator Co-design |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
4 |
| NCP: Neural Correspondence Prior for Effective Unsupervised Shape Matching |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| NOMAD: Nonlinear Manifold Decoders for Operator Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| NOTE: Robust Continual Test-time Adaptation Against Temporal Correlation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| NS3: Neuro-symbolic Semantic Code Search |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| NSNet: A General Neural Probabilistic Framework for Satisfiability Problems |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| NUWA-Infinity: Autoregressive over Autoregressive Generation for Infinite Visual Synthesis |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Natural Color Fool: Towards Boosting Black-box Unrestricted Attacks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Natural gradient enables fast sampling in spiking neural networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Natural image synthesis for the retina with variational information bottleneck representation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| NaturalProver: Grounded Mathematical Proof Generation with Language Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Navigating Memory Construction by Global Pseudo-Task Simulation for Continual Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| NeMF: Neural Motion Fields for Kinematic Animation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Near Instance-Optimal PAC Reinforcement Learning for Deterministic MDPs |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Near-Isometric Properties of Kronecker-Structured Random Tensor Embeddings |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Near-Optimal Collaborative Learning in Bandits |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
❌ |
4 |
| Near-Optimal Correlation Clustering with Privacy |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Near-Optimal Goal-Oriented Reinforcement Learning in Non-Stationary Environments |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Near-Optimal Multi-Agent Learning for Safe Coverage Control |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Near-Optimal No-Regret Learning Dynamics for General Convex Games |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Near-Optimal Private and Scalable $k$-Clustering |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Near-Optimal Randomized Exploration for Tabular Markov Decision Processes |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Near-Optimal Regret Bounds for Multi-batch Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Near-Optimal Regret for Adversarial MDP with Delayed Bandit Feedback |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Near-Optimal Sample Complexity Bounds for Constrained MDPs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Nearly Optimal Algorithms for Linear Contextual Bandits with Adversarial Corruptions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Nearly Optimal Best-of-Both-Worlds Algorithms for Online Learning with Feedback Graphs |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Nearly-Tight Bounds for Testing Histogram Distributions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Nest Your Adaptive Algorithm for Parameter-Agnostic Nonconvex Minimax Optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Network change point localisation under local differential privacy |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| NeuForm: Adaptive Overfitting for Neural Shape Editing |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| NeuPhysics: Editable Neural Geometry and Physics from Monocular Videos |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Neur2SP: Neural Two-Stage Stochastic Programming |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
4 |
| NeurOLight: A Physics-Agnostic Neural Operator Enabling Parametric Photonic Device Simulation |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
5 |
| Neural Abstractions |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Neural Approximation of Graph Topological Features |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Neural Attentive Circuits |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Neural Basis Models for Interpretability |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Neural Circuit Architectural Priors for Embodied Control |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Neural Collapse with Normalized Features: A Geometric Analysis over the Riemannian Manifold |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Neural Conservation Laws: A Divergence-Free Perspective |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Neural Differential Equations for Learning to Program Neural Nets Through Continuous Learning Rules |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Neural Estimation of Submodular Functions with Applications to Differentiable Subset Selection |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Neural Lyapunov Control of Unknown Nonlinear Systems with Stability Guarantees |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Neural Matching Fields: Implicit Representation of Matching Fields for Visual Correspondence |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Neural Network Architecture Beyond Width and Depth |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Neural Payoff Machines: Predicting Fair and Stable Payoff Allocations Among Team Members |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Neural Set Function Extensions: Learning with Discrete Functions in High Dimensions |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Neural Shape Deformation Priors |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Neural Sheaf Diffusion: A Topological Perspective on Heterophily and Oversmoothing in GNNs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Neural Stochastic Control |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Neural Stochastic PDEs: Resolution-Invariant Learning of Continuous Spatiotemporal Dynamics |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
2 |
| Neural Surface Reconstruction of Dynamic Scenes with Monocular RGB-D Camera |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Neural Temporal Walks: Motif-Aware Representation Learning on Continuous-Time Dynamic Graphs |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Neural Topological Ordering for Computation Graphs |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Neural Transmitted Radiance Fields |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Neural-Symbolic Entangled Framework for Complex Query Answering |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| NeuroSchedule: A Novel Effective GNN-based Scheduling Method for High-level Synthesis |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Neuron with Steady Response Leads to Better Generalization |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Neurosymbolic Deep Generative Models for Sequence Data with Relational Constraints |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| New Definitions and Evaluations for Saliency Methods: Staying Intrinsic, Complete and Sound |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| New Lower Bounds for Private Estimation and a Generalized Fingerprinting Lemma |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| No Free Lunch from Deep Learning in Neuroscience: A Case Study through Models of the Entorhinal-Hippocampal Circuit |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| No-regret learning in games with noisy feedback: Faster rates and adaptivity via learning rate separation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| NodeFormer: A Scalable Graph Structure Learning Transformer for Node Classification |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Noise Attention Learning: Enhancing Noise Robustness by Gradient Scaling |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Non-Convex Bilevel Games with Critical Point Selection Maps |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Non-Gaussian Tensor Programs |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Non-Linear Coordination Graphs |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Non-Linguistic Supervision for Contrastive Learning of Sentence Embeddings |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Non-Markovian Reward Modelling from Trajectory Labels via Interpretable Multiple Instance Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Non-Monotonic Latent Alignments for CTC-Based Non-Autoregressive Machine Translation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Non-Stationary Bandits under Recharging Payoffs: Improved Planning with Sublinear Regret |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Non-convex online learning via algorithmic equivalence |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Non-deep Networks |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Non-identifiability and the Blessings of Misspecification in Models of Molecular Fitness |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Non-monotonic Resource Utilization in the Bandits with Knapsacks Problem |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Non-rigid Point Cloud Registration with Neural Deformation Pyramid |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Non-stationary Bandits with Knapsacks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Non-stationary Transformers: Exploring the Stationarity in Time Series Forecasting |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Nonlinear MCMC for Bayesian Machine Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Nonlinear Sufficient Dimension Reduction with a Stochastic Neural Network |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Nonnegative Tensor Completion via Integer Optimization |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
5 |
| Nonparametric Uncertainty Quantification for Single Deterministic Neural Network |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Nonstationary Dual Averaging and Online Fair Allocation |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
5 |
| Normalizing Flows for Knockoff-free Controlled Feature Selection |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Not All Bits have Equal Value: Heterogeneous Precisions via Trainable Noise |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Not too little, not too much: a theoretical analysis of graph (over)smoothing |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| OGC: Unsupervised 3D Object Segmentation from Rigid Dynamics of Point Clouds |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| OOD Link Prediction Generalization Capabilities of Message-Passing GNNs in Larger Test Graphs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| OPEN: Orthogonal Propagation with Ego-Network Modeling |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| ORIENT: Submodular Mutual Information Measures for Data Subset Selection under Distribution Shift |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| OST: Improving Generalization of DeepFake Detection via One-Shot Test-Time Training |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| OTKGE: Multi-modal Knowledge Graph Embeddings via Optimal Transport |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Obj2Seq: Formatting Objects as Sequences with Class Prompt for Visual Tasks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Object Representations as Fixed Points: Training Iterative Refinement Algorithms with Implicit Differentiation |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Object Scene Representation Transformer |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
2 |
| Object-Category Aware Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Off-Policy Evaluation for Action-Dependent Non-stationary Environments |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Off-Policy Evaluation for Episodic Partially Observable Markov Decision Processes under Non-Parametric Models |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
4 |
| Off-Policy Evaluation with Deficient Support Using Side Information |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Off-Policy Evaluation with Policy-Dependent Optimization Response |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Off-Team Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Offline Goal-Conditioned Reinforcement Learning via $f$-Advantage Regression |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Offline Multi-Agent Reinforcement Learning with Knowledge Distillation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Okapi: Generalising Better by Making Statistical Matches Match |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Old can be Gold: Better Gradient Flow can Make Vanilla-GCNs Great Again |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| OmniVL: One Foundation Model for Image-Language and Video-Language Tasks |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| On A Mallows-type Model For (Ranked) Choices |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| On Analyzing Generative and Denoising Capabilities of Diffusion-based Deep Generative Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| On Batch Teaching with Sample Complexity Bounded by VCD |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On Computing Probabilistic Explanations for Decision Trees |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| On Convergence of FedProx: Local Dissimilarity Invariant Bounds, Non-smoothness and Beyond |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On Deep Generative Models for Approximation and Estimation of Distributions on Manifolds |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On Divergence Measures for Bayesian Pseudocoresets |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| On Efficient Online Imitation Learning via Classification |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On Elimination Strategies for Bandit Fixed-Confidence Identification |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| On Embeddings for Numerical Features in Tabular Deep Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On Enforcing Better Conditioned Meta-Learning for Rapid Few-Shot Adaptation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| On Feature Learning in the Presence of Spurious Correlations |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| On Gap-dependent Bounds for Offline Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On Image Segmentation With Noisy Labels: Characterization and Volume Properties of the Optimal Solutions to Accuracy and Dice |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| On Infinite Separations Between Simple and Optimal Mechanisms |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On Kernelized Multi-Armed Bandits with Constraints |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| On Learning Fairness and Accuracy on Multiple Subgroups |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| On Learning and Refutation in Noninteractive Local Differential Privacy |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On Leave-One-Out Conditional Mutual Information For Generalization |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| On Margin Maximization in Linear and ReLU Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On Margins and Generalisation for Voting Classifiers |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| On Measuring Excess Capacity in Neural Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| On Non-Linear operators for Geometric Deep Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On Optimal Learning Under Targeted Data Poisoning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On Privacy and Personalization in Cross-Silo Federated Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| On Reinforcement Learning and Distribution Matching for Fine-Tuning Language Models with no Catastrophic Forgetting |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| On Robust Multiclass Learnability |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On Sample Optimality in Personalized Collaborative and Federated Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| On Scalable Testing of Samplers |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| On Scrambling Phenomena for Randomly Initialized Recurrent Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On Translation and Reconstruction Guarantees of the Cycle-Consistent Generative Adversarial Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On Uncertainty, Tempering, and Data Augmentation in Bayesian Classification |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| On global convergence of ResNets: From finite to infinite width using linear parameterization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| On the Adversarial Robustness of Mixture of Experts |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On the Complexity of Adversarial Decision Making |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On the Convergence Theory for Hessian-Free Bilevel Algorithms |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| On the Convergence of Stochastic Multi-Objective Gradient Manipulation and Beyond |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Discrimination Risk of Mean Aggregation Feature Imputation in Graphs |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| On the Double Descent of Random Features Models Trained with SGD |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On the Effect of Pre-training for Transformer in Different Modality on Offline Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Effective Number of Linear Regions in Shallow Univariate ReLU Networks: Convergence Guarantees and Implicit Bias |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Effectiveness of Fine-tuning Versus Meta-reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| On the Effectiveness of Lipschitz-Driven Rehearsal in Continual Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| On the Effectiveness of Persistent Homology |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| On the Efficient Implementation of High Accuracy Optimality of Profile Maximum Likelihood |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On the Epistemic Limits of Personalized Prediction |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| On the Frequency-bias of Coordinate-MLPs |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On the Generalizability and Predictability of Recommender Systems |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| On the Generalization Power of the Overfitted Three-Layer Neural Tangent Kernel Model |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| On the Global Convergence Rates of Decentralized Softmax Gradient Play in Markov Potential Games |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| On the Identifiability of Nonlinear ICA: Sparsity and Beyond |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| On the Importance of Gradient Norm in PAC-Bayesian Bounds |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Interpretability of Regularisation for Neural Networks Through Model Gradient Similarity |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On the Learning Mechanisms in Physical Reasoning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Limitations of Stochastic Pre-processing Defenses |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| On the Parameterization and Initialization of Diagonal State Space Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Representation Collapse of Sparse Mixture of Experts |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| On the Robustness of Deep Clustering Models: Adversarial Attacks and Defenses |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Robustness of Graph Neural Diffusion to Topology Perturbations |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| On the SDEs and Scaling Rules for Adaptive Gradient Algorithms |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| On the Safety of Interpretable Machine Learning: A Maximum Deviation Approach |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| On the Sample Complexity of Stabilizing LTI Systems on a Single Trajectory |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On the Spectral Bias of Convolutional Neural Tangent and Gaussian Process Kernels |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Stability and Scalability of Node Perturbation Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| On the Statistical Efficiency of Reward-Free Exploration in Non-Linear RL |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On the Strong Correlation Between Model Invariance and Generalization |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| On the Symmetries of Deep Learning Models and their Internal Representations |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| On the Theoretical Properties of Noise Correlation in Stochastic Optimization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| On the Tradeoff Between Robustness and Fairness |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the consistent estimation of optimal Receiver Operating Characteristic (ROC) curve |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On the convergence of policy gradient methods to Nash equilibria in general stochastic games |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On the detrimental effect of invariances in the likelihood for variational inference |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the difficulty of learning chaotic dynamics with RNNs |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the generalization of learning algorithms that do not converge |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the inability of Gaussian process regression to optimally learn compositional functions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the non-universality of deep learning: quantifying the cost of symmetry |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the relationship between variational inference and auto-associative memory |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| On the role of overparameterization in off-policy Temporal Difference learning with linear function approximation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the symmetries of the synchronization problem in Cryo-EM: Multi-Frequency Vector Diffusion Maps on the Projective Plane |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On-Demand Sampling: Learning Optimally from Multiple Distributions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On-Device Training Under 256KB Memory |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| One Model to Edit Them All: Free-Form Text-Driven Image Manipulation with Semantic Modulations |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| One Positive Label is Sufficient: Single-Positive Multi-Label Learning with Label Enhancement |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| One for All: Simultaneous Metric and Preference Learning over Multiple Users |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| One-Inlier is First: Towards Efficient Position Encoding for Point Cloud Registration |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| One-shot Neural Backdoor Erasing via Adversarial Weight Masking |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| OnePose++: Keypoint-Free One-Shot Object Pose Estimation without CAD Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Online Agnostic Multiclass Boosting |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Online Algorithms for the Santa Claus Problem |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Online Allocation and Learning in the Presence of Strategic Agents |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Online Bipartite Matching with Advice: Tight Robustness-Consistency Tradeoffs for the Two-Stage Model |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Online Convex Optimization with Hard Constraints: Towards the Best of Two Worlds and Beyond |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Online Decision Mediation |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Online Deep Equilibrium Learning for Regularization by Denoising |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Online Frank-Wolfe with Arbitrary Delays |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
5 |
| Online Learning and Pricing for Network Revenue Management with Reusable Resources |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Online Minimax Multiobjective Optimization: Multicalibeating and Other Applications |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Online Neural Sequence Detection with Hierarchical Dirichlet Point Process |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Online PAC-Bayes Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Online Reinforcement Learning for Mixed Policy Scopes |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Online Training Through Time for Spiking Neural Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Open-Ended Reinforcement Learning with Neural Reward Functions |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| OpenAUC: Towards AUC-Oriented Open-Set Recognition |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Operative dimensions in unconstrained connectivity of recurrent neural networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Operator Splitting Value Iteration |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Optimal Algorithms for Decentralized Stochastic Variational Inequalities |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Optimal Binary Classification Beyond Accuracy |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Optimal Brain Compression: A Framework for Accurate Post-Training Quantization and Pruning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Optimal Comparator Adaptive Online Learning with Switching Cost |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Optimal Dynamic Regret in LQR Control |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimal Efficiency-Envy Trade-Off via Optimal Transport |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Optimal Gradient Sliding and its Application to Optimal Distributed Optimization Under Similarity |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Optimal Positive Generation via Latent Transformation for Contrastive Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Optimal Query Complexities for Dynamic Trace Estimation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Optimal Rates for Regularized Conditional Mean Embedding Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Optimal Scaling for Locally Balanced Proposals in Discrete Spaces |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Optimal Transport of Classifiers to Fairness |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Optimal Transport-based Identity Matching for Identity-invariant Facial Expression Recognition |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Optimal Weak to Strong Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimal and Adaptive Monteiro-Svaiter Acceleration |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Optimal-er Auctions through Attention |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimistic Mirror Descent Either Converges to Nash or to Strong Coarse Correlated Equilibria in Bimatrix Games |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Optimistic Posterior Sampling for Reinforcement Learning with Few Samples and Tight Guarantees |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Optimistic Tree Searches for Combinatorial Black-Box Optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Optimizing Data Collection for Machine Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Optimizing Relevance Maps of Vision Transformers Improves Robustness |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Oracle Inequalities for Model Selection in Offline Reinforcement Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Oracle-Efficient Online Learning for Smoothed Adversaries |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Order-Invariant Cardinality Estimators Are Differentially Private |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Ordered Subgraph Aggregation Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| OrdinalCLIP: Learning Rank Prompts for Language-Guided Ordinal Regression |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Orthogonal Transformer: An Efficient Vision Transformer Backbone with Token Orthogonalization |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Oscillatory Tracking of Continuous Attractor Neural Networks Account for Phase Precession and Procession of Hippocampal Place Cells |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Out-of-Distribution Detection via Conditional Kernel Independence Model |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Out-of-Distribution Detection with An Adaptive Likelihood Ratio on Informative Hierarchical VAE |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Outlier Suppression: Pushing the Limit of Low-bit Transformer Language Models |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Outlier-Robust Sparse Estimation via Non-Convex Optimization |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Outlier-Robust Sparse Mean Estimation for Heavy-Tailed Distributions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Outsourcing Training without Uploading Data via Efficient Collaborative Open-Source Sampling |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Overparameterization from Computational Constraints |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| P2P: Tuning Pre-trained Image Models for Point Cloud Analysis with Point-to-Pixel Prompting |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| PAC Prediction Sets for Meta-Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| PAC-Bayes Compression Bounds So Tight That They Can Explain Generalization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| PAC: Assisted Value Factorization with Counterfactual Predictions in Multi-Agent Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| PALBERT: Teaching ALBERT to Ponder |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| PALMER: Perception - Action Loop with Memory for Long-Horizon Planning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| PDSketch: Integrated Domain Programming, Learning, and Planning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| PKD: General Distillation Framework for Object Detectors via Pearson Correlation Coefficient |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| PaCo: Parameter-Compositional Multi-task Reinforcement Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Palm up: Playing in the Latent Manifold for Unsupervised Pretraining |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Panchromatic and Multispectral Image Fusion via Alternating Reverse Filtering Network |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Para-CFlows: $C^k$-universal diffeomorphism approximators as superior neural surrogates |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Parallel Tempering With a Variational Reference |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Parameter tuning and model selection in Optimal Transport with semi-dual Brenier formulation |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Parameter-Efficient Masking Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Parameter-free Dynamic Graph Embedding for Link Prediction |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Parameter-free Regret in High Probability with Heavy Tails |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Parameters or Privacy: A Provable Tradeoff Between Overparameterization and Membership Inference |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Parametrically Retargetable Decision-Makers Tend To Seek Power |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Paraphrasing Is All You Need for Novel Object Captioning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Pareto Set Learning for Expensive Multi-Objective Optimization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Partial Identification of Treatment Effects with Implicit Generative Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| PatchComplete: Learning Multi-Resolution Patch Priors for 3D Shape Completion on Unseen Categories |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Patching open-vocabulary models by interpolating weights |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Path Independent Equilibrium Models Can Better Exploit Test-Time Computation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Pay attention to your loss : understanding misconceptions about Lipschitz neural networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Peer Prediction for Learning Agents |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Perceptual Attacks of No-Reference Image Quality Models with Human-in-the-Loop |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Perfect Sampling from Pairwise Comparisons |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| PerfectDou: Dominating DouDizhu with Perfect Information Distillation |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Performative Power |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Periodic Graph Transformers for Crystal Material Property Prediction |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Peripheral Vision Transformer |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Personalized Federated Learning towards Communication Efficiency, Robustness and Fairness |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Personalized Online Federated Learning with Multiple Kernels |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Perturbation Learning Based Anomaly Detection |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
4 |
| Pessimism for Offline Linear Contextual Bandits using $\ell_p$ Confidence Sets |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Phase Transition from Clean Training to Adversarial Training |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Phase diagram of Stochastic Gradient Descent in high-dimensional two-layer neural networks |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Phase transitions in when feedback is useful |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Photorealistic Text-to-Image Diffusion Models with Deep Language Understanding |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| PhysGNN: A Physics--Driven Graph Neural Network Based Model for Predicting Soft Tissue Deformation in Image--Guided Neurosurgery |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Physically-Based Face Rendering for NIR-VIS Face Recognition |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Physics-Embedded Neural Networks: Graph Neural PDE Solvers with Mixed Boundary Conditions |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Physics-Informed Implicit Representations of Equilibrium Network Flows |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Picking on the Same Person: Does Algorithmic Monoculture lead to Outcome Homogenization? |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Pitfalls of Epistemic Uncertainty Quantification through Loss Minimisation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Plan To Predict: Learning an Uncertainty-Foreseeing Model For Model-Based Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Planning for Sample Efficient Imitation Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Planning to the Information Horizon of BAMDPs via Epistemic State Abstraction |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| PlasticityNet: Learning to Simulate Metal, Sand, and Snow for Optimization Time Integration |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Pluralistic Image Completion with Gaussian Mixture Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Point Transformer V2: Grouped Vector Attention and Partition-based Pooling |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Point-M2AE: Multi-scale Masked Autoencoders for Hierarchical Point Cloud Pre-training |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| PointNeXt: Revisiting PointNet++ with Improved Training and Scaling Strategies |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| PointTAD: Multi-Label Temporal Action Detection with Learnable Query Points |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Poisson Flow Generative Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| PolarMix: A General Data Augmentation Technique for LiDAR Point Clouds |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Policy Gradient With Serial Markov Chain Reasoning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Policy Optimization for Markov Games: Unified Framework and Faster Convergence |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Policy Optimization with Advantage Regularization for Long-Term Fairness in Decision Systems |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Policy Optimization with Linear Temporal Logic Constraints |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Polyhistor: Parameter-Efficient Multi-Task Adaptation for Dense Vision Tasks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Polynomial Neural Fields for Subband Decomposition and Manipulation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Polynomial time guarantees for the Burer-Monteiro method |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Polynomial-Time Optimal Equilibria with a Mediator in Extensive-Form Games |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
4 |
| PopArt: Efficient Sparse Regression and Experimental Design for Optimal Sparse Linear Bandits |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Positive-Unlabeled Learning using Random Forests via Recursive Greedy Risk Minimization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Positively Weighted Kernel Quadrature via Subsampling |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Post-hoc estimators for learning to defer to an expert |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Posted Pricing and Dynamic Prior-independent Mechanisms with Value Maximizers |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Posterior Collapse of a Linear Latent Variable Model |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Posterior Matching for Arbitrary Conditioning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Posterior Refinement Improves Sample Efficiency in Bayesian Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Posterior and Computational Uncertainty in Gaussian Processes |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
5 |
| Power and limitations of single-qubit native quantum neural networks |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Practical Adversarial Attacks on Spatiotemporal Traffic Forecasting Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Practical Adversarial Multivalid Conformal Prediction |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Pragmatically Learning from Pedagogical Demonstrations in Multi-Goal Environments |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Pre-Train Your Loss: Easy Bayesian Transfer Learning with Informative Priors |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Pre-Trained Image Encoder for Generalizable Visual Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Pre-Trained Language Models for Interactive Decision-Making |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Pre-Trained Model Reusability Evaluation for Small-Data Transfer Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Pre-activation Distributions Expose Backdoor Neurons |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Pre-trained Adversarial Perturbations |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Precise Learning Curves and Higher-Order Scalings for Dot-product Kernel Regression |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Precise Regret Bounds for Log-loss via a Truncated Bayesian Algorithm |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Predicting Cellular Responses to Novel Drug Perturbations at a Single-Cell Resolution |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Predicting Label Distribution from Multi-label Ranking |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Predictive Coding beyond Gaussian Distributions |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Predictive Querying for Autoregressive Neural Sequence Models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Preservation of the Global Knowledge by Not-True Distillation in Federated Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Privacy Induces Robustness: Information-Computation Gaps and Sparse Mean Estimation |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Privacy of Noisy Stochastic Gradient Descent: More Iterations without More Privacy Loss |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Private Estimation with Public Data |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Private Graph All-Pairwise-Shortest-Path Distance Release with Improved Error Rate |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Private Isotonic Regression |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Private Multiparty Perception for Navigation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Private Set Generation with Discriminative Information |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Private Synthetic Data for Multitask Learning and Marginal Queries |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Private and Communication-Efficient Algorithms for Entropy Estimation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Probabilistic Missing Value Imputation for Mixed Categorical and Ordered Data |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Probabilistic Transformer: Modelling Ambiguities and Distributions for RNA Folding and Molecule Design |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Probable Domain Generalization via Quantile Risk Minimization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Probing Classifiers are Unreliable for Concept Removal and Detection |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Procedural Image Programs for Representation Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Product Ranking for Revenue Maximization with Multiple Purchases |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Promising or Elusive? Unsupervised Object Segmentation from Real-world Single Images |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Prompt Certified Machine Unlearning with Randomized Gradient Smoothing and Quantization |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Proppo: a Message Passing Framework for Customizable and Composable Learning Algorithms |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| ProtoVAE: A Trustworthy Self-Explainable Prototypical Variational Model |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| ProtoX: Explaining a Reinforcement Learning Agent via Prototyping |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| Prototypical VoteNet for Few-Shot 3D Point Cloud Object Detection |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Provable Benefit of Multitask Representation Learning in Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Provable Defense against Backdoor Policies in Reinforcement Learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Provable General Function Class Representation Learning in Multitask Bandits and MDP |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Provable Generalization of Overparameterized Meta-learning Trained with SGD |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Provable Subspace Identification Under Post-Nonlinear Mixtures |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Provably Adversarially Robust Detection of Out-of-Distribution Data (Almost) for Free |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Provably Efficient Model-Free Constrained RL with Linear Function Approximation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Provably Efficient Offline Multi-agent Reinforcement Learning via Strategy-wise Bonus |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Provably Efficient Reinforcement Learning in Partially Observable Dynamical Systems |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Provably Feedback-Efficient Reinforcement Learning via Active Reward Learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Provably expressive temporal graph networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Provably sample-efficient RL with side information about latent dynamics |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Provably tuning the ElasticNet across instances |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Proximal Learning With Opponent-Learning Awareness |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Proximal Point Imitation Learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Prune and distill: similar reformatting of image information along rat visual cortex and deep neural networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Pruning Neural Networks via Coresets and Convex Geometry: Towards No Assumptions |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Pruning has a disparate impact on model accuracy |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Pruning’s Effect on Generalization Through the Lens of Training and Regularization |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Pseudo-Riemannian Graph Convolutional Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Public Wisdom Matters! Discourse-Aware Hyperbolic Fourier Co-Attention for Social Text Classification |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Pure Transformers are Powerful Graph Learners |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Pushing the limits of fairness impossibility: Who's the fairest of them all? |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
2 |
| Pyramid Attention For Source Code Summarization |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| PyramidCLIP: Hierarchical Feature Alignment for Vision-language Model Pretraining |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Q-ViT: Accurate and Fully Quantized Low-bit Vision Transformer |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| QC-StyleGAN - Quality Controllable Image Generation and Manipulation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| QUARK: Controllable Text Generation with Reinforced Unlearning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Quality Not Quantity: On the Interaction between Dataset Design and Robustness of CLIP |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Quantifying Statistical Significance of Neural Network-based Image Segmentation by Selective Inference |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Quantile Constrained Reinforcement Learning: A Reinforcement Learning Framework Constraining Outage Probability |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Quantized Training of Gradient Boosting Decision Trees |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Quantum Algorithms for Sampling Log-Concave Distributions and Estimating Normalizing Constants |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Quantum Speedups of Optimizing Approximately Convex Functions with Applications to Logarithmic Regret Stochastic Convex Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Quasi-Newton Methods for Saddle Point Problems |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| QueryPose: Sparse Multi-Person Pose Regression via Spatial-Aware Part-Level Query |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Queue Up Your Regrets: Achieving the Dynamic Capacity Region of Multiplayer Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Quo Vadis: Is Trajectory Forecasting the Key Towards Long-Term Multi-Object Tracking? |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| RAMBO-RL: Robust Adversarial Model-Based Offline Reinforcement Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| REVIVE: Regional Visual Representation Matters in Knowledge-Based Visual Question Answering |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| RISE: Robust Individualized Decision Learning with Sensitive Variables |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| RKHS-SHAP: Shapley Values for Kernel Methods |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| RLIP: Relational Language-Image Pre-training for Human-Object Interaction Detection |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| RNNs of RNNs: Recursive Construction of Stable Assemblies of Recurrent Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| RORL: Robust Offline Reinforcement Learning via Conservative Smoothing |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| RSA: Reducing Semantic Shift from Aggressive Augmentations for Self-supervised Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| RTFormer: Efficient Design for Real-Time Semantic Segmentation with Transformer |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| RainNet: A Large-Scale Imagery Dataset and Benchmark for Spatial Precipitation Downscaling |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Random Normalization Aggregation for Adversarial Defense |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Random Rank: The One and Only Strategyproof and Proportionally Fair Randomized Facility Location Mechanism |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Random Sharpness-Aware Minimization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Randomized Channel Shuffling: Minimal-Overhead Backdoor Attack Detection without Clean Datasets |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Randomized Message-Interception Smoothing: Gray-box Certificates for Graph Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Randomized Sketches for Clustering: Fast and Optimal Kernel $k$-Means |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Rank Diminishing in Deep Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| RankFeat: Rank-1 Feature Removal for Out-of-distribution Detection |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Rapid Model Architecture Adaption for Meta-Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Rapidly Mixing Multiple-try Metropolis Algorithms for Model Selection Problems |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Rare Gems: Finding Lottery Tickets at Initialization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Rashomon Capacity: A Metric for Predictive Multiplicity in Classification |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Rate-Distortion Theoretic Bounds on Generalization Error for Distributed Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Rate-Optimal Online Convex Optimization in Adaptive Linear Control |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Re-Analyze Gauss: Bounds for Private Matrix Approximation via Dyson Brownian Motion |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| ReCo: Retrieve and Co-segment for Zero-shot Transfer |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| ReFactor GNNs: Revisiting Factorisation-based Models from a Message-Passing Perspective |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Real-Valued Backpropagation is Unsuitable for Complex-Valued Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Recall Distortion in Neural Network Pruning and the Undecayed Pruning Algorithm |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Receding Horizon Inverse Reinforcement Learning |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Recipe for a General, Powerful, Scalable Graph Transformer |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Recommender Forest for Efficient Retrieval |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Reconstructing Training Data From Trained Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Reconstruction on Trees and Low-Degree Polynomials |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Recovering Private Text in Federated Learning of Language Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Recruitment Strategies That Take a Chance |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Recurrent Convolutional Neural Networks Learn Succinct Learning Algorithms |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Recurrent Memory Transformer |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Recurrent Video Restoration Transformer with Guided Deformable Attention |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Recursive Reasoning in Minimax Games: A Level $k$ Gradient Play Method |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Recursive Reinforcement Learning |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
3 |
| RecursiveMix: Mixed Learning with History |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Redeeming intrinsic rewards via constrained optimization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Redistribution of Weights and Activations for AdderNet Quantization |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Reduced Representation of Deformation Fields for Effective Non-rigid Shape Matching |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Reduction Algorithms for Persistence Diagrams of Networks: CoralTDA and PrunIT |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| Redundancy-Free Message Passing for Graph Neural Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Redundant representations help generalization in wide neural networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Refining Low-Resource Unsupervised Translation by Language Disentanglement of Multilingual Translation Model |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Regret Bounds for Information-Directed Reinforcement Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Regret Bounds for Multilabel Classification in Sparse Label Regimes |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Regret Bounds for Risk-Sensitive Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Regularized Gradient Descent Ascent for Two-Player Zero-Sum Markov Games |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Regularized Molecular Conformation Fields |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Reincarnating Reinforcement Learning: Reusing Prior Computation to Accelerate Progress |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Reinforced Genetic Algorithm for Structure-based Drug Design |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Reinforcement Learning in a Birth and Death Process: Breaking the Dependence on the State Space |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Reinforcement Learning with Automated Auxiliary Loss Search |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Reinforcement Learning with Logarithmic Regret and Policy Switches |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Reinforcement Learning with Neural Radiance Fields |
❌ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Reinforcement Learning with Non-Exponential Discounting |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Reinforcement Learning with a Terminator |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Relation-Constrained Decoding for Text Generation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Relational Proxies: Emergent Relationships as Fine-Grained Discriminators |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Relational Reasoning via Set Transformers: Provable Efficiency and Applications to MARL |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Relaxing Equivariance Constraints with Non-stationary Continuous Filters |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Remember the Past: Distilling Datasets into Addressable Memories for Neural Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Renyi Differential Privacy of Propose-Test-Release and Applications to Private and Robust Machine Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Repairing Neural Networks by Leaving the Right Past Behind |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Representing Spatial Trajectories as Distributions |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Reproducibility in Optimization: Theoretical Framework and Limits |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| ResQ: A Residual Q Function-based Approach for Multi-Agent Reinforcement Learning Value Factorization |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| ResT V2: Simpler, Faster and Stronger |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Residual Multiplicative Filter Networks for Multiscale Reconstruction |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Resolving the data ambiguity for periodic crystals |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Resource-Adaptive Federated Learning with All-In-One Neural Composition |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Respecting Transfer Gap in Knowledge Distillation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Retaining Knowledge for Learning with Dynamic Definition |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| Rethinking Alignment in Video Super-Resolution Transformers |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Rethinking Generalization in Few-Shot Classification |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Rethinking Image Restoration for Object Detection |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Rethinking Individual Global Max in Cooperative Multi-Agent Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Rethinking Knowledge Graph Evaluation Under the Open-World Assumption |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
4 |
| Rethinking Lipschitz Neural Networks and Certified Robustness: A Boolean Function Perspective |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Rethinking Resolution in the Context of Efficient Video Recognition |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Rethinking Value Function Learning for Generalization in Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Rethinking Variational Inference for Probabilistic Programs with Stochastic Support |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Rethinking and Improving Robustness of Convolutional Neural Networks: a Shapley Value-based Approach in Frequency Domain |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Rethinking and Scaling Up Graph Contrastive Learning: An Extremely Efficient Approach with Group Discrimination |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
4 |
| Rethinking the Reverse-engineering of Trojan Triggers |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Rethinking the compositionality of point clouds through regularization in the hyperbolic space |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Retrieval-Augmented Diffusion Models |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Retrieve, Reason, and Refine: Generating Accurate and Faithful Patient Instructions |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Retrospective Adversarial Replay for Continual Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Revisit last-iterate convergence of mSGD under milder requirement on step size |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Revisiting Active Sets for Gaussian Process Decoders |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Revisiting Graph Contrastive Learning from the Perspective of Graph Spectrum |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Revisiting Heterophily For Graph Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Revisiting Injective Attacks on Recommender Systems |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Revisiting Neural Scaling Laws in Language and Vision |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Revisiting Non-Parametric Matching Cost Volumes for Robust and Generalizable Stereo Matching |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Revisiting Optimal Convergence Rate for Smooth and Non-convex Stochastic Decentralized Optimization |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Revisiting Realistic Test-Time Training: Sequential Inference and Adaptation by Anchored Clustering |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Revisiting Sliced Wasserstein on Images: From Vectorization to Convolution |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Revisiting Sparse Convolutional Model for Visual Recognition |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Riemannian Diffusion Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Riemannian Neural SDE: Learning Stochastic Representations on Manifolds |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Riemannian Score-Based Generative Modelling |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Risk Bounds of Multi-Pass SGD for Least Squares in the Interpolation Regime |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Risk-Driven Design of Perception Systems |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Roadblocks for Temporarily Disabling Shortcuts and Learning New Knowledge |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Robust $\phi$-Divergence MDPs |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
5 |
| Robust Anytime Learning of Markov Decision Processes |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Robust Bayesian Regression via Hard Thresholding |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Robust Binary Models by Pruning Randomly-initialized Networks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Robust Calibration with Multi-domain Temperature Scaling |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Robust Feature-Level Adversaries are Interpretability Tools |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Robust Generalized Method of Moments: A Finite Sample Viewpoint |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Robust Graph Structure Learning via Multiple Statistical Tests |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Robust Imitation of a Few Demonstrations with a Backwards Model |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Robust Imitation via Mirror Descent Inverse Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Robust Learning against Relational Adversaries |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Robust Model Selection and Nearly-Proper Learning for GMMs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Robust Models are less Over-Confident |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Robust Neural Posterior Estimation and Statistical Model Criticism |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Robust On-Policy Sampling for Data-Efficient Policy Evaluation in Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Robust Reinforcement Learning using Offline Data |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Robust Rent Division |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Robust Semi-Supervised Learning when Not All Classes have Labels |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Robust Streaming PCA |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Robust Testing in High-Dimensional Sparse Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Robustness in deep learning: The good (width), the bad (depth), and the ugly (initialization) |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Robustness to Label Noise Depends on the Shape of the Noise Distribution |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Robustness to Unbounded Smoothness of Generalized SignSGD |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Root Cause Analysis of Failures in Microservices through Causal Discovery |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
4 |
| Rotation-Equivariant Conditional Spherical Neural Fields for Learning a Natural Illumination Prior |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| RényiCL: Contrastive Representation Learning with Skew Rényi Divergence |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| S$^3$-NeRF: Neural Reflectance Field from Shading and Shadow under a Single Viewpoint |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| S-PIFu: Integrating Parametric Human Models with PIFu for Single-view Clothed Human Reconstruction |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| S-Prompts Learning with Pre-trained Transformers: An Occam’s Razor for Domain Incremental Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| S2P: State-conditioned Image Synthesis for Data Augmentation in Offline Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| S3GC: Scalable Self-Supervised Graph Clustering |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| S4ND: Modeling Images and Videos as Multidimensional Signals with State Spaces |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| SAGDA: Achieving $\mathcal{O}(\epsilon^{-2})$ Communication Complexity in Federated Min-Max Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| SALSA: Attacking Lattice Cryptography with Transformers |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| SAMURAI: Shape And Material from Unconstrained Real-world Arbitrary Image collections |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| SAPA: Similarity-Aware Point Affiliation for Feature Upsampling |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| SAPD+: An Accelerated Stochastic Method for Nonconvex-Concave Minimax Problems |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| SAPipe: Staleness-Aware Pipeline for Data Parallel DNN Training |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| SAVi++: Towards End-to-End Object-Centric Learning from Real-World Videos |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| SAViT: Structure-Aware Vision Transformer Pruning via Collaborative Optimization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| SCINet: Time Series Modeling and Forecasting with Sample Convolution and Interaction |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| SCL-WC: Cross-Slide Contrastive Learning for Weakly-Supervised Whole-Slide Image Classification |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| SCONE: Surface Coverage Optimization in Unknown Environments by Volumetric Integration |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| SGAM: Building a Virtual 3D World through Simultaneous Generation and Mapping |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| SHAQ: Incorporating Shapley Value Theory into Multi-Agent Q-Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| SHINE: SubHypergraph Inductive Neural nEtwork |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| SIREN: Shaping Representations for Detecting Out-of-Distribution Objects |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| SIXO: Smoothing Inference with Twisted Objectives |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| SInGE: Sparsity via Integrated Gradients Estimation of Neuron Relevance |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| SKFlow: Learning Optical Flow with Super Kernels |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| SNAKE: Shape-aware Neural 3D Keypoint Field |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| SNN-RAT: Robustness-enhanced Spiking Neural Network through Regularized Adversarial Training |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| SPD domain-specific batch normalization to crack interpretable unsupervised domain adaptation in EEG |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| SPD: Synergy Pattern Diversifying Oriented Unsupervised Multi-agent Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| SPoVT: Semantic-Prototype Variational Transformer for Dense Point Cloud Semantic Completion |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| SQ Lower Bounds for Learning Single Neurons with Massart Noise |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| ST-Adapter: Parameter-Efficient Image-to-Video Transfer Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| STNDT: Modeling Neural Population Activity with Spatiotemporal Transformers |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| STaR: Bootstrapping Reasoning With Reasoning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Safe Opponent-Exploitation Subgame Refinement |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Safety Guarantees for Neural Network Dynamic Systems via Stochastic Barrier Functions |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| SageMix: Saliency-Guided Mixup for Point Clouds |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Saliency-Aware Neural Architecture Search |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Sample Complexity of Learning Heuristic Functions for Greedy-Best-First and A* Search |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Sample Constrained Treatment Effect Estimation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Sample-Efficient Learning of Correlated Equilibria in Extensive-Form Games |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Sample-Efficient Reinforcement Learning of Partially Observable Markov Games |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Sample-Then-Optimize Batch Neural Thompson Sampling |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Sampling from Log-Concave Distributions with Infinity-Distance Guarantees |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Sampling in Constrained Domains with Orthogonal-Space Variational Gradient Descent |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Sampling with Riemannian Hamiltonian Monte Carlo in a Constrained Space |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Sampling without Replacement Leads to Faster Rates in Finite-Sum Minimax Optimization |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| SatMAE: Pre-training Transformers for Temporal and Multi-Spectral Satellite Imagery |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Scalable Distributional Robustness in a Class of Non-Convex Optimization with Guarantees |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Scalable Infomin Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Scalable Interpretability via Polynomials |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Scalable Multi-agent Covering Option Discovery based on Kronecker Graphs |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Scalable Neural Video Representations with Learnable Positional Features |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Scalable Representation Learning in Linear Contextual Bandits with Constant Regret Guarantees |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Scalable Sensitivity and Uncertainty Analyses for Causal-Effect Estimates of Continuous-Valued Interventions |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Scalable and Efficient Non-adaptive Deterministic Group Testing |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Scalable and Efficient Training of Large Convolutional Neural Networks with Differential Privacy |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Scalable design of Error-Correcting Output Codes using Discrete Optimization with Graph Coloring |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
5 |
| Scale-invariant Learning by Physics Inversion |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
5 |
| Scaling & Shifting Your Features: A New Baseline for Efficient Model Tuning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Scaling Multimodal Pre-Training via Cross-Modality Gradient Harmonization |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Score-Based Diffusion meets Annealed Importance Sampling |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Score-Based Generative Models Detect Manifolds |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Score-based Generative Modeling Secretly Minimizes the Wasserstein Distance |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Searching for Better Spatio-temporal Alignment in Few-Shot Action Recognition |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Second Thoughts are Best: Learning to Re-Align With Human Values from Text Edits |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| SecureFedYJ: a safe feature Gaussianization protocol for Federated Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Seeing the forest and the tree: Building representations of both individual and collective dynamics with transformers |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| SegNeXt: Rethinking Convolutional Attention Design for Semantic Segmentation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| SegViT: Semantic Segmentation with Plain Vision Transformers |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Segmenting Moving Objects via an Object-Centric Layered Representation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| SelecMix: Debiased Learning by Contradicting-pair Sampling |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Selective compression learning of latent representations for variable-rate image compression |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Self-Aware Personalized Federated Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Self-Consistent Dynamical Field Theory of Kernel Evolution in Wide Neural Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Self-Explaining Deviations for Coordination |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Self-Organized Group for Cooperative Multi-agent Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Self-Similarity Priors: Neural Collages as Differentiable Fractal Representations |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Self-Supervised Aggregation of Diverse Experts for Test-Agnostic Long-Tailed Recognition |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Self-Supervised Contrastive Pre-Training For Time Series via Time-Frequency Consistency |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Self-Supervised Fair Representation Learning without Demographics |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Self-Supervised Image Restoration with Blurry and Noisy Pairs |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Self-Supervised Learning Through Efference Copies |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Self-Supervised Learning of Brain Dynamics from Broad Neuroimaging Data |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Self-Supervised Learning via Maximum Entropy Coding |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Self-Supervised Learning with an Information Maximization Criterion |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Self-Supervised Pretraining for Large-Scale Point Clouds |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Self-Supervised Visual Representation Learning with Semantic Grouping |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Self-explaining deep models with logic rule reasoning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Self-supervised Amodal Video Object Segmentation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Self-supervised Heterogeneous Graph Pre-training Based on Structural Clustering |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Self-supervised surround-view depth estimation with volumetric feature fusion |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| SemMAE: Semantic-Guided Masking for Learning Masked Autoencoders |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Semantic Diffusion Network for Semantic Segmentation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Semantic Exploration from Language Abstractions and Pretrained Representations |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Semantic Probabilistic Layers for Neuro-Symbolic Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Semantic uncertainty intervals for disentangled latent spaces |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Semi-Discrete Normalizing Flows through Differentiable Tessellation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Semi-Supervised Generative Models for Multiagent Trajectories |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Semi-Supervised Learning with Decision Trees: Graph Laplacian Tree Alternating Optimization |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Semi-Supervised Semantic Segmentation via Gentle Teaching Assistant |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Semi-Supervised Video Salient Object Detection Based on Uncertainty-Guided Pseudo Labels |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Semi-infinitely Constrained Markov Decision Processes |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
5 |
| Semi-supervised Active Linear Regression |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Semi-supervised Semantic Segmentation with Prototype-based Consistency Regularization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Semi-supervised Vision Transformers at Scale |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| SemiFL: Semi-Supervised Federated Learning for Unlabeled Clients with Alternate Training |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| SeqPATE: Differentially Private Text Generation via Knowledge Distillation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Sequence Model Imitation Learning with Unobserved Contexts |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Sequence-to-Set Generative Models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Sequencer: Deep LSTM for Image Classification |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Sequential Information Design: Learning to Persuade in the Dark |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Set-based Meta-Interpolation for Few-Task Meta-Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Shadow Knowledge Distillation: Bridging Offline and Online Knowledge Transfer |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Shape And Structure Preserving Differential Privacy |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Shape, Light, and Material Decomposition from Images using Monte Carlo Rendering and Denoising |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| ShapeCrafter: A Recursive Text-Conditioned 3D Shape Generation Model |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Sharing Knowledge for Meta-learning with Feature Descriptions |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Sharp Analysis of Stochastic Optimization under Global Kurdyka-Lojasiewicz Inequality |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Sharper Convergence Guarantees for Asynchronous SGD for Distributed and Federated Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Sharpness-Aware Training for Free |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Shield Decentralization for Safe Multi-Agent Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| ShuffleMixer: An Efficient ConvNet for Image Super-Resolution |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| SignRFF: Sign Random Fourier Features |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Signal Processing for Implicit Neural Representations |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Signal Propagation in Transformers: Theoretical Perspectives and the Role of Rank Collapse |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Signal Recovery with Non-Expansive Generative Network Priors |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
2 |
| Simple Mechanisms for Welfare Maximization in Rich Advertising Auctions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Simple Unsupervised Object-Centric Learning for Complex and Naturalistic Videos |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Simple and Optimal Greedy Online Contention Resolution Schemes |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Simplified Graph Convolution with Heterophily |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Simulation-guided Beam Search for Neural Combinatorial Optimization |
✅ |
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✅ |
❌ |
✅ |
✅ |
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6 |
| Simultaneous Missing Value Imputation and Structure Learning with Groups |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Single Loop Gaussian Homotopy Method for Non-convex Optimization |
✅ |
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✅ |
❌ |
✅ |
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4 |
| Single Model Uncertainty Estimation via Stochastic Data Centering |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Single-Stage Visual Relationship Learning using Conditional Queries |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Single-pass Streaming Lower Bounds for Multi-armed Bandits Exploration with Instance-sensitive Sample Complexity |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Single-phase deep learning in cortico-cortical networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Singular Value Fine-tuning: Few-shot Segmentation requires Few-parameters Fine-tuning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Size and depth of monotone neural networks: interpolation and approximation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| SizeShiftReg: a Regularization Method for Improving Size-Generalization in Graph Neural Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Sketch-GNN: Scalable Graph Neural Networks with Sublinear Training Complexity |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| SketchBoost: Fast Gradient Boosted Decision Tree for Multioutput Problems |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
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5 |
| Sketching based Representations for Robust Image Classification with Provable Guarantees |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Skills Regularized Task Decomposition for Multi-task Offline Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
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4 |
| Sleeper Agent: Scalable Hidden Trigger Backdoors for Neural Networks Trained from Scratch |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
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6 |
| Smooth Fictitious Play in Stochastic Games with Perturbed Payoffs and Unknown Transitions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Smoothed Embeddings for Certified Few-Shot Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Smoothed Online Convex Optimization Based on Discounted-Normal-Predictor |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| SnAKe: Bayesian Optimization with Pathwise Exploration |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
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4 |
| So3krates: Equivariant attention for interactions on arbitrary length-scales in molecular systems |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| SoLar: Sinkhorn Label Refinery for Imbalanced Partial-Label Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Sobolev Acceleration and Statistical Optimality for Learning Elliptic Equations via Gradient Descent |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Social-Inverse: Inverse Decision-making of Social Contagion Management with Task Migrations |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
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5 |
| Society of Agents: Regret Bounds of Concurrent Thompson Sampling |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| SoftPatch: Unsupervised Anomaly Detection with Noisy Data |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Solving Quantitative Reasoning Problems with Language Models |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| SoteriaFL: A Unified Framework for Private Federated Learning with Communication Compression |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Sound and Complete Causal Identification with Latent Variables Given Local Background Knowledge |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Sound and Complete Verification of Polynomial Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| SparCL: Sparse Continual Learning on the Edge |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Sparse Fourier Backpropagation in Cryo-EM Reconstruction |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Sparse Gaussian Process Hyperparameters: Optimize or Integrate? |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Sparse Hypergraph Community Detection Thresholds in Stochastic Block Model |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Sparse Interaction Additive Networks via Feature Interaction Detection and Sparse Selection |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Sparse Probabilistic Circuits via Pruning and Growing |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Sparse Structure Search for Delta Tuning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Sparse Winning Tickets are Data-Efficient Image Recognizers |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Sparse2Dense: Learning to Densify 3D Features for 3D Object Detection |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Sparsity in Continuous-Depth Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Spartan: Differentiable Sparsity via Regularized Transportation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Spatial Mixture-of-Experts |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Spatial Pruned Sparse Convolution for Efficient 3D Object Detection |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Spectral Bias Outside the Training Set for Deep Networks in the Kernel Regime |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Spectral Bias in Practice: The Role of Function Frequency in Generalization |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Spectrum Random Masking for Generalization in Image-based Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Spending Thinking Time Wisely: Accelerating MCTS with Virtual Expansions |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Spherical Channels for Modeling Atomic Interactions |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Spherization Layer: Representation Using Only Angles |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Split-kl and PAC-Bayes-split-kl Inequalities for Ternary Random Variables |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Squeezeformer: An Efficient Transformer for Automatic Speech Recognition |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Stability Analysis and Generalization Bounds of Adversarial Training |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Stability and Generalization Analysis of Gradient Methods for Shallow Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Stability and Generalization for Markov Chain Stochastic Gradient Methods |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Stability and Generalization of Kernel Clustering: from Single Kernel to Multiple Kernel |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Staggered Rollout Designs Enable Causal Inference Under Interference Without Network Knowledge |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Staircase Attention for Recurrent Processing of Sequences |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Star Temporal Classification: Sequence Modeling with Partially Labeled Data |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Stars: Tera-Scale Graph Building for Clustering and Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Statistical Learning and Inverse Problems: A Stochastic Gradient Approach |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Statistical, Robustness, and Computational Guarantees for Sliced Wasserstein Distances |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Statistically Meaningful Approximation: a Case Study on Approximating Turing Machines with Transformers |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Stimulative Training of Residual Networks: A Social Psychology Perspective of Loafing |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Stochastic Adaptive Activation Function |
❌ |
✅ |
✅ |
❌ |
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2 |
| Stochastic Halpern Iteration with Variance Reduction for Stochastic Monotone Inclusions |
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❌ |
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5 |
| Stochastic Multiple Target Sampling Gradient Descent |
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3 |
| Stochastic Online Learning with Feedback Graphs: Finite-Time and Asymptotic Optimality |
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1 |
| Stochastic Second-Order Methods Improve Best-Known Sample Complexity of SGD for Gradient-Dominated Functions |
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❌ |
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5 |
| Stochastic Window Transformer for Image Restoration |
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3 |
| Streaming Radiance Fields for 3D Video Synthesis |
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4 |
| Structural Analysis of Branch-and-Cut and the Learnability of Gomory Mixed Integer Cuts |
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0 |
| Structural Kernel Search via Bayesian Optimization and Symbolical Optimal Transport |
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4 |
| Structural Knowledge Distillation for Object Detection |
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❌ |
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5 |
| Structural Pruning via Latency-Saliency Knapsack |
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✅ |
✅ |
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7 |
| Structure-Aware Image Segmentation with Homotopy Warping |
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✅ |
✅ |
✅ |
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6 |
| Structure-Preserving 3D Garment Modeling with Neural Sewing Machines |
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❌ |
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❌ |
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4 |
| Structured Energy Network As a Loss |
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✅ |
✅ |
❌ |
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6 |
| Structured Recognition for Generative Models with Explaining Away |
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✅ |
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4 |
| Structuring Representations Using Group Invariants |
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3 |
| Structuring Uncertainty for Fine-Grained Sampling in Stochastic Segmentation Networks |
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✅ |
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6 |
| Sub-exponential time Sum-of-Squares lower bounds for Principal Components Analysis |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Subgame Solving in Adversarial Team Games |
✅ |
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❌ |
✅ |
✅ |
✅ |
4 |
| Subgroup Robustness Grows On Trees: An Empirical Baseline Investigation |
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✅ |
✅ |
❌ |
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❌ |
✅ |
3 |
| Sublinear Algorithms for Hierarchical Clustering |
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❌ |
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❌ |
0 |
| Submodular Maximization in Clean Linear Time |
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❌ |
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❌ |
✅ |
3 |
| Subquadratic Kronecker Regression with Applications to Tensor Decomposition |
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✅ |
❌ |
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5 |
| Subsidiary Prototype Alignment for Universal Domain Adaptation |
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4 |
| Subspace Recovery from Heterogeneous Data with Non-isotropic Noise |
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0 |
| Subspace clustering in high-dimensions: Phase transitions & Statistical-to-Computational gap |
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❌ |
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3 |
| Supervised Training of Conditional Monge Maps |
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3 |
| Supervising the Multi-Fidelity Race of Hyperparameter Configurations |
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5 |
| Support Recovery in Sparse PCA with Incomplete Data |
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3 |
| Supported Policy Optimization for Offline Reinforcement Learning |
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4 |
| Surprise Minimizing Multi-Agent Learning with Energy-based Models |
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❌ |
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6 |
| Surprising Instabilities in Training Deep Networks and a Theoretical Analysis |
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✅ |
❌ |
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2 |
| Sustainable Online Reinforcement Learning for Auto-bidding |
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✅ |
2 |
| SwinTrack: A Simple and Strong Baseline for Transformer Tracking |
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4 |
| Sym-NCO: Leveraging Symmetricity for Neural Combinatorial Optimization |
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4 |
| Symbolic Distillation for Learned TCP Congestion Control |
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4 |
| Symmetry Teleportation for Accelerated Optimization |
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5 |
| Symmetry-induced Disentanglement on Graphs |
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3 |
| Symplectic Spectrum Gaussian Processes: Learning Hamiltonians from Noisy and Sparse Data |
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4 |
| Syndicated Bandits: A Framework for Auto Tuning Hyper-parameters in Contextual Bandit Algorithms |
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❌ |
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4 |
| Synergy-of-Experts: Collaborate to Improve Adversarial Robustness |
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4 |
| Synthetic Model Combination: An Instance-wise Approach to Unsupervised Ensemble Learning |
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3 |
| Systematic improvement of neural network quantum states using Lanczos |
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3 |
| TA-GATES: An Encoding Scheme for Neural Network Architectures |
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5 |
| TA-MoE: Topology-Aware Large Scale Mixture-of-Expert Training |
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5 |
| TANGO: Text-driven Photorealistic and Robust 3D Stylization via Lighting Decomposition |
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4 |
| TANKBind: Trigonometry-Aware Neural NetworKs for Drug-Protein Binding Structure Prediction |
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3 |
| TCT: Convexifying Federated Learning using Bootstrapped Neural Tangent Kernels |
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4 |
| TOIST: Task Oriented Instance Segmentation Transformer with Noun-Pronoun Distillation |
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✅ |
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5 |
| TPU-KNN: K Nearest Neighbor Search at Peak FLOP/s |
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4 |
| TREC: Transient Redundancy Elimination-based Convolution |
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4 |
| TTOpt: A Maximum Volume Quantized Tensor Train-based Optimization and its Application to Reinforcement Learning |
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4 |
| TUSK: Task-Agnostic Unsupervised Keypoints |
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2 |
| TVLT: Textless Vision-Language Transformer |
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5 |
| TaSIL: Taylor Series Imitation Learning |
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3 |
| TabNAS: Rejection Sampling for Neural Architecture Search on Tabular Datasets |
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6 |
| Taming Fat-Tailed (“Heavier-Tailed” with Potentially Infinite Variance) Noise in Federated Learning |
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2 |
| TarGF: Learning Target Gradient Field to Rearrange Objects without Explicit Goal Specification |
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2 |
| Target alignment in truncated kernel ridge regression |
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1 |
| Task Discovery: Finding the Tasks that Neural Networks Generalize on |
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4 |
| Task-Agnostic Graph Explanations |
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5 |
| Task-Free Continual Learning via Online Discrepancy Distance Learning |
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4 |
| Task-level Differentially Private Meta Learning |
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4 |
| Teach Less, Learn More: On the Undistillable Classes in Knowledge Distillation |
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2 |
| Teacher Forcing Recovers Reward Functions for Text Generation |
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4 |
| Template based Graph Neural Network with Optimal Transport Distances |
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5 |
| Tempo: Accelerating Transformer-Based Model Training through Memory Footprint Reduction |
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4 |
| Temporal Effective Batch Normalization in Spiking Neural Networks |
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❌ |
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2 |
| Temporal Latent Bottleneck: Synthesis of Fast and Slow Processing Mechanisms in Sequence Learning |
✅ |
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❌ |
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6 |
| Temporally Disentangled Representation Learning |
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3 |
| Temporally-Consistent Survival Analysis |
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7 |
| Tensor Program Optimization with Probabilistic Programs |
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4 |
| Tensor Wheel Decomposition and Its Tensor Completion Application |
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6 |
| Test Time Adaptation via Conjugate Pseudo-labels |
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6 |
| Test-Time Prompt Tuning for Zero-Shot Generalization in Vision-Language Models |
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3 |
| Test-Time Training with Masked Autoencoders |
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5 |
| Text Classification with Born's Rule |
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6 |
| Text-Adaptive Multiple Visual Prototype Matching for Video-Text Retrieval |
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3 |
| The Burer-Monteiro SDP method can fail even above the Barvinok-Pataki bound |
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3 |
| The Curse of Unrolling: Rate of Differentiating Through Optimization |
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2 |
| The Effects of Regularization and Data Augmentation are Class Dependent |
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4 |
| The First Optimal Acceleration of High-Order Methods in Smooth Convex Optimization |
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1 |
| The First Optimal Algorithm for Smooth and Strongly-Convex-Strongly-Concave Minimax Optimization |
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1 |
| The Franz-Parisi Criterion and Computational Trade-offs in High Dimensional Statistics |
❌ |
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0 |
| The Gyro-Structure of Some Matrix Manifolds |
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❌ |
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2 |
| The Hessian Screening Rule |
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4 |
| The Impact of Task Underspecification in Evaluating Deep Reinforcement Learning |
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✅ |
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2 |
| The Implicit Delta Method |
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5 |
| The Mechanism of Prediction Head in Non-contrastive Self-supervised Learning |
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3 |
| The Minority Matters: A Diversity-Promoting Collaborative Metric Learning Algorithm |
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5 |
| The Missing Invariance Principle found -- the Reciprocal Twin of Invariant Risk Minimization |
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2 |
| The Nature of Temporal Difference Errors in Multi-step Distributional Reinforcement Learning |
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2 |
| The Neural Covariance SDE: Shaped Infinite Depth-and-Width Networks at Initialization |
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2 |
| The Neural Testbed: Evaluating Joint Predictions |
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3 |
| The Phenomenon of Policy Churn |
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2 |
| The Pitfalls of Regularization in Off-Policy TD Learning |
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2 |
| The Policy-gradient Placement and Generative Routing Neural Networks for Chip Design |
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4 |
| The Power and Limitation of Pretraining-Finetuning for Linear Regression under Covariate Shift |
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2 |
| The Privacy Onion Effect: Memorization is Relative |
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2 |
| The Query Complexity of Cake Cutting |
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0 |
| The Role of Baselines in Policy Gradient Optimization |
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3 |
| The Sample Complexity of One-Hidden-Layer Neural Networks |
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0 |
| The Stability-Efficiency Dilemma: Investigating Sequence Length Warmup for Training GPT Models |
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4 |
| The Unreasonable Effectiveness of Fully-Connected Layers for Low-Data Regimes |
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4 |
| The Unreliability of Explanations in Few-shot Prompting for Textual Reasoning |
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3 |
| The alignment property of SGD noise and how it helps select flat minima: A stability analysis |
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4 |
| The computational and learning benefits of Daleian neural networks |
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3 |
| The least-control principle for local learning at equilibrium |
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2 |
| The price of ignorance: how much does it cost to forget noise structure in low-rank matrix estimation? |
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2 |
| The price of unfairness in linear bandits with biased feedback |
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1 |
| The trade-offs of model size in large recommendation models : 100GB to 10MB Criteo-tb DLRM model |
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4 |
| Theoretical analysis of deep neural networks for temporally dependent observations |
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3 |
| Theoretically Better and Numerically Faster Distributed Optimization with Smoothness-Aware Quantization Techniques |
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3 |
| Theoretically Provable Spiking Neural Networks |
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2 |
| Theory and Approximate Solvers for Branched Optimal Transport with Multiple Sources |
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4 |
| Theseus: A Library for Differentiable Nonlinear Optimization |
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4 |
| Thinking Outside the Ball: Optimal Learning with Gradient Descent for Generalized Linear Stochastic Convex Optimization |
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0 |
| Thinned random measures for sparse graphs with overlapping communities |
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3 |
| Thompson Sampling Efficiently Learns to Control Diffusion Processes |
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3 |
| Thor: Wielding Hammers to Integrate Language Models and Automated Theorem Provers |
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7 |
| Tiered Reinforcement Learning: Pessimism in the Face of Uncertainty and Constant Regret |
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2 |
| Tight Analysis of Extra-gradient and Optimistic Gradient Methods For Nonconvex Minimax Problems |
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❌ |
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2 |
| Tight Lower Bounds on Worst-Case Guarantees for Zero-Shot Learning with Attributes |
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✅ |
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2 |
| Tight Mutual Information Estimation With Contrastive Fenchel-Legendre Optimization |
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3 |
| Tikhonov Regularization is Optimal Transport Robust under Martingale Constraints |
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✅ |
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6 |
| Time-Conditioned Dances with Simplicial Complexes: Zigzag Filtration Curve based Supra-Hodge Convolution Networks for Time-series Forecasting |
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4 |
| To update or not to update? Neurons at equilibrium in deep models |
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6 |
| ToDD: Topological Compound Fingerprinting in Computer-Aided Drug Discovery |
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❌ |
✅ |
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❌ |
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4 |
| TokenMixup: Efficient Attention-guided Token-level Data Augmentation for Transformers |
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✅ |
❌ |
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6 |
| Top Two Algorithms Revisited |
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✅ |
❌ |
❌ |
✅ |
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5 |
| Torsional Diffusion for Molecular Conformer Generation |
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✅ |
✅ |
✅ |
❌ |
❌ |
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5 |
| TotalSelfScan: Learning Full-body Avatars from Self-Portrait Videos of Faces, Hands, and Bodies |
❌ |
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✅ |
❌ |
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2 |
| Toward Efficient Robust Training against Union of $\ell_p$ Threat Models |
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✅ |
✅ |
❌ |
❌ |
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4 |
| Toward Equation of Motion for Deep Neural Networks: Continuous-time Gradient Descent and Discretization Error Analysis |
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✅ |
❌ |
✅ |
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5 |
| Toward Robust Spiking Neural Network Against Adversarial Perturbation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
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5 |
| Toward Understanding Privileged Features Distillation in Learning-to-Rank |
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✅ |
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❌ |
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4 |
| Toward a realistic model of speech processing in the brain with self-supervised learning |
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❌ |
✅ |
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❌ |
❌ |
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3 |
| Towards Consistency in Adversarial Classification |
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❌ |
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❌ |
❌ |
❌ |
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0 |
| Towards Disentangling Information Paths with Coded ResNeXt |
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✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Towards Diverse and Faithful One-shot Adaption of Generative Adversarial Networks |
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❌ |
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❌ |
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4 |
| Towards Effective Multi-Modal Interchanges in Zero-Resource Sounding Object Localization |
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✅ |
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❌ |
❌ |
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4 |
| Towards Efficient 3D Object Detection with Knowledge Distillation |
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✅ |
✅ |
✅ |
❌ |
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5 |
| Towards Efficient Post-training Quantization of Pre-trained Language Models |
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✅ |
✅ |
✅ |
❌ |
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5 |
| Towards Hard-pose Virtual Try-on via 3D-aware Global Correspondence Learning |
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✅ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Towards Improving Calibration in Object Detection Under Domain Shift |
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❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Towards Improving Faithfulness in Abstractive Summarization |
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✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Towards Learning Universal Hyperparameter Optimizers with Transformers |
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✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Towards Lightweight Black-Box Attack Against Deep Neural Networks |
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✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Towards Optimal Communication Complexity in Distributed Non-Convex Optimization |
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❌ |
✅ |
❌ |
❌ |
❌ |
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3 |
| Towards Out-of-Distribution Sequential Event Prediction: A Causal Treatment |
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✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Towards Practical Control of Singular Values of Convolutional Layers |
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✅ |
✅ |
❌ |
✅ |
❌ |
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5 |
| Towards Practical Few-shot Query Sets: Transductive Minimum Description Length Inference |
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✅ |
✅ |
✅ |
❌ |
❌ |
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5 |
| Towards Reasonable Budget Allocation in Untargeted Graph Structure Attacks via Gradient Debias |
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❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Towards Reliable Simulation-Based Inference with Balanced Neural Ratio Estimation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Towards Robust Blind Face Restoration with Codebook Lookup Transformer |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Towards Safe Reinforcement Learning with a Safety Editor Policy |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Towards Theoretically Inspired Neural Initialization Optimization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Towards Trustworthy Automatic Diagnosis Systems by Emulating Doctors' Reasoning with Deep Reinforcement Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Towards Understanding Grokking: An Effective Theory of Representation Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Towards Understanding the Condensation of Neural Networks at Initial Training |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Towards Understanding the Mixture-of-Experts Layer in Deep Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Towards Versatile Embodied Navigation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Towards a Standardised Performance Evaluation Protocol for Cooperative MARL |
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✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Towards a Unified Framework for Uncertainty-aware Nonlinear Variable Selection with Theoretical Guarantees |
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❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Tracking Functional Changes in Nonstationary Signals with Evolutionary Ensemble Bayesian Model for Robust Neural Decoding |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Tractable Function-Space Variational Inference in Bayesian Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Tractable Optimality in Episodic Latent MABs |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Trade-off between Payoff and Model Rewards in Shapley-Fair Collaborative Machine Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Trading Off Resource Budgets For Improved Regret Bounds |
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❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Trading off Image Quality for Robustness is not Necessary with Regularized Deterministic Autoencoders |
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✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Trading off Utility, Informativeness, and Complexity in Emergent Communication |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Training Scale-Invariant Neural Networks on the Sphere Can Happen in Three Regimes |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
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4 |
| Training Spiking Neural Networks with Event-driven Backpropagation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Training Spiking Neural Networks with Local Tandem Learning |
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✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| Training Subset Selection for Weak Supervision |
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✅ |
✅ |
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❌ |
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5 |
| Training Uncertainty-Aware Classifiers with Conformalized Deep Learning |
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✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Training and Inference on Any-Order Autoregressive Models the Right Way |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Training language models to follow instructions with human feedback |
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❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Training stochastic stabilized supralinear networks by dynamics-neutral growth |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Training with More Confidence: Mitigating Injected and Natural Backdoors During Training |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Trajectory Inference via Mean-field Langevin in Path Space |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Trajectory balance: Improved credit assignment in GFlowNets |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Trajectory of Mini-Batch Momentum: Batch Size Saturation and Convergence in High Dimensions |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Trajectory-guided Control Prediction for End-to-end Autonomous Driving: A Simple yet Strong Baseline |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| TransBoost: Improving the Best ImageNet Performance using Deep Transduction |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| TransTab: Learning Transferable Tabular Transformers Across Tables |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Transcormer: Transformer for Sentence Scoring with Sliding Language Modeling |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Transfer Learning on Heterogeneous Feature Spaces for Treatment Effects Estimation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
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6 |
| Transferring Fairness under Distribution Shifts via Fair Consistency Regularization |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Transferring Pre-trained Multimodal Representations with Cross-modal Similarity Matching |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Transform Once: Efficient Operator Learning in Frequency Domain |
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✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Transformer Memory as a Differentiable Search Index |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Transformer-based Working Memory for Multiagent Reinforcement Learning with Action Parsing |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Transformers from an Optimization Perspective |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Transformers meet Stochastic Block Models: Attention with Data-Adaptive Sparsity and Cost |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Transition to Linearity of General Neural Networks with Directed Acyclic Graph Architecture |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Translation-equivariant Representation in Recurrent Networks with a Continuous Manifold of Attractors |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Trap and Replace: Defending Backdoor Attacks by Trapping Them into an Easy-to-Replace Subnetwork |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Tree Mover's Distance: Bridging Graph Metrics and Stability of Graph Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Tree ensemble kernels for Bayesian optimization with known constraints over mixed-feature spaces |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
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4 |
| TreeMoCo: Contrastive Neuron Morphology Representation Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Triangulation candidates for Bayesian optimization |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Trimmed Maximum Likelihood Estimation for Robust Generalized Linear Model |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Truly Deterministic Policy Optimization |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Truncated Matrix Power Iteration for Differentiable DAG Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Truncated proposals for scalable and hassle-free simulation-based inference |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Trust Region Policy Optimization with Optimal Transport Discrepancies: Duality and Algorithm for Continuous Actions |
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❌ |
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❌ |
✅ |
❌ |
✅ |
4 |
| Trustworthy Monte Carlo |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
3 |
| Tsetlin Machine for Solving Contextual Bandit Problems |
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✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Turbocharging Solution Concepts: Solving NEs, CEs and CCEs with Neural Equilibrium Solvers |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Two-Stream Network for Sign Language Recognition and Translation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Two-layer neural network on infinite dimensional data: global optimization guarantee in the mean-field regime |
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❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| UDC: Unified DNAS for Compressible TinyML Models for Neural Processing Units |
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❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| ULNeF: Untangled Layered Neural Fields for Mix-and-Match Virtual Try-On |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
2 |
| UMIX: Improving Importance Weighting for Subpopulation Shift via Uncertainty-Aware Mixup |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| UQGAN: A Unified Model for Uncertainty Quantification of Deep Classifiers trained via Conditional GANs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| UViM: A Unified Modeling Approach for Vision with Learned Guiding Codes |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Uncalibrated Models Can Improve Human-AI Collaboration |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Uncertainty Estimation Using Riemannian Model Dynamics for Offline Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Uncertainty Estimation for Multi-view Data: The Power of Seeing the Whole Picture |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Uncertainty-Aware Hierarchical Refinement for Incremental Implicitly-Refined Classification |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Uncertainty-Aware Reinforcement Learning for Risk-Sensitive Player Evaluation in Sports Game |
❌ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
3 |
| Uncoupled Learning Dynamics with $O(\log T)$ Swap Regret in Multiplayer Games |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Uncovering the Structural Fairness in Graph Contrastive Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Understanding Benign Overfitting in Gradient-Based Meta Learning |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Understanding Cross-Domain Few-Shot Learning Based on Domain Similarity and Few-Shot Difficulty |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Understanding Deep Contrastive Learning via Coordinate-wise Optimization |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Understanding Deep Neural Function Approximation in Reinforcement Learning via $\epsilon$-Greedy Exploration |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Understanding Hyperdimensional Computing for Parallel Single-Pass Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Understanding Non-linearity in Graph Neural Networks from the Bayesian-Inference Perspective |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Understanding Programmatic Weak Supervision via Source-aware Influence Function |
❌ |
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✅ |
✅ |
❌ |
❌ |
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4 |
| Understanding Robust Learning through the Lens of Representation Similarities |
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✅ |
❌ |
✅ |
❌ |
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4 |
| Understanding Square Loss in Training Overparametrized Neural Network Classifiers |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
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3 |
| Understanding and Extending Subgraph GNNs by Rethinking Their Symmetries |
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✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Understanding and Improving Robustness of Vision Transformers through Patch-based Negative Augmentation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Understanding the Eluder Dimension |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Understanding the Evolution of Linear Regions in Deep Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Understanding the Failure of Batch Normalization for Transformers in NLP |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
4 |
| Understanding the Generalization Benefit of Normalization Layers: Sharpness Reduction |
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✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| UnfoldML: Cost-Aware and Uncertainty-Based Dynamic 2D Prediction for Multi-Stage Classification |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Uni-Perceiver-MoE: Learning Sparse Generalist Models with Conditional MoEs |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| UniCLIP: Unified Framework for Contrastive Language-Image Pre-training |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| UniGAN: Reducing Mode Collapse in GANs using a Uniform Generator |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Uni[MASK]: Unified Inference in Sequential Decision Problems |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Unified Optimal Transport Framework for Universal Domain Adaptation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Unifying Voxel-based Representation with Transformer for 3D Object Detection |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Unifying and Boosting Gradient-Based Training-Free Neural Architecture Search |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Universal Rates for Interactive Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Universality of Group Convolutional Neural Networks Based on Ridgelet Analysis on Groups |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Universally Expressive Communication in Multi-Agent Reinforcement Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Unknown-Aware Domain Adversarial Learning for Open-Set Domain Adaptation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Unlabelled Sample Compression Schemes for Intersection-Closed Classes and Extremal Classes |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
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1 |
| Unpacking Reward Shaping: Understanding the Benefits of Reward Engineering on Sample Complexity |
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| Unsupervised Adaptation from Repeated Traversals for Autonomous Driving |
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| Unsupervised Causal Generative Understanding of Images |
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| Unsupervised Cross-Task Generalization via Retrieval Augmentation |
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4 |
| Unsupervised Domain Adaptation for Semantic Segmentation using Depth Distribution |
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6 |
| Unsupervised Image-to-Image Translation with Density Changing Regularization |
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4 |
| Unsupervised Learning From Incomplete Measurements for Inverse Problems |
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5 |
| Unsupervised Learning for Combinatorial Optimization with Principled Objective Relaxation |
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5 |
| Unsupervised Learning of Equivariant Structure from Sequences |
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4 |
| Unsupervised Learning of Group Invariant and Equivariant Representations |
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4 |
| Unsupervised Learning of Shape Programs with Repeatable Implicit Parts |
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2 |
| Unsupervised Learning under Latent Label Shift |
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6 |
| Unsupervised Multi-Object Segmentation by Predicting Probable Motion Patterns |
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4 |
| Unsupervised Multi-View Object Segmentation Using Radiance Field Propagation |
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| Unsupervised Object Detection Pretraining with Joint Object Priors Generation and Detector Learning |
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4 |
| Unsupervised Object Representation Learning using Translation and Rotation Group Equivariant VAE |
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5 |
| Unsupervised Point Cloud Completion and Segmentation by Generative Adversarial Autoencoding Network |
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4 |
| Unsupervised Reinforcement Learning with Contrastive Intrinsic Control |
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5 |
| Unsupervised Representation Learning from Pre-trained Diffusion Probabilistic Models |
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4 |
| Unsupervised Skill Discovery via Recurrent Skill Training |
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3 |
| Unsupervised Visual Representation Learning via Mutual Information Regularized Assignment |
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6 |
| Untargeted Backdoor Watermark: Towards Harmless and Stealthy Dataset Copyright Protection |
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3 |
| Uplifting Bandits |
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4 |
| Use-Case-Grounded Simulations for Explanation Evaluation |
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4 |
| Using Embeddings for Causal Estimation of Peer Influence in Social Networks |
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3 |
| Using Mixup as a Regularizer Can Surprisingly Improve Accuracy & Out-of-Distribution Robustness |
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5 |
| Using Partial Monotonicity in Submodular Maximization |
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| Using natural language and program abstractions to instill human inductive biases in machines |
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1 |
| VAEL: Bridging Variational Autoencoders and Probabilistic Logic Programming |
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4 |
| VCT: A Video Compression Transformer |
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| VER: Scaling On-Policy RL Leads to the Emergence of Navigation in Embodied Rearrangement |
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4 |
| VF-PS: How to Select Important Participants in Vertical Federated Learning, Efficiently and Securely? |
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7 |
| VICE: Variational Interpretable Concept Embeddings |
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5 |
| VICRegL: Self-Supervised Learning of Local Visual Features |
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| VITA: Video Instance Segmentation via Object Token Association |
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| VLMo: Unified Vision-Language Pre-Training with Mixture-of-Modality-Experts |
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5 |
| VRL3: A Data-Driven Framework for Visual Deep Reinforcement Learning |
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3 |
| VTC-LFC: Vision Transformer Compression with Low-Frequency Components |
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| VaiPhy: a Variational Inference Based Algorithm for Phylogeny |
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5 |
| Value Function Decomposition for Iterative Design of Reinforcement Learning Agents |
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2 |
| Variable-rate hierarchical CPC leads to acoustic unit discovery in speech |
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5 |
| Variance Reduced ProxSkip: Algorithm, Theory and Application to Federated Learning |
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3 |
| Variational Model Perturbation for Source-Free Domain Adaptation |
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| Variational inference via Wasserstein gradient flows |
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2 |
| VectorAdam for Rotation Equivariant Geometry Optimization |
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4 |
| Verification and search algorithms for causal DAGs |
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2 |
| Versatile Multi-stage Graph Neural Network for Circuit Representation |
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| ViTPose: Simple Vision Transformer Baselines for Human Pose Estimation |
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| Video Diffusion Models |
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| Video PreTraining (VPT): Learning to Act by Watching Unlabeled Online Videos |
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| Video-based Human-Object Interaction Detection from Tubelet Tokens |
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| VideoMAE: Masked Autoencoders are Data-Efficient Learners for Self-Supervised Video Pre-Training |
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5 |
| ViewFool: Evaluating the Robustness of Visual Recognition to Adversarial Viewpoints |
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| VisCo Grids: Surface Reconstruction with Viscosity and Coarea Grids |
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| VisFIS: Visual Feature Importance Supervision with Right-for-the-Right-Reason Objectives |
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5 |
| Vision GNN: An Image is Worth Graph of Nodes |
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5 |
| Vision Transformers provably learn spatial structure |
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5 |
| Visual Clues: Bridging Vision and Language Foundations for Image Paragraph Captioning |
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| Visual Concepts Tokenization |
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| Visual Prompting via Image Inpainting |
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4 |
| Visual correspondence-based explanations improve AI robustness and human-AI team accuracy |
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3 |
| VoiceBlock: Privacy through Real-Time Adversarial Attacks with Audio-to-Audio Models |
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6 |
| VoxGRAF: Fast 3D-Aware Image Synthesis with Sparse Voxel Grids |
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4 |
| WT-MVSNet: Window-based Transformers for Multi-view Stereo |
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5 |
| Washing The Unwashable : On The (Im)possibility of Fairwashing Detection |
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3 |
| Wasserstein $K$-means for clustering probability distributions |
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| Wasserstein Iterative Networks for Barycenter Estimation |
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| Wasserstein Logistic Regression with Mixed Features |
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7 |
| Watermarking for Out-of-distribution Detection |
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5 |
| WaveBound: Dynamic Error Bounds for Stable Time Series Forecasting |
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5 |
| Wavelet Feature Maps Compression for Image-to-Image CNNs |
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| Wavelet Score-Based Generative Modeling |
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5 |
| Weak-shot Semantic Segmentation via Dual Similarity Transfer |
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4 |
| Weakly Supervised Representation Learning with Sparse Perturbations |
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3 |
| Weakly supervised causal representation learning |
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3 |
| Weakly-Supervised Multi-Granularity Map Learning for Vision-and-Language Navigation |
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4 |
| WebShop: Towards Scalable Real-World Web Interaction with Grounded Language Agents |
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3 |
| Weighted Distillation with Unlabeled Examples |
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| Weighted Mutual Learning with Diversity-Driven Model Compression |
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| WeightedSHAP: analyzing and improving Shapley based feature attributions |
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3 |
| Weisfeiler and Leman Go Walking: Random Walk Kernels Revisited |
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6 |
| What Can Transformers Learn In-Context? A Case Study of Simple Function Classes |
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| What Can the Neural Tangent Kernel Tell Us About Adversarial Robustness? |
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| What I Cannot Predict, I Do Not Understand: A Human-Centered Evaluation Framework for Explainability Methods |
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| What Makes Graph Neural Networks Miscalibrated? |
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| What Makes a "Good" Data Augmentation in Knowledge Distillation - A Statistical Perspective |
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| What You See is What You Classify: Black Box Attributions |
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| What You See is What You Get: Principled Deep Learning via Distributional Generalization |
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| What are the best Systems? New Perspectives on NLP Benchmarking |
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6 |
| What is Where by Looking: Weakly-Supervised Open-World Phrase-Grounding without Text Inputs |
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| What is a Good Metric to Study Generalization of Minimax Learners? |
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| What's the Harm? Sharp Bounds on the Fraction Negatively Affected by Treatment |
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| When Adversarial Training Meets Vision Transformers: Recipes from Training to Architecture |
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| When Combinatorial Thompson Sampling meets Approximation Regret |
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| When Do Flat Minima Optimizers Work? |
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| When Does Differentially Private Learning Not Suffer in High Dimensions? |
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| When Does Group Invariant Learning Survive Spurious Correlations? |
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| When Privacy Meets Partial Information: A Refined Analysis of Differentially Private Bandits |
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| When are Local Queries Useful for Robust Learning? |
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| When are Offline Two-Player Zero-Sum Markov Games Solvable? |
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| When does dough become a bagel? Analyzing the remaining mistakes on ImageNet |
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| When does return-conditioned supervised learning work for offline reinforcement learning? |
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| When to Ask for Help: Proactive Interventions in Autonomous Reinforcement Learning |
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| When to Intervene: Learning Optimal Intervention Policies for Critical Events |
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| When to Make Exceptions: Exploring Language Models as Accounts of Human Moral Judgment |
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| When to Trust Your Simulator: Dynamics-Aware Hybrid Offline-and-Online Reinforcement Learning |
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| When to Update Your Model: Constrained Model-based Reinforcement Learning |
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| Where do Models go Wrong? Parameter-Space Saliency Maps for Explainability |
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| Where to Pay Attention in Sparse Training for Feature Selection? |
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| Where2comm: Communication-Efficient Collaborative Perception via Spatial Confidence Maps |
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| Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post Hoc Explanations |
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| Whitening Convergence Rate of Coupling-based Normalizing Flows |
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5 |
| Why Do Artificially Generated Data Help Adversarial Robustness |
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| Why Robust Generalization in Deep Learning is Difficult: Perspective of Expressive Power |
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| Why So Pessimistic? Estimating Uncertainties for Offline RL through Ensembles, and Why Their Independence Matters |
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| Why do We Need Large Batchsizes in Contrastive Learning? A Gradient-Bias Perspective |
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| Why neural networks find simple solutions: The many regularizers of geometric complexity |
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| Will Bilevel Optimizers Benefit from Loops |
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| XTC: Extreme Compression for Pre-trained Transformers Made Simple and Efficient |
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| You Can’t Count on Luck: Why Decision Transformers and RvS Fail in Stochastic Environments |
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| You Never Stop Dancing: Non-freezing Dance Generation via Bank-constrained Manifold Projection |
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| You Only Live Once: Single-Life Reinforcement Learning |
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| Your Out-of-Distribution Detection Method is Not Robust! |
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5 |
| Your Transformer May Not be as Powerful as You Expect |
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5 |
| ZARTS: On Zero-order Optimization for Neural Architecture Search |
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6 |
| ZIN: When and How to Learn Invariance Without Environment Partition? |
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4 |
| ZSON: Zero-Shot Object-Goal Navigation using Multimodal Goal Embeddings |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Zero-Shot 3D Drug Design by Sketching and Generating |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Zero-Shot Video Question Answering via Frozen Bidirectional Language Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Zero-Sum Stochastic Stackelberg Games |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Zero-shot Transfer Learning within a Heterogeneous Graph via Knowledge Transfer Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| ZeroC: A Neuro-Symbolic Model for Zero-shot Concept Recognition and Acquisition at Inference Time |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| ZeroQuant: Efficient and Affordable Post-Training Quantization for Large-Scale Transformers |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Zeroth-Order Hard-Thresholding: Gradient Error vs. Expansivity |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Zeroth-Order Negative Curvature Finding: Escaping Saddle Points without Gradients |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Zonotope Domains for Lagrangian Neural Network Verification |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| ZooD: Exploiting Model Zoo for Out-of-Distribution Generalization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| coVariance Neural Networks |
❌ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
3 |
| projUNN: efficient method for training deep networks with unitary matrices |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| u-HuBERT: Unified Mixed-Modal Speech Pretraining And Zero-Shot Transfer to Unlabeled Modality |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| “Why Not Other Classes?”: Towards Class-Contrastive Back-Propagation Explanations |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| 🏘️ ProcTHOR: Large-Scale Embodied AI Using Procedural Generation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |