| $(\textrm{Implicit})^2$: Implicit Layers for Implicit Representations |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| $\alpha$-IoU: A Family of Power Intersection over Union Losses for Bounding Box Regression |
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✅ |
✅ |
✅ |
✅ |
✅ |
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6 |
| $\texttt{LeadCache}$: Regret-Optimal Caching in Networks |
✅ |
✅ |
✅ |
❌ |
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4 |
| (Almost) Free Incentivized Exploration from Decentralized Learning Agents |
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❌ |
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2 |
| 3D Pose Transfer with Correspondence Learning and Mesh Refinement |
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✅ |
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✅ |
❌ |
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4 |
| 3D Siamese Voxel-to-BEV Tracker for Sparse Point Clouds |
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✅ |
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✅ |
❌ |
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4 |
| 3DP3: 3D Scene Perception via Probabilistic Programming |
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✅ |
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❌ |
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2 |
| A 3D Generative Model for Structure-Based Drug Design |
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❌ |
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✅ |
3 |
| A Bayesian-Symbolic Approach to Reasoning and Learning in Intuitive Physics |
✅ |
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✅ |
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4 |
| A Bi-Level Framework for Learning to Solve Combinatorial Optimization on Graphs |
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✅ |
✅ |
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✅ |
✅ |
7 |
| A Biased Graph Neural Network Sampler with Near-Optimal Regret |
✅ |
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✅ |
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❌ |
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✅ |
4 |
| A Causal Lens for Controllable Text Generation |
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❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| A Central Limit Theorem for Differentially Private Query Answering |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A Closer Look at the Worst-case Behavior of Multi-armed Bandit Algorithms |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| A Compositional Atlas of Tractable Circuit Operations for Probabilistic Inference |
✅ |
✅ |
✅ |
❌ |
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❌ |
3 |
| A Comprehensively Tight Analysis of Gradient Descent for PCA |
✅ |
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✅ |
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✅ |
❌ |
✅ |
4 |
| A Computationally Efficient Method for Learning Exponential Family Distributions |
✅ |
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❌ |
❌ |
1 |
| A Consciousness-Inspired Planning Agent for Model-Based Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| A Constant Approximation Algorithm for Sequential Random-Order No-Substitution k-Median Clustering |
✅ |
❌ |
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❌ |
❌ |
❌ |
❌ |
1 |
| A Continuous Mapping For Augmentation Design |
✅ |
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✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| A Contrastive Learning Approach for Training Variational Autoencoder Priors |
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✅ |
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❌ |
❌ |
✅ |
2 |
| A Convergence Analysis of Gradient Descent on Graph Neural Networks |
❌ |
✅ |
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❌ |
❌ |
✅ |
2 |
| A Critical Look at the Consistency of Causal Estimation with Deep Latent Variable Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| A Domain-Shrinking based Bayesian Optimization Algorithm with Order-Optimal Regret Performance |
✅ |
✅ |
✅ |
❌ |
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❌ |
✅ |
4 |
| A Faster Decentralized Algorithm for Nonconvex Minimax Problems |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| A Faster Maximum Cardinality Matching Algorithm with Applications in Machine Learning |
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✅ |
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✅ |
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✅ |
3 |
| A Framework to Learn with Interpretation |
✅ |
✅ |
✅ |
❌ |
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✅ |
4 |
| A Gang of Adversarial Bandits |
✅ |
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❌ |
❌ |
❌ |
❌ |
1 |
| A Gaussian Process-Bayesian Bernoulli Mixture Model for Multi-Label Active Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| A Geometric Analysis of Neural Collapse with Unconstrained Features |
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❌ |
✅ |
✅ |
✅ |
5 |
| A Geometric Perspective towards Neural Calibration via Sensitivity Decomposition |
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✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A Geometric Structure of Acceleration and Its Role in Making Gradients Small Fast |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A Gradient Method for Multilevel Optimization |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| A Hierarchical Reinforcement Learning Based Optimization Framework for Large-scale Dynamic Pickup and Delivery Problems |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| A Highly-Efficient Group Elastic Net Algorithm with an Application to Function-On-Scalar Regression |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
4 |
| A Kernel-based Test of Independence for Cluster-correlated Data |
❌ |
✅ |
✅ |
❌ |
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❌ |
✅ |
3 |
| A Law of Iterated Logarithm for Multi-Agent Reinforcement Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A Little Robustness Goes a Long Way: Leveraging Robust Features for Targeted Transfer Attacks |
❌ |
❌ |
✅ |
✅ |
❌ |
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✅ |
3 |
| A Mathematical Framework for Quantifying Transferability in Multi-source Transfer Learning |
✅ |
✅ |
✅ |
❌ |
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❌ |
✅ |
4 |
| A Max-Min Entropy Framework for Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| A Minimalist Approach to Offline Reinforcement Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| A Multi-Implicit Neural Representation for Fonts |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| A Near-Optimal Algorithm for Debiasing Trained Machine Learning Models |
✅ |
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✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A Near-Optimal Algorithm for Stochastic Bilevel Optimization via Double-Momentum |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A No-go Theorem for Robust Acceleration in the Hyperbolic Plane |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A Non-commutative Extension of Lee-Seung's Algorithm for Positive Semidefinite Factorizations |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| A Normative and Biologically Plausible Algorithm for Independent Component Analysis |
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✅ |
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❌ |
✅ |
3 |
| A Note on Sparse Generalized Eigenvalue Problem |
✅ |
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❌ |
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❌ |
✅ |
2 |
| A PAC-Bayes Analysis of Adversarial Robustness |
✅ |
✅ |
✅ |
❌ |
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❌ |
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4 |
| A Probabilistic State Space Model for Joint Inference from Differential Equations and Data |
✅ |
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✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| A Prototype-Oriented Framework for Unsupervised Domain Adaptation |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Provably Efficient Sample Collection Strategy for Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| A Regression Approach to Learning-Augmented Online Algorithms |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Separation Result Between Data-oblivious and Data-aware Poisoning Attacks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| A Shading-Guided Generative Implicit Model for Shape-Accurate 3D-Aware Image Synthesis |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| A Stochastic Newton Algorithm for Distributed Convex Optimization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| A Surrogate Objective Framework for Prediction+Programming with Soft Constraints |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| A Theoretical Analysis of Fine-tuning with Linear Teachers |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Theory of the Distortion-Perception Tradeoff in Wasserstein Space |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| A Theory-Driven Self-Labeling Refinement Method for Contrastive Representation Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| A Topological Perspective on Causal Inference |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A Trainable Spectral-Spatial Sparse Coding Model for Hyperspectral Image Restoration |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| A Unified Approach to Fair Online Learning via Blackwell Approachability |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Unified View of cGANs with and without Classifiers |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| A Universal Law of Robustness via Isoperimetry |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Variational Perspective on Diffusion-Based Generative Models and Score Matching |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| A Winning Hand: Compressing Deep Networks Can Improve Out-of-Distribution Robustness |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| A first-order primal-dual method with adaptivity to local smoothness |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| A flow-based latent state generative model of neural population responses to natural images |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A generative nonparametric Bayesian model for whole genomes |
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✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
3 |
| A mechanistic multi-area recurrent network model of decision-making |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| A nonparametric method for gradual change problems with statistical guarantees |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| A novel notion of barycenter for probability distributions based on optimal weak mass transport |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| A sampling-based circuit for optimal decision making |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A self consistent theory of Gaussian Processes captures feature learning effects in finite CNNs |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| A single gradient step finds adversarial examples on random two-layers neural networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| A unified framework for bandit multiple testing |
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✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| A universal probabilistic spike count model reveals ongoing modulation of neural variability |
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❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| A variational approximate posterior for the deep Wishart process |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| A$^2$-Net: Learning Attribute-Aware Hash Codes for Large-Scale Fine-Grained Image Retrieval |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| A-NeRF: Articulated Neural Radiance Fields for Learning Human Shape, Appearance, and Pose |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
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3 |
| A/B Testing for Recommender Systems in a Two-sided Marketplace |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
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4 |
| A/B/n Testing with Control in the Presence of Subpopulations |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| ABC: Auxiliary Balanced Classifier for Class-imbalanced Semi-supervised Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| AC-GC: Lossy Activation Compression with Guaranteed Convergence |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| AC/DC: Alternating Compressed/DeCompressed Training of Deep Neural Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| AFEC: Active Forgetting of Negative Transfer in Continual Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| ASSANet: An Anisotropic Separable Set Abstraction for Efficient Point Cloud Representation Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| ATISS: Autoregressive Transformers for Indoor Scene Synthesis |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Absolute Neighbour Difference based Correlation Test for Detecting Heteroscedastic Relationships |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Accelerated Sparse Neural Training: A Provable and Efficient Method to Find N:M Transposable Masks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Accelerating Quadratic Optimization with Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Accelerating Robotic Reinforcement Learning via Parameterized Action Primitives |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| Accommodating Picky Customers: Regret Bound and Exploration Complexity for Multi-Objective Reinforcement Learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Accumulative Poisoning Attacks on Real-time Data |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Accurate Point Cloud Registration with Robust Optimal Transport |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Accurately Solving Rod Dynamics with Graph Learning |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
5 |
| Achieving Forgetting Prevention and Knowledge Transfer in Continual Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Achieving Rotational Invariance with Bessel-Convolutional Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Across-animal odor decoding by probabilistic manifold alignment |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Action-guided 3D Human Motion Prediction |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Activation Sharing with Asymmetric Paths Solves Weight Transport Problem without Bidirectional Connection |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Active 3D Shape Reconstruction from Vision and Touch |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Active Assessment of Prediction Services as Accuracy Surface Over Attribute Combinations |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Active Learning of Convex Halfspaces on Graphs |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Active Offline Policy Selection |
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✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Active clustering for labeling training data |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
1 |
| Actively Identifying Causal Effects with Latent Variables Given Only Response Variable Observable |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Adaptable Agent Populations via a Generative Model of Policies |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Adapting to function difficulty and growth conditions in private optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Adaptive Conformal Inference Under Distribution Shift |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Adaptive Data Augmentation on Temporal Graphs |
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✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Adaptive Denoising via GainTuning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Adaptive Diffusion in Graph Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Adaptive Ensemble Q-learning: Minimizing Estimation Bias via Error Feedback |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Adaptive First-Order Methods Revisited: Convex Minimization without Lipschitz Requirements |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Adaptive Machine Unlearning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Adaptive Online Packing-guided Search for POMDPs |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Adaptive Proximal Gradient Methods for Structured Neural Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Adaptive Risk Minimization: Learning to Adapt to Domain Shift |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Adaptive Sampling for Minimax Fair Classification |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Adaptive wavelet distillation from neural networks through interpretations |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Adder Attention for Vision Transformer |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Addressing Algorithmic Disparity and Performance Inconsistency in Federated Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Adjusting for Autocorrelated Errors in Neural Networks for Time Series |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Adversarial Attack Generation Empowered by Min-Max Optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Adversarial Attacks on Black Box Video Classifiers: Leveraging the Power of Geometric Transformations |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Adversarial Attacks on Graph Classifiers via Bayesian Optimisation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Adversarial Examples Make Strong Poisons |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Adversarial Examples for k-Nearest Neighbor Classifiers Based on Higher-Order Voronoi Diagrams |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Adversarial Examples in Multi-Layer Random ReLU Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Adversarial Feature Desensitization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Adversarial Graph Augmentation to Improve Graph Contrastive Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Adversarial Intrinsic Motivation for Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Adversarial Neuron Pruning Purifies Backdoored Deep Models |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Adversarial Regression with Doubly Non-negative Weighting Matrices |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Adversarial Reweighting for Partial Domain Adaptation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Adversarial Robustness of Streaming Algorithms through Importance Sampling |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Adversarial Robustness with Non-uniform Perturbations |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Adversarial Robustness with Semi-Infinite Constrained Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Adversarial Robustness without Adversarial Training: A Teacher-Guided Curriculum Learning Approach |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Adversarial Teacher-Student Representation Learning for Domain Generalization |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Adversarial Training Helps Transfer Learning via Better Representations |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Adversarially Robust 3D Point Cloud Recognition Using Self-Supervisions |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Adversarially Robust Change Point Detection |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Adversarially robust learning for security-constrained optimal power flow |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Agent Modelling under Partial Observability for Deep Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Agnostic Reinforcement Learning with Low-Rank MDPs and Rich Observations |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Algorithmic Instabilities of Accelerated Gradient Descent |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Algorithmic stability and generalization of an unsupervised feature selection algorithm |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Alias-Free Generative Adversarial Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Align before Fuse: Vision and Language Representation Learning with Momentum Distillation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Aligned Structured Sparsity Learning for Efficient Image Super-Resolution |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Aligning Pretraining for Detection via Object-Level Contrastive Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Aligning Silhouette Topology for Self-Adaptive 3D Human Pose Recovery |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Alignment Attention by Matching Key and Query Distributions |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| All Tokens Matter: Token Labeling for Training Better Vision Transformers |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Amortized Synthesis of Constrained Configurations Using a Differentiable Surrogate |
❌ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
3 |
| Amortized Variational Inference for Simple Hierarchical Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| An Analysis of Constant Step Size SGD in the Non-convex Regime: Asymptotic Normality and Bias |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| An Axiomatic Theory of Provably-Fair Welfare-Centric Machine Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| An Efficient Pessimistic-Optimistic Algorithm for Stochastic Linear Bandits with General Constraints |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| An Efficient Transfer Learning Framework for Multiagent Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| An Empirical Investigation of Domain Generalization with Empirical Risk Minimizers |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| An Empirical Study of Adder Neural Networks for Object Detection |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| An Even More Optimal Stochastic Optimization Algorithm: Minibatching and Interpolation Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| An Exact Characterization of the Generalization Error for the Gibbs Algorithm |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| An Exponential Improvement on the Memorization Capacity of Deep Threshold Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| An Exponential Lower Bound for Linearly Realizable MDP with Constant Suboptimality Gap |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| An Image is Worth More Than a Thousand Words: Towards Disentanglement in The Wild |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| An Improved Analysis and Rates for Variance Reduction under Without-replacement Sampling Orders |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| An Improved Analysis of Gradient Tracking for Decentralized Machine Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| An Information-theoretic Approach to Distribution Shifts |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| An Online Method for A Class of Distributionally Robust Optimization with Non-convex Objectives |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| An Online Riemannian PCA for Stochastic Canonical Correlation Analysis |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| An Uncertainty Principle is a Price of Privacy-Preserving Microdata |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| An analysis of Ermakov-Zolotukhin quadrature using kernels |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| An online passive-aggressive algorithm for difference-of-squares classification |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Analogous to Evolutionary Algorithm: Designing a Unified Sequence Model |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Analysis of Sensing Spectral for Signal Recovery under a Generalized Linear Model |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Analysis of one-hidden-layer neural networks via the resolvent method |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Analytic Insights into Structure and Rank of Neural Network Hessian Maps |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Analytic Study of Families of Spurious Minima in Two-Layer ReLU Neural Networks: A Tale of Symmetry II |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Analytical Study of Momentum-Based Acceleration Methods in Paradigmatic High-Dimensional Non-Convex Problems |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Analyzing the Confidentiality of Undistillable Teachers in Knowledge Distillation |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Analyzing the Generalization Capability of SGLD Using Properties of Gaussian Channels |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Answering Complex Causal Queries With the Maximum Causal Set Effect |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Anti-Backdoor Learning: Training Clean Models on Poisoned Data |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Antipodes of Label Differential Privacy: PATE and ALIBI |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Approximate Decomposable Submodular Function Minimization for Cardinality-Based Components |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Approximate optimization of convex functions with outlier noise |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Approximating the Permanent with Deep Rejection Sampling |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Arbitrary Conditional Distributions with Energy |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Are My Deep Learning Systems Fair? An Empirical Study of Fixed-Seed Training |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Are Transformers more robust than CNNs? |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Argmax Flows and Multinomial Diffusion: Learning Categorical Distributions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Artistic Style Transfer with Internal-external Learning and Contrastive Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Assessing Fairness in the Presence of Missing Data |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Associating Objects with Transformers for Video Object Segmentation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Associative Memories via Predictive Coding |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Asymptotically Best Causal Effect Identification with Multi-Armed Bandits |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Asymptotically Exact Error Characterization of Offline Policy Evaluation with Misspecified Linear Models |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Asymptotics of representation learning in finite Bayesian neural networks |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
3 |
| Asymptotics of the Bootstrap via Stability with Applications to Inference with Model Selection |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Asynchronous Decentralized Online Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Asynchronous Decentralized SGD with Quantized and Local Updates |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Asynchronous Stochastic Optimization Robust to Arbitrary Delays |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Attention Approximates Sparse Distributed Memory |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Attention Bottlenecks for Multimodal Fusion |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Attention over Learned Object Embeddings Enables Complex Visual Reasoning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Auditing Black-Box Prediction Models for Data Minimization Compliance |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| AugMax: Adversarial Composition of Random Augmentations for Robust Training |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Augmented Shortcuts for Vision Transformers |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Auto-Encoding Knowledge Graph for Unsupervised Medical Report Generation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| AutoBalance: Optimized Loss Functions for Imbalanced Data |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| AutoGEL: An Automated Graph Neural Network with Explicit Link Information |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Autobahn: Automorphism-based Graph Neural Nets |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Autoformer: Decomposition Transformers with Auto-Correlation for Long-Term Series Forecasting |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Automated Discovery of Adaptive Attacks on Adversarial Defenses |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Automated Dynamic Mechanism Design |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Automatic Data Augmentation for Generalization in Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Automatic Symmetry Discovery with Lie Algebra Convolutional Network |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Automatic Unsupervised Outlier Model Selection |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Automatic and Harmless Regularization with Constrained and Lexicographic Optimization: A Dynamic Barrier Approach |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Automorphic Equivalence-aware Graph Neural Network |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Autonomous Reinforcement Learning via Subgoal Curricula |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Average-Reward Learning and Planning with Options |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Averaging on the Bures-Wasserstein manifold: dimension-free convergence of gradient descent |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| BARTScore: Evaluating Generated Text as Text Generation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| BAST: Bayesian Additive Regression Spanning Trees for Complex Constrained Domain |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| BCD Nets: Scalable Variational Approaches for Bayesian Causal Discovery |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| BCORLE($\lambda$): An Offline Reinforcement Learning and Evaluation Framework for Coupons Allocation in E-commerce Market |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| BNS: Building Network Structures Dynamically for Continual Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Baby Intuitions Benchmark (BIB): Discerning the goals, preferences, and actions of others |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Backdoor Attack with Imperceptible Input and Latent Modification |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Backward-Compatible Prediction Updates: A Probabilistic Approach |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Balanced Chamfer Distance as a Comprehensive Metric for Point Cloud Completion |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Baleen: Robust Multi-Hop Reasoning at Scale via Condensed Retrieval |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Bandit Learning with Delayed Impact of Actions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Bandit Phase Retrieval |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Bandit Quickest Changepoint Detection |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Bandits with Knapsacks beyond the Worst Case |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Bandits with many optimal arms |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Batch Active Learning at Scale |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Batch Multi-Fidelity Bayesian Optimization with Deep Auto-Regressive Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Batch Normalization Orthogonalizes Representations in Deep Random Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| BatchQuant: Quantized-for-all Architecture Search with Robust Quantizer |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Batched Thompson Sampling |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| BayesIMP: Uncertainty Quantification for Causal Data Fusion |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Bayesian Adaptation for Covariate Shift |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Bayesian Bellman Operators |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Bayesian Optimization of Function Networks |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Bayesian Optimization with High-Dimensional Outputs |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Bayesian decision-making under misspecified priors with applications to meta-learning |
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❌ |
❌ |
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❌ |
✅ |
1 |
| Be Confident! Towards Trustworthy Graph Neural Networks via Confidence Calibration |
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❌ |
✅ |
✅ |
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3 |
| Behavior From the Void: Unsupervised Active Pre-Training |
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❌ |
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✅ |
3 |
| Believe What You See: Implicit Constraint Approach for Offline Multi-Agent Reinforcement Learning |
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✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| Bellman Eluder Dimension: New Rich Classes of RL Problems, and Sample-Efficient Algorithms |
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❌ |
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❌ |
1 |
| Bellman-consistent Pessimism for Offline Reinforcement Learning |
✅ |
❌ |
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❌ |
❌ |
❌ |
1 |
| Beltrami Flow and Neural Diffusion on Graphs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Benign Overfitting in Multiclass Classification: All Roads Lead to Interpolation |
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✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| BernNet: Learning Arbitrary Graph Spectral Filters via Bernstein Approximation |
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✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Best of Both Worlds: Practical and Theoretically Optimal Submodular Maximization in Parallel |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Best-case lower bounds in online learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Beta-CROWN: Efficient Bound Propagation with Per-neuron Split Constraints for Neural Network Robustness Verification |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Better Algorithms for Individually Fair $k$-Clustering |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Better Safe Than Sorry: Preventing Delusive Adversaries with Adversarial Training |
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✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy to Game |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Beyond Bandit Feedback in Online Multiclass Classification |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Beyond BatchNorm: Towards a Unified Understanding of Normalization in Deep Learning |
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❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Beyond Pinball Loss: Quantile Methods for Calibrated Uncertainty Quantification |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Beyond Smoothness: Incorporating Low-Rank Analysis into Nonparametric Density Estimation |
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❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Beyond Tikhonov: faster learning with self-concordant losses, via iterative regularization |
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❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Beyond Value-Function Gaps: Improved Instance-Dependent Regret Bounds for Episodic Reinforcement Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Beyond the Signs: Nonparametric Tensor Completion via Sign Series |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Bias Out-of-the-Box: An Empirical Analysis of Intersectional Occupational Biases in Popular Generative Language Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Bias and variance of the Bayesian-mean decoder |
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❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Biological learning in key-value memory networks |
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❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Black Box Probabilistic Numerics |
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✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
3 |
| BlendGAN: Implicitly GAN Blending for Arbitrary Stylized Face Generation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Blending Anti-Aliasing into Vision Transformer |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| BooVAE: Boosting Approach for Continual Learning of VAE |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| BooVI: Provably Efficient Bootstrapped Value Iteration |
✅ |
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❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Boost Neural Networks by Checkpoints |
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❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Boosted CVaR Classification |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Boosting with Multiple Sources |
✅ |
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✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Bootstrap Your Object Detector via Mixed Training |
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❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Bootstrapping the Error of Oja's Algorithm |
✅ |
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❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Bounds all around: training energy-based models with bidirectional bounds |
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✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Breaking the Dilemma of Medical Image-to-image Translation |
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✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Breaking the Linear Iteration Cost Barrier for Some Well-known Conditional Gradient Methods Using MaxIP Data-structures |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Breaking the Moments Condition Barrier: No-Regret Algorithm for Bandits with Super Heavy-Tailed Payoffs |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Breaking the Sample Complexity Barrier to Regret-Optimal Model-Free Reinforcement Learning |
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❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Breaking the centralized barrier for cross-device federated learning |
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✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Brick-by-Brick: Combinatorial Construction with Deep Reinforcement Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Bridging Explicit and Implicit Deep Generative Models via Neural Stein Estimators |
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✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Bridging Non Co-occurrence with Unlabeled In-the-wild Data for Incremental Object Detection |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Bridging Offline Reinforcement Learning and Imitation Learning: A Tale of Pessimism |
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❌ |
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❌ |
❌ |
❌ |
❌ |
1 |
| Bridging the Gap Between Practice and PAC-Bayes Theory in Few-Shot Meta-Learning |
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❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Bridging the Imitation Gap by Adaptive Insubordination |
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✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Bubblewrap: Online tiling and real-time flow prediction on neural manifolds |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| BulletTrain: Accelerating Robust Neural Network Training via Boundary Example Mining |
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✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| ByPE-VAE: Bayesian Pseudocoresets Exemplar VAE |
✅ |
✅ |
✅ |
❌ |
✅ |
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✅ |
5 |
| CAFE: Catastrophic Data Leakage in Vertical Federated Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| CAM-GAN: Continual Adaptation Modules for Generative Adversarial Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
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2 |
| CANITA: Faster Rates for Distributed Convex Optimization with Communication Compression |
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3 |
| CAPE: Encoding Relative Positions with Continuous Augmented Positional Embeddings |
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✅ |
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❌ |
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4 |
| CARMS: Categorical-Antithetic-REINFORCE Multi-Sample Gradient Estimator |
✅ |
✅ |
✅ |
❌ |
✅ |
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5 |
| CATs: Cost Aggregation Transformers for Visual Correspondence |
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4 |
| CBP: backpropagation with constraint on weight precision using a pseudo-Lagrange multiplier method |
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❌ |
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6 |
| CCVS: Context-aware Controllable Video Synthesis |
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4 |
| CHIP: CHannel Independence-based Pruning for Compact Neural Networks |
✅ |
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✅ |
❌ |
✅ |
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6 |
| CLDA: Contrastive Learning for Semi-Supervised Domain Adaptation |
❌ |
❌ |
✅ |
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❌ |
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✅ |
3 |
| CLIP-It! Language-Guided Video Summarization |
❌ |
❌ |
✅ |
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❌ |
❌ |
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3 |
| CO-PILOT: COllaborative Planning and reInforcement Learning On sub-Task curriculum |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| COCO-LM: Correcting and Contrasting Text Sequences for Language Model Pretraining |
❌ |
✅ |
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✅ |
✅ |
❌ |
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5 |
| COHESIV: Contrastive Object and Hand Embedding Segmentation In Video |
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❌ |
✅ |
✅ |
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❌ |
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4 |
| COMBO: Conservative Offline Model-Based Policy Optimization |
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✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| CROCS: Clustering and Retrieval of Cardiac Signals Based on Patient Disease Class, Sex, and Age |
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❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| CSDI: Conditional Score-based Diffusion Models for Probabilistic Time Series Imputation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Calibrating Predictions to Decisions: A Novel Approach to Multi-Class Calibration |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Calibration and Consistency of Adversarial Surrogate Losses |
❌ |
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❌ |
✅ |
1 |
| Can Information Flows Suggest Targets for Interventions in Neural Circuits? |
❌ |
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3 |
| Can Less be More? When Increasing-to-Balancing Label Noise Rates Considered Beneficial |
✅ |
✅ |
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❌ |
❌ |
❌ |
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3 |
| Can You Learn an Algorithm? Generalizing from Easy to Hard Problems with Recurrent Networks |
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3 |
| Can contrastive learning avoid shortcut solutions? |
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4 |
| Can fMRI reveal the representation of syntactic structure in the brain? |
❌ |
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4 |
| Can multi-label classification networks know what they don’t know? |
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5 |
| Can we globally optimize cross-validation loss? Quasiconvexity in ridge regression |
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❌ |
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4 |
| Can we have it all? On the Trade-off between Spatial and Adversarial Robustness of Neural Networks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
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5 |
| Canonical Capsules: Self-Supervised Capsules in Canonical Pose |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Capacity and Bias of Learned Geometric Embeddings for Directed Graphs |
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❌ |
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3 |
| Capturing implicit hierarchical structure in 3D biomedical images with self-supervised hyperbolic representations |
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❌ |
✅ |
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❌ |
❌ |
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3 |
| Cardinality constrained submodular maximization for random streams |
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❌ |
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4 |
| Cardinality-Regularized Hawkes-Granger Model |
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❌ |
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4 |
| Catalytic Role Of Noise And Necessity Of Inductive Biases In The Emergence Of Compositional Communication |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
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5 |
| Catch-A-Waveform: Learning to Generate Audio from a Single Short Example |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Causal Abstractions of Neural Networks |
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✅ |
❌ |
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❌ |
3 |
| Causal Bandits with Unknown Graph Structure |
✅ |
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1 |
| Causal Effect Inference for Structured Treatments |
✅ |
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✅ |
4 |
| Causal Identification with Matrix Equations |
✅ |
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❌ |
❌ |
❌ |
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1 |
| Causal Inference for Event Pairs in Multivariate Point Processes |
✅ |
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✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Causal Influence Detection for Improving Efficiency in Reinforcement Learning |
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❌ |
✅ |
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❌ |
❌ |
✅ |
2 |
| Causal Navigation by Continuous-time Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Causal-BALD: Deep Bayesian Active Learning of Outcomes to Infer Treatment-Effects from Observational Data |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Celebrating Diversity in Shared Multi-Agent Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Center Smoothing: Certified Robustness for Networks with Structured Outputs |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
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5 |
| CentripetalText: An Efficient Text Instance Representation for Scene Text Detection |
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✅ |
✅ |
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4 |
| Certifying Robustness to Programmable Data Bias in Decision Trees |
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✅ |
✅ |
❌ |
❌ |
❌ |
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3 |
| Challenges and Opportunities in High Dimensional Variational Inference |
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✅ |
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❌ |
✅ |
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3 |
| Change Point Detection via Multivariate Singular Spectrum Analysis |
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❌ |
✅ |
3 |
| Channel Permutations for N:M Sparsity |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Characterizing Generalization under Out-Of-Distribution Shifts in Deep Metric Learning |
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✅ |
✅ |
❌ |
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3 |
| Characterizing possible failure modes in physics-informed neural networks |
❌ |
✅ |
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❌ |
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2 |
| Characterizing the risk of fairwashing |
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✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Charting and Navigating the Space of Solutions for Recurrent Neural Networks |
❌ |
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❌ |
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❌ |
❌ |
✅ |
2 |
| Chasing Sparsity in Vision Transformers: An End-to-End Exploration |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Chebyshev-Cantelli PAC-Bayes-Bennett Inequality for the Weighted Majority Vote |
❌ |
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✅ |
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4 |
| Choose a Transformer: Fourier or Galerkin |
❌ |
✅ |
✅ |
❌ |
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4 |
| Circa: Stochastic ReLUs for Private Deep Learning |
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✅ |
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3 |
| Class-Disentanglement and Applications in Adversarial Detection and Defense |
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3 |
| Class-Incremental Learning via Dual Augmentation |
✅ |
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✅ |
❌ |
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4 |
| Class-agnostic Reconstruction of Dynamic Objects from Videos |
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✅ |
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4 |
| Clockwork Variational Autoencoders |
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4 |
| Closing the Gap: Tighter Analysis of Alternating Stochastic Gradient Methods for Bilevel Problems |
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3 |
| Closing the loop in medical decision support by understanding clinical decision-making: A case study on organ transplantation |
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❌ |
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2 |
| Clustering Effect of Adversarial Robust Models |
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✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Co-Adaptation of Algorithmic and Implementational Innovations in Inference-based Deep Reinforcement Learning |
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4 |
| Co-evolution Transformer for Protein Contact Prediction |
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5 |
| CoAtNet: Marrying Convolution and Attention for All Data Sizes |
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3 |
| CoFiNet: Reliable Coarse-to-fine Correspondences for Robust PointCloud Registration |
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3 |
| CoFrNets: Interpretable Neural Architecture Inspired by Continued Fractions |
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✅ |
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3 |
| Coarse-to-fine Animal Pose and Shape Estimation |
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5 |
| Cockpit: A Practical Debugging Tool for the Training of Deep Neural Networks |
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3 |
| CogView: Mastering Text-to-Image Generation via Transformers |
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4 |
| Collaborating with Humans without Human Data |
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1 |
| Collaborative Causal Discovery with Atomic Interventions |
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3 |
| Collaborative Learning in the Jungle (Decentralized, Byzantine, Heterogeneous, Asynchronous and Nonconvex Learning) |
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✅ |
5 |
| Collaborative Uncertainty in Multi-Agent Trajectory Forecasting |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Collapsed Variational Bounds for Bayesian Neural Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Combating Noise: Semi-supervised Learning by Region Uncertainty Quantification |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Combinatorial Optimization for Panoptic Segmentation: A Fully Differentiable Approach |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Combinatorial Pure Exploration with Bottleneck Reward Function |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Combiner: Full Attention Transformer with Sparse Computation Cost |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Combining Human Predictions with Model Probabilities via Confusion Matrices and Calibration |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Combining Latent Space and Structured Kernels for Bayesian Optimization over Combinatorial Spaces |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Combining Recurrent, Convolutional, and Continuous-time Models with Linear State Space Layers |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Communication-efficient SGD: From Local SGD to One-Shot Averaging |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Compacter: Efficient Low-Rank Hypercomplex Adapter Layers |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Complexity Lower Bounds for Nonconvex-Strongly-Concave Min-Max Optimization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Compositional Modeling of Nonlinear Dynamical Systems with ODE-based Random Features |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Compositional Reinforcement Learning from Logical Specifications |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Compositional Transformers for Scene Generation |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Comprehensive Knowledge Distillation with Causal Intervention |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Compressed Video Contrastive Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Compressing Neural Networks: Towards Determining the Optimal Layer-wise Decomposition |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Compressive Visual Representations |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Computer-Aided Design as Language |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| ConE: Cone Embeddings for Multi-Hop Reasoning over Knowledge Graphs |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Concentration inequalities under sub-Gaussian and sub-exponential conditions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Conditional Generation Using Polynomial Expansions |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Conditionally Parameterized, Discretization-Aware Neural Networks for Mesh-Based Modeling of Physical Systems |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Conditioning Sparse Variational Gaussian Processes for Online Decision-making |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Confidence-Aware Imitation Learning from Demonstrations with Varying Optimality |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Confident Anchor-Induced Multi-Source Free Domain Adaptation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Conflict-Averse Gradient Descent for Multi-task learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Conformal Bayesian Computation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Conformal Prediction using Conditional Histograms |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Conformal Time-series Forecasting |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Conic Blackwell Algorithm: Parameter-Free Convex-Concave Saddle-Point Solving |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Conservative Data Sharing for Multi-Task Offline Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Conservative Offline Distributional Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Consistency Regularization for Variational Auto-Encoders |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Consistent Estimation for PCA and Sparse Regression with Oblivious Outliers |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Consistent Non-Parametric Methods for Maximizing Robustness |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Constrained Optimization to Train Neural Networks on Critical and Under-Represented Classes |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Constrained Robust Submodular Partitioning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Constrained Two-step Look-Ahead Bayesian Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Container: Context Aggregation Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Contextual Recommendations and Low-Regret Cutting-Plane Algorithms |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Contextual Similarity Aggregation with Self-attention for Visual Re-ranking |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Continual Auxiliary Task Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Continual Learning via Local Module Composition |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Continual World: A Robotic Benchmark For Continual Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Continuized Accelerations of Deterministic and Stochastic Gradient Descents, and of Gossip Algorithms |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Continuous Doubly Constrained Batch Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Continuous Latent Process Flows |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Continuous Mean-Covariance Bandits |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Continuous vs. Discrete Optimization of Deep Neural Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Continuous-time edge modelling using non-parametric point processes |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Contrast and Mix: Temporal Contrastive Video Domain Adaptation with Background Mixing |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Contrastive Active Inference |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Contrastive Graph Poisson Networks: Semi-Supervised Learning with Extremely Limited Labels |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
2 |
| Contrastive Laplacian Eigenmaps |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Contrastive Learning for Neural Topic Model |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Contrastive Learning of Global and Local Video Representations |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Contrastive Reinforcement Learning of Symbolic Reasoning Domains |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Contrastively Disentangled Sequential Variational Autoencoder |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Control Variates for Slate Off-Policy Evaluation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Controllable and Compositional Generation with Latent-Space Energy-Based Models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Controlled Text Generation as Continuous Optimization with Multiple Constraints |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Controlling Neural Networks with Rule Representations |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Convergence Rates of Stochastic Gradient Descent under Infinite Noise Variance |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Convergence and Alignment of Gradient Descent with Random Backpropagation Weights |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Convergence of adaptive algorithms for constrained weakly convex optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Convex Polytope Trees |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Convex-Concave Min-Max Stackelberg Games |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Convolutional Normalization: Improving Deep Convolutional Network Robustness and Training |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Cooperative Stochastic Bandits with Asynchronous Agents and Constrained Feedback |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Coordinated Proximal Policy Optimization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Coresets for Classification – Simplified and Strengthened |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Coresets for Clustering with Missing Values |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Coresets for Decision Trees of Signals |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Coresets for Time Series Clustering |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Correlated Stochastic Block Models: Exact Graph Matching with Applications to Recovering Communities |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Corruption Robust Active Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| CorticalFlow: A Diffeomorphic Mesh Transformer Network for Cortical Surface Reconstruction |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Cortico-cerebellar networks as decoupling neural interfaces |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Counterbalancing Learning and Strategic Incentives in Allocation Markets |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Counterexample Guided RL Policy Refinement Using Bayesian Optimization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Counterfactual Explanations Can Be Manipulated |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Counterfactual Explanations in Sequential Decision Making Under Uncertainty |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Counterfactual Invariance to Spurious Correlations in Text Classification |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Counterfactual Maximum Likelihood Estimation for Training Deep Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Coupled Gradient Estimators for Discrete Latent Variables |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Coupled Segmentation and Edge Learning via Dynamic Graph Propagation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Covariance-Aware Private Mean Estimation Without Private Covariance Estimation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Credal Self-Supervised Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Credit Assignment Through Broadcasting a Global Error Vector |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Credit Assignment in Neural Networks through Deep Feedback Control |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Cross-modal Domain Adaptation for Cost-Efficient Visual Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Cross-view Geo-localization with Layer-to-Layer Transformer |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| CrypTen: Secure Multi-Party Computation Meets Machine Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Curriculum Design for Teaching via Demonstrations: Theory and Applications |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Curriculum Disentangled Recommendation with Noisy Multi-feedback |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Curriculum Learning for Vision-and-Language Navigation |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Curriculum Offline Imitating Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Cycle Self-Training for Domain Adaptation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| D2C: Diffusion-Decoding Models for Few-Shot Conditional Generation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| DECAF: Generating Fair Synthetic Data Using Causally-Aware Generative Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| DIB-R++: Learning to Predict Lighting and Material with a Hybrid Differentiable Renderer |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| DNN-based Topology Optimisation: Spatial Invariance and Neural Tangent Kernel |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| DOBF: A Deobfuscation Pre-Training Objective for Programming Languages |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| DOCTOR: A Simple Method for Detecting Misclassification Errors |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| DP-SSL: Towards Robust Semi-supervised Learning with A Few Labeled Samples |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| DRIVE: One-bit Distributed Mean Estimation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| DROID-SLAM: Deep Visual SLAM for Monocular, Stereo, and RGB-D Cameras |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| DRONE: Data-aware Low-rank Compression for Large NLP Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| DSelect-k: Differentiable Selection in the Mixture of Experts with Applications to Multi-Task Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Dangers of Bayesian Model Averaging under Covariate Shift |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Data Augmentation Can Improve Robustness |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Data Sharing and Compression for Cooperative Networked Control |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Data driven semi-supervised learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Data-Efficient GAN Training Beyond (Just) Augmentations: A Lottery Ticket Perspective |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Data-Efficient Instance Generation from Instance Discrimination |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Dataset Distillation with Infinitely Wide Convolutional Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| De-randomizing MCMC dynamics with the diffusion Stein operator |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Dealing With Misspecification In Fixed-Confidence Linear Top-m Identification |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Debiased Visual Question Answering from Feature and Sample Perspectives |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Deceive D: Adaptive Pseudo Augmentation for GAN Training with Limited Data |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Decentralized Learning in Online Queuing Systems |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Decentralized Q-learning in Zero-sum Markov Games |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
5 |
| Decision Transformer: Reinforcement Learning via Sequence Modeling |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Deconditional Downscaling with Gaussian Processes |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Deconvolutional Networks on Graph Data |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Decoupling the Depth and Scope of Graph Neural Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Decrypting Cryptic Crosswords: Semantically Complex Wordplay Puzzles as a Target for NLP |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| Deep Bandits Show-Off: Simple and Efficient Exploration with Deep Networks |
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❌ |
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3 |
| Deep Conditional Gaussian Mixture Model for Constrained Clustering |
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3 |
| Deep Contextual Video Compression |
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4 |
| Deep Explicit Duration Switching Models for Time Series |
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2 |
| Deep Extended Hazard Models for Survival Analysis |
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2 |
| Deep Extrapolation for Attribute-Enhanced Generation |
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❌ |
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5 |
| Deep Jump Learning for Off-Policy Evaluation in Continuous Treatment Settings |
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❌ |
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5 |
| Deep Learning Through the Lens of Example Difficulty |
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✅ |
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3 |
| Deep Learning on a Data Diet: Finding Important Examples Early in Training |
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4 |
| Deep Learning with Label Differential Privacy |
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❌ |
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3 |
| Deep Marching Tetrahedra: a Hybrid Representation for High-Resolution 3D Shape Synthesis |
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4 |
| Deep Markov Factor Analysis: Towards Concurrent Temporal and Spatial Analysis of fMRI Data |
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5 |
| Deep Molecular Representation Learning via Fusing Physical and Chemical Information |
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4 |
| Deep Networks Provably Classify Data on Curves |
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0 |
| Deep Neural Networks as Point Estimates for Deep Gaussian Processes |
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2 |
| Deep Proxy Causal Learning and its Application to Confounded Bandit Policy Evaluation |
✅ |
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❌ |
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6 |
| Deep Reinforcement Learning at the Edge of the Statistical Precipice |
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4 |
| Deep Residual Learning in Spiking Neural Networks |
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4 |
| Deep Self-Dissimilarities as Powerful Visual Fingerprints |
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2 |
| Deep Synoptic Monte-Carlo Planning in Reconnaissance Blind Chess |
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5 |
| Deep inference of latent dynamics with spatio-temporal super-resolution using selective backpropagation through time |
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4 |
| Deep learning is adaptive to intrinsic dimensionality of model smoothness in anisotropic Besov space |
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0 |
| DeepGEM: Generalized Expectation-Maximization for Blind Inversion |
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3 |
| DeepReduce: A Sparse-tensor Communication Framework for Federated Deep Learning |
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5 |
| DeepSITH: Efficient Learning via Decomposition of What and When Across Time Scales |
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4 |
| Deeply Shared Filter Bases for Parameter-Efficient Convolutional Neural Networks |
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4 |
| Deformable Butterfly: A Highly Structured and Sparse Linear Transform |
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❌ |
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5 |
| Delayed Gradient Averaging: Tolerate the Communication Latency for Federated Learning |
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❌ |
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5 |
| Delayed Propagation Transformer: A Universal Computation Engine towards Practical Control in Cyber-Physical Systems |
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✅ |
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❌ |
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3 |
| Demystifying and Generalizing BinaryConnect |
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3 |
| Denoising Normalizing Flow |
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4 |
| Dense Keypoints via Multiview Supervision |
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3 |
| Dense Unsupervised Learning for Video Segmentation |
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5 |
| Densely connected normalizing flows |
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❌ |
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5 |
| Derivative-Free Policy Optimization for Linear Risk-Sensitive and Robust Control Design: Implicit Regularization and Sample Complexity |
✅ |
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❌ |
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❌ |
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2 |
| Design of Experiments for Stochastic Contextual Linear Bandits |
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4 |
| Designing Counterfactual Generators using Deep Model Inversion |
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2 |
| Detecting Anomalous Event Sequences with Temporal Point Processes |
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❌ |
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6 |
| Detecting Errors and Estimating Accuracy on Unlabeled Data with Self-training Ensembles |
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❌ |
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6 |
| Detecting Individual Decision-Making Style: Exploring Behavioral Stylometry in Chess |
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4 |
| Detecting Moments and Highlights in Videos via Natural Language Queries |
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5 |
| Detecting and Adapting to Irregular Distribution Shifts in Bayesian Online Learning |
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2 |
| Determinantal point processes based on orthogonal polynomials for sampling minibatches in SGD |
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2 |
| DiBS: Differentiable Bayesian Structure Learning |
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4 |
| Differentiable Annealed Importance Sampling and the Perils of Gradient Noise |
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3 |
| Differentiable Equilibrium Computation with Decision Diagrams for Stackelberg Models of Combinatorial Congestion Games |
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❌ |
✅ |
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6 |
| Differentiable Learning Under Triage |
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6 |
| Differentiable Multiple Shooting Layers |
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2 |
| Differentiable Optimization of Generalized Nondecomposable Functions using Linear Programs |
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6 |
| Differentiable Quality Diversity |
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4 |
| Differentiable Simulation of Soft Multi-body Systems |
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4 |
| Differentiable Spike: Rethinking Gradient-Descent for Training Spiking Neural Networks |
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5 |
| Differentiable Spline Approximations |
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5 |
| Differentiable Synthesis of Program Architectures |
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6 |
| Differentiable Unsupervised Feature Selection based on a Gated Laplacian |
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5 |
| Differentiable rendering with perturbed optimizers |
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3 |
| Differential Privacy Dynamics of Langevin Diffusion and Noisy Gradient Descent |
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1 |
| Differential Privacy Over Riemannian Manifolds |
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2 |
| Differentially Private Empirical Risk Minimization under the Fairness Lens |
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3 |
| Differentially Private Federated Bayesian Optimization with Distributed Exploration |
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4 |
| Differentially Private Learning with Adaptive Clipping |
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5 |
| Differentially Private Model Personalization |
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2 |
| Differentially Private Multi-Armed Bandits in the Shuffle Model |
✅ |
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❌ |
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❌ |
❌ |
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1 |
| Differentially Private Sampling from Distributions |
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❌ |
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0 |
| Differentially Private Stochastic Optimization: New Results in Convex and Non-Convex Settings |
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❌ |
❌ |
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1 |
| Differentially Private n-gram Extraction |
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4 |
| Diffusion Models Beat GANs on Image Synthesis |
✅ |
✅ |
✅ |
❌ |
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❌ |
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4 |
| Diffusion Normalizing Flow |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
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4 |
| Diffusion Schrödinger Bridge with Applications to Score-Based Generative Modeling |
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✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Dimension-free empirical entropy estimation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Dimensionality Reduction for Wasserstein Barycenter |
✅ |
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✅ |
❌ |
❌ |
❌ |
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3 |
| Direct Multi-view Multi-person 3D Pose Estimation |
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✅ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Directed Graph Contrastive Learning |
✅ |
✅ |
✅ |
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5 |
| Directed Probabilistic Watershed |
✅ |
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✅ |
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❌ |
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4 |
| Directed Spectrum Measures Improve Latent Network Models Of Neural Populations |
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3 |
| Directional Message Passing on Molecular Graphs via Synthetic Coordinates |
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❌ |
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5 |
| Dirichlet Energy Constrained Learning for Deep Graph Neural Networks |
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3 |
| Discerning Decision-Making Process of Deep Neural Networks with Hierarchical Voting Transformation |
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2 |
| Discovering Dynamic Salient Regions for Spatio-Temporal Graph Neural Networks |
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4 |
| Discovering and Achieving Goals via World Models |
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2 |
| Discovery of Options via Meta-Learned Subgoals |
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3 |
| Discrete-Valued Neural Communication |
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3 |
| Disentangled Contrastive Learning on Graphs |
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3 |
| Disentangling Identifiable Features from Noisy Data with Structured Nonlinear ICA |
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4 |
| Disentangling the Roles of Curation, Data-Augmentation and the Prior in the Cold Posterior Effect |
❌ |
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2 |
| Disrupting Deep Uncertainty Estimation Without Harming Accuracy |
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4 |
| Dissecting the Diffusion Process in Linear Graph Convolutional Networks |
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5 |
| Distilling Image Classifiers in Object Detectors |
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5 |
| Distilling Meta Knowledge on Heterogeneous Graph for Illicit Drug Trafficker Detection on Social Media |
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4 |
| Distilling Object Detectors with Feature Richness |
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3 |
| Distilling Robust and Non-Robust Features in Adversarial Examples by Information Bottleneck |
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2 |
| Distributed Deep Learning In Open Collaborations |
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4 |
| Distributed Estimation with Multiple Samples per User: Sharp Rates and Phase Transition |
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0 |
| Distributed Machine Learning with Sparse Heterogeneous Data |
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2 |
| Distributed Principal Component Analysis with Limited Communication |
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3 |
| Distributed Saddle-Point Problems Under Data Similarity |
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5 |
| Distributed Zero-Order Optimization under Adversarial Noise |
✅ |
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1 |
| Distribution-free inference for regression: discrete, continuous, and in between |
❌ |
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❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Distributional Gradient Matching for Learning Uncertain Neural Dynamics Models |
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✅ |
❌ |
✅ |
✅ |
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5 |
| Distributional Reinforcement Learning for Multi-Dimensional Reward Functions |
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✅ |
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❌ |
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4 |
| Distributionally Robust Imitation Learning |
✅ |
✅ |
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❌ |
❌ |
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3 |
| Divergence Frontiers for Generative Models: Sample Complexity, Quantization Effects, and Frontier Integrals |
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✅ |
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2 |
| Diverse Message Passing for Attribute with Heterophily |
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3 |
| Diversity Enhanced Active Learning with Strictly Proper Scoring Rules |
✅ |
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❌ |
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6 |
| Diversity Matters When Learning From Ensembles |
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4 |
| Do Different Tracking Tasks Require Different Appearance Models? |
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4 |
| Do Input Gradients Highlight Discriminative Features? |
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3 |
| Do Neural Optimal Transport Solvers Work? A Continuous Wasserstein-2 Benchmark |
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3 |
| Do Transformers Really Perform Badly for Graph Representation? |
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4 |
| Do Vision Transformers See Like Convolutional Neural Networks? |
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3 |
| Do Wider Neural Networks Really Help Adversarial Robustness? |
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5 |
| Does Knowledge Distillation Really Work? |
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3 |
| Does Preprocessing Help Training Over-parameterized Neural Networks? |
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1 |
| Does enforcing fairness mitigate biases caused by subpopulation shift? |
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3 |
| Domain Adaptation with Invariant Representation Learning: What Transformations to Learn? |
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2 |
| Domain Invariant Representation Learning with Domain Density Transformations |
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5 |
| DominoSearch: Find layer-wise fine-grained N:M sparse schemes from dense neural networks |
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4 |
| Don’t Generate Me: Training Differentially Private Generative Models with Sinkhorn Divergence |
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4 |
| Double Machine Learning Density Estimation for Local Treatment Effects with Instruments |
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2 |
| Double/Debiased Machine Learning for Dynamic Treatment Effects |
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6 |
| Doubly Robust Thompson Sampling with Linear Payoffs |
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2 |
| Dr Jekyll & Mr Hyde: the strange case of off-policy policy updates |
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✅ |
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✅ |
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5 |
| Drawing Robust Scratch Tickets: Subnetworks with Inborn Robustness Are Found within Randomly Initialized Networks |
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3 |
| Drop, Swap, and Generate: A Self-Supervised Approach for Generating Neural Activity |
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4 |
| Drop-DTW: Aligning Common Signal Between Sequences While Dropping Outliers |
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4 |
| DropGNN: Random Dropouts Increase the Expressiveness of Graph Neural Networks |
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4 |
| Dual Adaptivity: A Universal Algorithm for Minimizing the Adaptive Regret of Convex Functions |
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❌ |
✅ |
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3 |
| Dual Parameterization of Sparse Variational Gaussian Processes |
✅ |
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✅ |
✅ |
✅ |
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7 |
| Dual Progressive Prototype Network for Generalized Zero-Shot Learning |
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✅ |
❌ |
✅ |
3 |
| Dual-stream Network for Visual Recognition |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| DualNet: Continual Learning, Fast and Slow |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Dueling Bandits with Adversarial Sleeping |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Dueling Bandits with Team Comparisons |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Duplex Sequence-to-Sequence Learning for Reversible Machine Translation |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Dynaboard: An Evaluation-As-A-Service Platform for Holistic Next-Generation Benchmarking |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
4 |
| Dynamic Analysis of Higher-Order Coordination in Neuronal Assemblies via De-Sparsified Orthogonal Matching Pursuit |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Dynamic Bottleneck for Robust Self-Supervised Exploration |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Dynamic COVID risk assessment accounting for community virus exposure from a spatial-temporal transmission model |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Dynamic Causal Bayesian Optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Dynamic Distillation Network for Cross-Domain Few-Shot Recognition with Unlabeled Data |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Dynamic Grained Encoder for Vision Transformers |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Dynamic Inference with Neural Interpreters |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Dynamic Neural Representational Decoders for High-Resolution Semantic Segmentation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Dynamic Normalization and Relay for Video Action Recognition |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Dynamic Resolution Network |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Dynamic Sasvi: Strong Safe Screening for Norm-Regularized Least Squares |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Dynamic Trace Estimation |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
3 |
| Dynamic Visual Reasoning by Learning Differentiable Physics Models from Video and Language |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Dynamic influence maximization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Dynamic population-based meta-learning for multi-agent communication with natural language |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| DynamicViT: Efficient Vision Transformers with Dynamic Token Sparsification |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Dynamical Wasserstein Barycenters for Time-series Modeling |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Dynamics of Stochastic Momentum Methods on Large-scale, Quadratic Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Dynamics-regulated kinematic policy for egocentric pose estimation |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| E(n) Equivariant Normalizing Flows |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| EDGE: Explaining Deep Reinforcement Learning Policies |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| EF21: A New, Simpler, Theoretically Better, and Practically Faster Error Feedback |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| EIGNN: Efficient Infinite-Depth Graph Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| ELLA: Exploration through Learned Language Abstraction |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Early Convolutions Help Transformers See Better |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Early-stopped neural networks are consistent |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Edge Representation Learning with Hypergraphs |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| EditGAN: High-Precision Semantic Image Editing |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
4 |
| Editing a classifier by rewriting its prediction rules |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Effective Meta-Regularization by Kernelized Proximal Regularization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Efficient Active Learning for Gaussian Process Classification by Error Reduction |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Efficient Algorithms for Learning Depth-2 Neural Networks with General ReLU Activations |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Efficient Bayesian network structure learning via local Markov boundary search |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Efficient Combination of Rematerialization and Offloading for Training DNNs |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Efficient Equivariant Network |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Efficient First-Order Contextual Bandits: Prediction, Allocation, and Triangular Discrimination |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient Generalization with Distributionally Robust Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Efficient Learning of Discrete-Continuous Computation Graphs |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Efficient Mirror Descent Ascent Methods for Nonsmooth Minimax Problems |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Efficient Neural Network Training via Forward and Backward Propagation Sparsification |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| Efficient Online Estimation of Causal Effects by Deciding What to Observe |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Efficient Statistical Assessment of Neural Network Corruption Robustness |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Efficient Training of Retrieval Models using Negative Cache |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Efficient Training of Visual Transformers with Small Datasets |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Efficient Truncated Linear Regression with Unknown Noise Variance |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
3 |
| Efficient and Accurate Gradients for Neural SDEs |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Efficient and Local Parallel Random Walks |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Efficient constrained sampling via the mirror-Langevin algorithm |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Efficient hierarchical Bayesian inference for spatio-temporal regression models in neuroimaging |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Efficient methods for Gaussian Markov random fields under sparse linear constraints |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
5 |
| Efficiently Identifying Task Groupings for Multi-Task Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Efficiently Learning One Hidden Layer ReLU Networks From Queries |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Embedding Principle of Loss Landscape of Deep Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Emergent Communication of Generalizations |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Emergent Communication under Varying Sizes and Connectivities |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Emergent Discrete Communication in Semantic Spaces |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Enabling Fast Differentially Private SGD via Just-in-Time Compilation and Vectorization |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Encoding Robustness to Image Style via Adversarial Feature Perturbations |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Encoding Spatial Distribution of Convolutional Features for Texture Representation |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| End-to-End Training of Multi-Document Reader and Retriever for Open-Domain Question Answering |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| End-to-End Weak Supervision |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| End-to-end Multi-modal Video Temporal Grounding |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| End-to-end reconstruction meets data-driven regularization for inverse problems |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Ensembling Graph Predictions for AMR Parsing |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Entropic Desired Dynamics for Intrinsic Control |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Entropy-based adaptive Hamiltonian Monte Carlo |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Environment Generation for Zero-Shot Compositional Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Episodic Multi-agent Reinforcement Learning with Curiosity-driven Exploration |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Equilibrium Refinement for the Age of Machines: The One-Sided Quasi-Perfect Equilibrium |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Equilibrium and non-Equilibrium regimes in the learning of Restricted Boltzmann Machines |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Equivariant Manifold Flows |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Error Compensated Distributed SGD Can Be Accelerated |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| ErrorCompensatedX: error compensation for variance reduced algorithms |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Escape saddle points by a simple gradient-descent based algorithm |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
5 |
| Escaping Saddle Points with Compressed SGD |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Estimating High Order Gradients of the Data Distribution by Denoising |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Estimating Multi-cause Treatment Effects via Single-cause Perturbation |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
4 |
| Estimating the Long-Term Effects of Novel Treatments |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Estimating the Unique Information of Continuous Variables |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Evaluating Efficient Performance Estimators of Neural Architectures |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Evaluating Gradient Inversion Attacks and Defenses in Federated Learning |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Evaluating State-of-the-Art Classification Models Against Bayes Optimality |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Evaluating model performance under worst-case subpopulations |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Evaluation of Human-AI Teams for Learned and Rule-Based Agents in Hanabi |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Even your Teacher Needs Guidance: Ground-Truth Targets Dampen Regularization Imposed by Self-Distillation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Evidential Softmax for Sparse Multimodal Distributions in Deep Generative Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| EvoGrad: Efficient Gradient-Based Meta-Learning and Hyperparameter Optimization |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Evolution Gym: A Large-Scale Benchmark for Evolving Soft Robots |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| Exact Privacy Guarantees for Markov Chain Implementations of the Exponential Mechanism with Artificial Atoms |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Exact marginal prior distributions of finite Bayesian neural networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Excess Capacity and Backdoor Poisoning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Explainable Semantic Space by Grounding Language to Vision with Cross-Modal Contrastive Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
4 |
| Explaining Hyperparameter Optimization via Partial Dependence Plots |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Explaining Latent Representations with a Corpus of Examples |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Explaining heterogeneity in medial entorhinal cortex with task-driven neural networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Explanation-based Data Augmentation for Image Classification |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
4 |
| Explicable Reward Design for Reinforcement Learning Agents |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
4 |
| Explicit loss asymptotics in the gradient descent training of neural networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Exploiting Chain Rule and Bayes' Theorem to Compare Probability Distributions |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Exploiting Data Sparsity in Secure Cross-Platform Social Recommendation |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Exploiting Domain-Specific Features to Enhance Domain Generalization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Exploiting Local Convergence of Quasi-Newton Methods Globally: Adaptive Sample Size Approach |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Exploiting Opponents Under Utility Constraints in Sequential Games |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
3 |
| Exploiting a Zoo of Checkpoints for Unseen Tasks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Exploiting the Intrinsic Neighborhood Structure for Source-free Domain Adaptation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Exploration-Exploitation in Multi-Agent Competition: Convergence with Bounded Rationality |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Exploring Architectural Ingredients of Adversarially Robust Deep Neural Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Exploring Cross-Video and Cross-Modality Signals for Weakly-Supervised Audio-Visual Video Parsing |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
4 |
| Exploring Forensic Dental Identification with Deep Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Exploring Social Posterior Collapse in Variational Autoencoder for Interaction Modeling |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Exploring the Limits of Out-of-Distribution Detection |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Exponential Bellman Equation and Improved Regret Bounds for Risk-Sensitive Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Exponential Graph is Provably Efficient for Decentralized Deep Training |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Exponential Separation between Two Learning Models and Adversarial Robustness |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Extending Lagrangian and Hamiltonian Neural Networks with Differentiable Contact Models |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Extracting Deformation-Aware Local Features by Learning to Deform |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| FACMAC: Factored Multi-Agent Centralised Policy Gradients |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| FINE Samples for Learning with Noisy Labels |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| FL-WBC: Enhancing Robustness against Model Poisoning Attacks in Federated Learning from a Client Perspective |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| FLEX: Unifying Evaluation for Few-Shot NLP |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| FMMformer: Efficient and Flexible Transformer via Decomposed Near-field and Far-field Attention |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Factored Policy Gradients: Leveraging Structure for Efficient Learning in MOMDPs |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Fair Algorithms for Multi-Agent Multi-Armed Bandits |
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1 |
| Fair Classification with Adversarial Perturbations |
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4 |
| Fair Clustering Under a Bounded Cost |
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4 |
| Fair Exploration via Axiomatic Bargaining |
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1 |
| Fair Scheduling for Time-dependent Resources |
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2 |
| Fair Sequential Selection Using Supervised Learning Models |
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3 |
| Fair Sortition Made Transparent |
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1 |
| Fair Sparse Regression with Clustering: An Invex Relaxation for a Combinatorial Problem |
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2 |
| Fairness in Ranking under Uncertainty |
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✅ |
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2 |
| Fairness via Representation Neutralization |
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✅ |
✅ |
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4 |
| Fast Abductive Learning by Similarity-based Consistency Optimization |
✅ |
✅ |
✅ |
❌ |
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5 |
| Fast Algorithms for $L_\infty$-constrained S-rectangular Robust MDPs |
✅ |
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✅ |
❌ |
✅ |
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5 |
| Fast Approximate Dynamic Programming for Infinite-Horizon Markov Decision Processes |
✅ |
✅ |
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3 |
| Fast Approximation of the Sliced-Wasserstein Distance Using Concentration of Random Projections |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Fast Axiomatic Attribution for Neural Networks |
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✅ |
✅ |
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4 |
| Fast Bayesian Inference for Gaussian Cox Processes via Path Integral Formulation |
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✅ |
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✅ |
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3 |
| Fast Certified Robust Training with Short Warmup |
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✅ |
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❌ |
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4 |
| Fast Doubly-Adaptive MCMC to Estimate the Gibbs Partition Function with Weak Mixing Time Bounds |
✅ |
✅ |
✅ |
❌ |
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4 |
| Fast Extra Gradient Methods for Smooth Structured Nonconvex-Nonconcave Minimax Problems |
✅ |
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❌ |
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2 |
| Fast Federated Learning in the Presence of Arbitrary Device Unavailability |
✅ |
✅ |
✅ |
❌ |
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5 |
| Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
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6 |
| Fast Multi-Resolution Transformer Fine-tuning for Extreme Multi-label Text Classification |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Fast Policy Extragradient Methods for Competitive Games with Entropy Regularization |
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❌ |
❌ |
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2 |
| Fast Projection onto the Capped Simplex with Applications to Sparse Regression in Bioinformatics |
✅ |
✅ |
✅ |
❌ |
✅ |
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5 |
| Fast Pure Exploration via Frank-Wolfe |
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✅ |
❌ |
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3 |
| Fast Routing under Uncertainty: Adaptive Learning in Congestion Games via Exponential Weights |
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❌ |
❌ |
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3 |
| Fast Training Method for Stochastic Compositional Optimization Problems |
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✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fast Training of Neural Lumigraph Representations using Meta Learning |
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✅ |
✅ |
❌ |
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4 |
| Fast Tucker Rank Reduction for Non-Negative Tensors Using Mean-Field Approximation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fast and Memory Efficient Differentially Private-SGD via JL Projections |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
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6 |
| Fast and accurate randomized algorithms for low-rank tensor decompositions |
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4 |
| Fast rates for prediction with limited expert advice |
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❌ |
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1 |
| FastCorrect: Fast Error Correction with Edit Alignment for Automatic Speech Recognition |
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✅ |
✅ |
✅ |
✅ |
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5 |
| Faster Algorithms and Constant Lower Bounds for the Worst-Case Expected Error |
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✅ |
❌ |
❌ |
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2 |
| Faster Directional Convergence of Linear Neural Networks under Spherically Symmetric Data |
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❌ |
❌ |
❌ |
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1 |
| Faster Matchings via Learned Duals |
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✅ |
✅ |
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4 |
| Faster Neural Network Training with Approximate Tensor Operations |
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✅ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Faster Non-asymptotic Convergence for Double Q-learning |
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❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Faster proximal algorithms for matrix optimization using Jacobi-based eigenvalue methods |
✅ |
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❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Fault-Tolerant Federated Reinforcement Learning with Theoretical Guarantee |
✅ |
✅ |
✅ |
❌ |
✅ |
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5 |
| FedDR – Randomized Douglas-Rachford Splitting Algorithms for Nonconvex Federated Composite Optimization |
✅ |
❌ |
✅ |
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✅ |
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4 |
| Federated Graph Classification over Non-IID Graphs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Federated Hyperparameter Tuning: Challenges, Baselines, and Connections to Weight-Sharing |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Federated Linear Contextual Bandits |
✅ |
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3 |
| Federated Multi-Task Learning under a Mixture of Distributions |
✅ |
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✅ |
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5 |
| Federated Reconstruction: Partially Local Federated Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Federated Split Task-Agnostic Vision Transformer for COVID-19 CXR Diagnosis |
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✅ |
✅ |
✅ |
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6 |
| Federated-EM with heterogeneity mitigation and variance reduction |
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3 |
| Few-Round Learning for Federated Learning |
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5 |
| Few-Shot Data-Driven Algorithms for Low Rank Approximation |
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4 |
| Few-Shot Object Detection via Association and DIscrimination |
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❌ |
✅ |
✅ |
✅ |
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4 |
| Few-Shot Segmentation via Cycle-Consistent Transformer |
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❌ |
✅ |
✅ |
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4 |
| Finding Bipartite Components in Hypergraphs |
✅ |
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❌ |
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6 |
| Finding Discriminative Filters for Specific Degradations in Blind Super-Resolution |
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5 |
| Finding Optimal Tangent Points for Reducing Distortions of Hard-label Attacks |
✅ |
✅ |
✅ |
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❌ |
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5 |
| Finding Regions of Heterogeneity in Decision-Making via Expected Conditional Covariance |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Fine-Grained Neural Network Explanation by Identifying Input Features with Predictive Information |
❌ |
✅ |
✅ |
❌ |
✅ |
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4 |
| Fine-Grained Zero-Shot Learning with DNA as Side Information |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
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5 |
| Fine-grained Generalization Analysis of Inductive Matrix Completion |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Finite Sample Analysis of Average-Reward TD Learning and $Q$-Learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
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2 |
| Finite-Sample Analysis of Off-Policy TD-Learning via Generalized Bellman Operators |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
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1 |
| Fitting summary statistics of neural data with a differentiable spiking network simulator |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Fixes That Fail: Self-Defeating Improvements in Machine-Learning Systems |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| FjORD: Fair and Accurate Federated Learning under heterogeneous targets with Ordered Dropout |
✅ |
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✅ |
❌ |
✅ |
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5 |
| Flattening Sharpness for Dynamic Gradient Projection Memory Benefits Continual Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| FlexMatch: Boosting Semi-Supervised Learning with Curriculum Pseudo Labeling |
✅ |
✅ |
✅ |
✅ |
✅ |
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6 |
| Flexible Option Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
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4 |
| Flow Network based Generative Models for Non-Iterative Diverse Candidate Generation |
❌ |
✅ |
✅ |
❌ |
✅ |
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4 |
| Focal Attention for Long-Range Interactions in Vision Transformers |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| For high-dimensional hierarchical models, consider exchangeability of effects across covariates instead of across datasets |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
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3 |
| Formalizing Generalization and Adversarial Robustness of Neural Networks to Weight Perturbations |
❌ |
❌ |
✅ |
❌ |
✅ |
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3 |
| Formalizing the Generalization-Forgetting Trade-off in Continual Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
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❌ |
5 |
| Forster Decomposition and Learning Halfspaces with Noise |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
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1 |
| Foundations of Symbolic Languages for Model Interpretability |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Fractal Structure and Generalization Properties of Stochastic Optimization Algorithms |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Framing RNN as a kernel method: A neural ODE approach |
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1 |
| From Canonical Correlation Analysis to Self-supervised Graph Neural Networks |
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6 |
| From Optimality to Robustness: Adaptive Re-Sampling Strategies in Stochastic Bandits |
✅ |
✅ |
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❌ |
❌ |
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3 |
| From global to local MDI variable importances for random forests and when they are Shapley values |
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✅ |
✅ |
❌ |
❌ |
❌ |
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3 |
| Functional Neural Networks for Parametric Image Restoration Problems |
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❌ |
✅ |
✅ |
❌ |
❌ |
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3 |
| Functional Regularization for Reinforcement Learning via Learned Fourier Features |
✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| Functional Variational Inference based on Stochastic Process Generators |
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✅ |
✅ |
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4 |
| Functionally Regionalized Knowledge Transfer for Low-resource Drug Discovery |
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✅ |
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4 |
| Fuzzy Clustering with Similarity Queries |
✅ |
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❌ |
❌ |
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1 |
| G-PATE: Scalable Differentially Private Data Generator via Private Aggregation of Teacher Discriminators |
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✅ |
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❌ |
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5 |
| GENESIS-V2: Inferring Unordered Object Representations without Iterative Refinement |
✅ |
✅ |
✅ |
❌ |
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4 |
| GRIN: Generative Relation and Intention Network for Multi-agent Trajectory Prediction |
❌ |
✅ |
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✅ |
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5 |
| Garment4D: Garment Reconstruction from Point Cloud Sequences |
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✅ |
✅ |
❌ |
❌ |
❌ |
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2 |
| Gauge Equivariant Transformer |
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✅ |
❌ |
❌ |
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1 |
| Gaussian Kernel Mixture Network for Single Image Defocus Deblurring |
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✅ |
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5 |
| GemNet: Universal Directional Graph Neural Networks for Molecules |
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✅ |
✅ |
✅ |
❌ |
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4 |
| General Low-rank Matrix Optimization: Geometric Analysis and Sharper Bounds |
✅ |
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❌ |
❌ |
❌ |
❌ |
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1 |
| General Nonlinearities in SO(2)-Equivariant CNNs |
❌ |
✅ |
✅ |
❌ |
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4 |
| Generalizable Imitation Learning from Observation via Inferring Goal Proximity |
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3 |
| Generalizable Multi-linear Attention Network |
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✅ |
✅ |
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3 |
| Generalization Bounds For Meta-Learning: An Information-Theoretic Analysis |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| Generalization Bounds for (Wasserstein) Robust Optimization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Generalization Bounds for Graph Embedding Using Negative Sampling: Linear vs Hyperbolic |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Generalization Bounds for Meta-Learning via PAC-Bayes and Uniform Stability |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Generalization Error Rates in Kernel Regression: The Crossover from the Noiseless to Noisy Regime |
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✅ |
❌ |
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3 |
| Generalization Guarantee of SGD for Pairwise Learning |
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❌ |
❌ |
❌ |
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1 |
| Generalization of Model-Agnostic Meta-Learning Algorithms: Recurring and Unseen Tasks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
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1 |
| Generalized DataWeighting via Class-Level Gradient Manipulation |
✅ |
✅ |
✅ |
✅ |
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5 |
| Generalized Depthwise-Separable Convolutions for Adversarially Robust and Efficient Neural Networks |
✅ |
✅ |
✅ |
❌ |
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5 |
| Generalized Jensen-Shannon Divergence Loss for Learning with Noisy Labels |
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✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| Generalized Linear Bandits with Local Differential Privacy |
✅ |
✅ |
✅ |
❌ |
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❌ |
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4 |
| Generalized Proximal Policy Optimization with Sample Reuse |
✅ |
✅ |
✅ |
❌ |
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4 |
| Generalized Shape Metrics on Neural Representations |
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❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Generalized and Discriminative Few-Shot Object Detection via SVD-Dictionary Enhancement |
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❌ |
✅ |
✅ |
❌ |
❌ |
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3 |
| Generating High-Quality Explanations for Navigation in Partially-Revealed Environments |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
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5 |
| Generative Occupancy Fields for 3D Surface-Aware Image Synthesis |
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✅ |
✅ |
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4 |
| Generative vs. Discriminative: Rethinking The Meta-Continual Learning |
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✅ |
✅ |
❌ |
❌ |
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4 |
| Generic Neural Architecture Search via Regression |
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✅ |
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❌ |
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4 |
| GeoMol: Torsional Geometric Generation of Molecular 3D Conformer Ensembles |
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✅ |
✅ |
✅ |
❌ |
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4 |
| Geometry Processing with Neural Fields |
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✅ |
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❌ |
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3 |
| Glance-and-Gaze Vision Transformer |
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❌ |
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4 |
| Global Convergence to Local Minmax Equilibrium in Classes of Nonconvex Zero-Sum Games |
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❌ |
❌ |
❌ |
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1 |
| Global Convergence of Gradient Descent for Asymmetric Low-Rank Matrix Factorization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Global Convergence of Online Optimization for Nonlinear Model Predictive Control |
✅ |
✅ |
❌ |
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❌ |
✅ |
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4 |
| Global Filter Networks for Image Classification |
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6 |
| Global-aware Beam Search for Neural Abstractive Summarization |
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4 |
| Goal-Aware Cross-Entropy for Multi-Target Reinforcement Learning |
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✅ |
❌ |
❌ |
❌ |
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✅ |
2 |
| Going Beyond Linear RL: Sample Efficient Neural Function Approximation |
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1 |
| Going Beyond Linear Transformers with Recurrent Fast Weight Programmers |
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✅ |
✅ |
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❌ |
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5 |
| Gone Fishing: Neural Active Learning with Fisher Embeddings |
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✅ |
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5 |
| Good Classification Measures and How to Find Them |
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❌ |
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2 |
| Grad2Task: Improved Few-shot Text Classification Using Gradients for Task Representation |
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❌ |
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4 |
| GradInit: Learning to Initialize Neural Networks for Stable and Efficient Training |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Gradient Descent on Two-layer Nets: Margin Maximization and Simplicity Bias |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Gradient Driven Rewards to Guarantee Fairness in Collaborative Machine Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Gradient Inversion with Generative Image Prior |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Gradient Starvation: A Learning Proclivity in Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Gradient-Free Adversarial Training Against Image Corruption for Learning-based Steering |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Gradient-based Editing of Memory Examples for Online Task-free Continual Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Gradient-based Hyperparameter Optimization Over Long Horizons |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Gradual Domain Adaptation without Indexed Intermediate Domains |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Grammar-Based Grounded Lexicon Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Graph Adversarial Self-Supervised Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Graph Differentiable Architecture Search with Structure Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Graph Neural Networks with Adaptive Residual |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Graph Neural Networks with Local Graph Parameters |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Graph Posterior Network: Bayesian Predictive Uncertainty for Node Classification |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| GraphFormers: GNN-nested Transformers for Representation Learning on Textual Graph |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Graphical Models in Heavy-Tailed Markets |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Greedy Approximation Algorithms for Active Sequential Hypothesis Testing |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Greedy and Random Quasi-Newton Methods with Faster Explicit Superlinear Convergence |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Grounding Representation Similarity Through Statistical Testing |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Grounding Spatio-Temporal Language with Transformers |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Grounding inductive biases in natural images: invariance stems from variations in data |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Group Equivariant Subsampling |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| H-NeRF: Neural Radiance Fields for Rendering and Temporal Reconstruction of Humans in Motion |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| HNPE: Leveraging Global Parameters for Neural Posterior Estimation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| HRFormer: High-Resolution Vision Transformer for Dense Predict |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| HSVA: Hierarchical Semantic-Visual Adaptation for Zero-Shot Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Habitat 2.0: Training Home Assistants to Rearrange their Habitat |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Hamiltonian Dynamics with Non-Newtonian Momentum for Rapid Sampling |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Handling Long-tailed Feature Distribution in AdderNets |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Hard-Attention for Scalable Image Classification |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Hardware-adaptive Efficient Latency Prediction for NAS via Meta-Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Hash Layers For Large Sparse Models |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Heavy Ball Momentum for Conditional Gradient |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Heavy Ball Neural Ordinary Differential Equations |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Heavy Tails in SGD and Compressibility of Overparametrized Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Hessian Eigenspectra of More Realistic Nonlinear Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Heterogeneous Multi-player Multi-armed Bandits: Closing the Gap and Generalization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Heuristic-Guided Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Hierarchical Clustering: $O(1)$-Approximation for Well-Clustered Graphs |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Hierarchical Reinforcement Learning with Timed Subgoals |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Hierarchical Skills for Efficient Exploration |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
3 |
| High Probability Complexity Bounds for Line Search Based on Stochastic Oracles |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| High-probability Bounds for Non-Convex Stochastic Optimization with Heavy Tails |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Higher Order Kernel Mean Embeddings to Capture Filtrations of Stochastic Processes |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
2 |
| Hindsight Task Relabelling: Experience Replay for Sparse Reward Meta-RL |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| History Aware Multimodal Transformer for Vision-and-Language Navigation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Hit and Lead Discovery with Explorative RL and Fragment-based Molecule Generation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| How Data Augmentation affects Optimization for Linear Regression |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| How Does it Sound? |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| How Fine-Tuning Allows for Effective Meta-Learning |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| How Modular should Neural Module Networks Be for Systematic Generalization? |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| How Powerful are Performance Predictors in Neural Architecture Search? |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| How Should Pre-Trained Language Models Be Fine-Tuned Towards Adversarial Robustness? |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| How Tight Can PAC-Bayes be in the Small Data Regime? |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| How Well do Feature Visualizations Support Causal Understanding of CNN Activations? |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| How can classical multidimensional scaling go wrong? |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| How does a Neural Network's Architecture Impact its Robustness to Noisy Labels? |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| How to transfer algorithmic reasoning knowledge to learn new algorithms? |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Human-Adversarial Visual Question Answering |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Hybrid Regret Bounds for Combinatorial Semi-Bandits and Adversarial Linear Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| HyperSPNs: Compact and Expressive Probabilistic Circuits |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Hyperbolic Busemann Learning with Ideal Prototypes |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Hyperbolic Procrustes Analysis Using Riemannian Geometry |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Hypergraph Propagation and Community Selection for Objects Retrieval |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Hyperparameter Optimization Is Deceiving Us, and How to Stop It |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Hyperparameter Tuning is All You Need for LISTA |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
4 |
| IA-RED$^2$: Interpretability-Aware Redundancy Reduction for Vision Transformers |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| INDIGO: GNN-Based Inductive Knowledge Graph Completion Using Pair-Wise Encoding |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| IQ-Learn: Inverse soft-Q Learning for Imitation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| IRM—when it works and when it doesn't: A test case of natural language inference |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Identifiability in inverse reinforcement learning |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Identifiable Generative models for Missing Not at Random Data Imputation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Identification and Estimation of Joint Probabilities of Potential Outcomes in Observational Studies with Covariate Information |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Identification of Partially Observed Linear Causal Models: Graphical Conditions for the Non-Gaussian and Heterogeneous Cases |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Identification of the Generalized Condorcet Winner in Multi-dueling Bandits |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Identifying and Benchmarking Natural Out-of-Context Prediction Problems |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Identity testing for Mallows model |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Image Generation using Continuous Filter Atoms |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| ImageBART: Bidirectional Context with Multinomial Diffusion for Autoregressive Image Synthesis |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Imitating Deep Learning Dynamics via Locally Elastic Stochastic Differential Equations |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Imitation with Neural Density Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Implicit Bias of SGD for Diagonal Linear Networks: a Provable Benefit of Stochasticity |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Implicit Finite-Horizon Approximation and Efficient Optimal Algorithms for Stochastic Shortest Path |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Implicit Generative Copulas |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Implicit MLE: Backpropagating Through Discrete Exponential Family Distributions |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Implicit Regularization in Matrix Sensing via Mirror Descent |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Implicit SVD for Graph Representation Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Implicit Semantic Response Alignment for Partial Domain Adaptation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Implicit Sparse Regularization: The Impact of Depth and Early Stopping |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Implicit Task-Driven Probability Discrepancy Measure for Unsupervised Domain Adaptation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Implicit Transformer Network for Screen Content Image Continuous Super-Resolution |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Impression learning: Online representation learning with synaptic plasticity |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Improve Agents without Retraining: Parallel Tree Search with Off-Policy Correction |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Improved Coresets and Sublinear Algorithms for Power Means in Euclidean Spaces |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Improved Guarantees for Offline Stochastic Matching via new Ordered Contention Resolution Schemes |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Improved Learning Rates of a Functional Lasso-type SVM with Sparse Multi-Kernel Representation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Improved Regret Bounds for Tracking Experts with Memory |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Improved Regularization and Robustness for Fine-tuning in Neural Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Improved Transformer for High-Resolution GANs |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Improved Variance-Aware Confidence Sets for Linear Bandits and Linear Mixture MDP |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Improving Anytime Prediction with Parallel Cascaded Networks and a Temporal-Difference Loss |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Improving Calibration through the Relationship with Adversarial Robustness |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Improving Coherence and Consistency in Neural Sequence Models with Dual-System, Neuro-Symbolic Reasoning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Improving Compositionality of Neural Networks by Decoding Representations to Inputs |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
4 |
| Improving Computational Efficiency in Visual Reinforcement Learning via Stored Embeddings |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Improving Conditional Coverage via Orthogonal Quantile Regression |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Improving Contrastive Learning on Imbalanced Data via Open-World Sampling |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Improving Deep Learning Interpretability by Saliency Guided Training |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Improving Generalization in Meta-RL with Imaginary Tasks from Latent Dynamics Mixture |
❌ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Improving Robustness using Generated Data |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Improving Self-supervised Learning with Automated Unsupervised Outlier Arbitration |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Improving Transferability of Representations via Augmentation-Aware Self-Supervision |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Improving Visual Quality of Image Synthesis by A Token-based Generator with Transformers |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Improving black-box optimization in VAE latent space using decoder uncertainty |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Increasing Liquid State Machine Performance with Edge-of-Chaos Dynamics Organized by Astrocyte-modulated Plasticity |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Independent Prototype Propagation for Zero-Shot Compositionality |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Independent mechanism analysis, a new concept? |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Indexed Minimum Empirical Divergence for Unimodal Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Individual Privacy Accounting via a Rényi Filter |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Infinite Time Horizon Safety of Bayesian Neural Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| Influence Patterns for Explaining Information Flow in BERT |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| InfoGCL: Information-Aware Graph Contrastive Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Information Directed Reward Learning for Reinforcement Learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Information Directed Sampling for Sparse Linear Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Information is Power: Intrinsic Control via Information Capture |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Information-constrained optimization: can adaptive processing of gradients help? |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Information-theoretic generalization bounds for black-box learning algorithms |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Instance-Conditional Knowledge Distillation for Object Detection |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Instance-Conditioned GAN |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Instance-Dependent Bounds for Zeroth-order Lipschitz Optimization with Error Certificates |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Instance-Dependent Partial Label Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Instance-dependent Label-noise Learning under a Structural Causal Model |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Instance-optimal Mean Estimation Under Differential Privacy |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Integrated Latent Heterogeneity and Invariance Learning in Kernel Space |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Integrating Expert ODEs into Neural ODEs: Pharmacology and Disease Progression |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Integrating Tree Path in Transformer for Code Representation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Interactive Label Cleaning with Example-based Explanations |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Interesting Object, Curious Agent: Learning Task-Agnostic Exploration |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Intermediate Layers Matter in Momentum Contrastive Self Supervised Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Interpolation can hurt robust generalization even when there is no noise |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Interpretable agent communication from scratch (with a generic visual processor emerging on the side) |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Interpreting Representation Quality of DNNs for 3D Point Cloud Processing |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Interventional Sum-Product Networks: Causal Inference with Tractable Probabilistic Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Intriguing Properties of Contrastive Losses |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Intriguing Properties of Vision Transformers |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Intrinsic Dimension, Persistent Homology and Generalization in Neural Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Introspective Distillation for Robust Question Answering |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Invariance Principle Meets Information Bottleneck for Out-of-Distribution Generalization |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Invariant Causal Imitation Learning for Generalizable Policies |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Inverse Optimal Control Adapted to the Noise Characteristics of the Human Sensorimotor System |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Inverse Problems Leveraging Pre-trained Contrastive Representations |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Inverse Reinforcement Learning in a Continuous State Space with Formal Guarantees |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
5 |
| Inverse-Weighted Survival Games |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Invertible DenseNets with Concatenated LipSwish |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Invertible Tabular GANs: Killing Two Birds with One Stone for Tabular Data Synthesis |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Is Automated Topic Model Evaluation Broken? The Incoherence of Coherence |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Is Bang-Bang Control All You Need? Solving Continuous Control with Bernoulli Policies |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Ising Model Selection Using $\ell_{1}$-Regularized Linear Regression: A Statistical Mechanics Analysis |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| It Has Potential: Gradient-Driven Denoisers for Convergent Solutions to Inverse Problems |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Iterative Amortized Policy Optimization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Iterative Causal Discovery in the Possible Presence of Latent Confounders and Selection Bias |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
3 |
| Iterative Connecting Probability Estimation for Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Iterative Methods for Private Synthetic Data: Unifying Framework and New Methods |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Iterative Teacher-Aware Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Iterative Teaching by Label Synthesis |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Iteratively Reweighted Least Squares for Basis Pursuit with Global Linear Convergence Rate |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Joint Inference for Neural Network Depth and Dropout Regularization |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Joint Modeling of Visual Objects and Relations for Scene Graph Generation |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Joint Semantic Mining for Weakly Supervised RGB-D Salient Object Detection |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Joint inference and input optimization in equilibrium networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| K-Net: Towards Unified Image Segmentation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| K-level Reasoning for Zero-Shot Coordination in Hanabi |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| KALE Flow: A Relaxed KL Gradient Flow for Probabilities with Disjoint Support |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| KS-GNN: Keywords Search over Incomplete Graphs via Graphs Neural Network |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
4 |
| Keeping Your Eye on the Ball: Trajectory Attention in Video Transformers |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Kernel Functional Optimisation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Kernel Identification Through Transformers |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Knowledge-Adaptation Priors |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Knowledge-inspired 3D Scene Graph Prediction in Point Cloud |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| L2ight: Enabling On-Chip Learning for Optical Neural Networks via Efficient in-situ Subspace Optimization |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| LADA: Look-Ahead Data Acquisition via Augmentation for Deep Active Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| LEADS: Learning Dynamical Systems that Generalize Across Environments |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| LLC: Accurate, Multi-purpose Learnt Low-dimensional Binary Codes |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| LSH-SMILE: Locality Sensitive Hashing Accelerated Simulation and Learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Label Disentanglement in Partition-based Extreme Multilabel Classification |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Label Noise SGD Provably Prefers Flat Global Minimizers |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Label consistency in overfitted generalized $k$-means |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Label-Imbalanced and Group-Sensitive Classification under Overparameterization |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Labeling Trick: A Theory of Using Graph Neural Networks for Multi-Node Representation Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Landmark-Guided Subgoal Generation in Hierarchical Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Landmark-RxR: Solving Vision-and-Language Navigation with Fine-Grained Alignment Supervision |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Landscape analysis of an improved power method for tensor decomposition |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Language models enable zero-shot prediction of the effects of mutations on protein function |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Laplace Redux - Effortless Bayesian Deep Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Large Scale Learning on Non-Homophilous Graphs: New Benchmarks and Strong Simple Methods |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Large-Scale Learning with Fourier Features and Tensor Decompositions |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Large-Scale Unsupervised Object Discovery |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Large-Scale Wasserstein Gradient Flows |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Last iterate convergence of SGD for Least-Squares in the Interpolation regime. |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Last-iterate Convergence in Extensive-Form Games |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Latent Equilibrium: A unified learning theory for arbitrarily fast computation with arbitrarily slow neurons |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Latent Execution for Neural Program Synthesis Beyond Domain-Specific Languages |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Latent Matters: Learning Deep State-Space Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Lattice partition recovery with dyadic CART |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Learnability of Linear Thresholds from Label Proportions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learnable Fourier Features for Multi-dimensional Spatial Positional Encoding |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learned Robust PCA: A Scalable Deep Unfolding Approach for High-Dimensional Outlier Detection |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
5 |
| Learning 3D Dense Correspondence via Canonical Point Autoencoder |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning Barrier Certificates: Towards Safe Reinforcement Learning with Zero Training-time Violations |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Causal Semantic Representation for Out-of-Distribution Prediction |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning Collaborative Policies to Solve NP-hard Routing Problems |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning Compact Representations of Neural Networks using DiscriminAtive Masking (DAM) |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning Conjoint Attentions for Graph Neural Nets |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning Debiased Representation via Disentangled Feature Augmentation |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning Debiased and Disentangled Representations for Semantic Segmentation |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Disentangled Behavior Embeddings |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Distilled Collaboration Graph for Multi-Agent Perception |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Diverse Policies in MOBA Games via Macro-Goals |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Learning Domain Invariant Representations in Goal-conditioned Block MDPs |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning Dynamic Graph Representation of Brain Connectome with Spatio-Temporal Attention |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Equilibria in Matching Markets from Bandit Feedback |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning Equivariant Energy Based Models with Equivariant Stein Variational Gradient Descent |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Fast-Inference Bayesian Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning Frequency Domain Approximation for Binary Neural Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning Gaussian Mixtures with Generalized Linear Models: Precise Asymptotics in High-dimensions |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Generalized Gumbel-max Causal Mechanisms |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning Generative Vision Transformer with Energy-Based Latent Space for Saliency Prediction |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning Graph Cellular Automata |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning Graph Models for Retrosynthesis Prediction |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Learning Hard Optimization Problems: A Data Generation Perspective |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Learning High-Precision Bounding Box for Rotated Object Detection via Kullback-Leibler Divergence |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Interpretable Decision Rule Sets: A Submodular Optimization Approach |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning Knowledge Graph-based World Models of Textual Environments |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Large Neighborhood Search Policy for Integer Programming |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Learning MDPs from Features: Predict-Then-Optimize for Sequential Decision Making by Reinforcement Learning |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning Markov State Abstractions for Deep Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Models for Actionable Recourse |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning Nonparametric Volterra Kernels with Gaussian Processes |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning One Representation to Optimize All Rewards |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Optimal Predictive Checklists |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
6 |
| Learning Policies with Zero or Bounded Constraint Violation for Constrained MDPs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning Riemannian metric for disease progression modeling |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Robust Hierarchical Patterns of Human Brain across Many fMRI Studies |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Learning Semantic Representations to Verify Hardware Designs |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Signal-Agnostic Manifolds of Neural Fields |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Space Partitions for Path Planning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning Stable Deep Dynamics Models for Partially Observed or Delayed Dynamical Systems |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning State Representations from Random Deep Action-conditional Predictions |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning Stochastic Majority Votes by Minimizing a PAC-Bayes Generalization Bound |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning Student-Friendly Teacher Networks for Knowledge Distillation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning Theory Can (Sometimes) Explain Generalisation in Graph Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning Transferable Adversarial Perturbations |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning Transferable Features for Point Cloud Detection via 3D Contrastive Co-training |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning Treatment Effects in Panels with General Intervention Patterns |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Tree Interpretation from Object Representation for Deep Reinforcement Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Learning a Single Neuron with Bias Using Gradient Descent |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Learning and Generalization in RNNs |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Learning curves of generic features maps for realistic datasets with a teacher-student model |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning from Inside: Self-driven Siamese Sampling and Reasoning for Video Question Answering |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Learning in Multi-Stage Decentralized Matching Markets |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning in Non-Cooperative Configurable Markov Decision Processes |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning in two-player zero-sum partially observable Markov games with perfect recall |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning interaction rules from multi-animal trajectories via augmented behavioral models |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning latent causal graphs via mixture oracles |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning on Random Balls is Sufficient for Estimating (Some) Graph Parameters |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning rule influences recurrent network representations but not attractor structure in decision-making tasks |
❌ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
3 |
| Learning the optimal Tikhonov regularizer for inverse problems |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Learning to Adapt via Latent Domains for Adaptive Semantic Segmentation |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Learning to Assimilate in Chaotic Dynamical Systems |
❌ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Learning to Combine Per-Example Solutions for Neural Program Synthesis |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
4 |
| Learning to Compose Visual Relations |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning to Draw: Emergent Communication through Sketching |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning to Elect |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning to Execute: Efficient Learning of Universal Plan-Conditioned Policies in Robotics |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning to Generate Realistic Noisy Images via Pixel-level Noise-aware Adversarial Training |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Learning to Generate Visual Questions with Noisy Supervision |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning to Ground Multi-Agent Communication with Autoencoders |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning to Iteratively Solve Routing Problems with Dual-Aspect Collaborative Transformer |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning to Learn Dense Gaussian Processes for Few-Shot Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning to Learn Graph Topologies |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Learning to Predict Trustworthiness with Steep Slope Loss |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning to Schedule Heuristics in Branch and Bound |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Learning to See by Looking at Noise |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning to Select Exogenous Events for Marked Temporal Point Process |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Learning to Simulate Self-driven Particles System with Coordinated Policy Optimization |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
3 |
| Learning to Synthesize Programs as Interpretable and Generalizable Policies |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning to Time-Decode in Spiking Neural Networks Through the Information Bottleneck |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning to dehaze with polarization |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Learning to delegate for large-scale vehicle routing |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning where to learn: Gradient sparsity in meta and continual learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning with Algorithmic Supervision via Continuous Relaxations |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning with Holographic Reduced Representations |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning with Labeling Induced Abstentions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning with Noisy Correspondence for Cross-modal Matching |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning with User-Level Privacy |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning-Augmented Dynamic Power Management with Multiple States via New Ski Rental Bounds |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning-to-learn non-convex piecewise-Lipschitz functions |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Least Square Calibration for Peer Reviews |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Leveraging Distribution Alignment via Stein Path for Cross-Domain Cold-Start Recommendation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Leveraging Recursive Gumbel-Max Trick for Approximate Inference in Combinatorial Spaces |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Leveraging SE(3) Equivariance for Self-supervised Category-Level Object Pose Estimation from Point Clouds |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Leveraging Spatial and Temporal Correlations in Sparsified Mean Estimation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Leveraging the Inductive Bias of Large Language Models for Abstract Textual Reasoning |
❌ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Lifelong Domain Adaptation via Consolidated Internal Distribution |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Light Field Networks: Neural Scene Representations with Single-Evaluation Rendering |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Limiting fluctuation and trajectorial stability of multilayer neural networks with mean field training |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Linear Convergence in Federated Learning: Tackling Client Heterogeneity and Sparse Gradients |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Linear Convergence of Gradient Methods for Estimating Structured Transition Matrices in High-dimensional Vector Autoregressive Models |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Linear and Kernel Classification in the Streaming Model: Improved Bounds for Heavy Hitters |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Linear-Time Probabilistic Solution of Boundary Value Problems |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Lip to Speech Synthesis with Visual Context Attentional GAN |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| List-Decodable Mean Estimation in Nearly-PCA Time |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Littlestone Classes are Privately Online Learnable |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Local Differential Privacy for Regret Minimization in Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Local Disentanglement in Variational Auto-Encoders Using Jacobian $L_1$ Regularization |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Local Explanation of Dialogue Response Generation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Local Hyper-Flow Diffusion |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Local Signal Adaptivity: Provable Feature Learning in Neural Networks Beyond Kernels |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Local plasticity rules can learn deep representations using self-supervised contrastive predictions |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Local policy search with Bayesian optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Locality Sensitive Teaching |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
5 |
| Locality defeats the curse of dimensionality in convolutional teacher-student scenarios |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Localization with Sampling-Argmax |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Localization, Convexity, and Star Aggregation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Locally Most Powerful Bayesian Test for Out-of-Distribution Detection using Deep Generative Models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Locally Valid and Discriminative Prediction Intervals for Deep Learning Models |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
5 |
| Locally differentially private estimation of functionals of discrete distributions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Locally private online change point detection |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Logarithmic Regret from Sublinear Hints |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Logarithmic Regret in Feature-based Dynamic Pricing |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Long Short-Term Transformer for Online Action Detection |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Long-Short Transformer: Efficient Transformers for Language and Vision |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Look at What I’m Doing: Self-Supervised Spatial Grounding of Narrations in Instructional Videos |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Look at the Variance! Efficient Black-box Explanations with Sobol-based Sensitivity Analysis |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Looking Beyond Single Images for Contrastive Semantic Segmentation Learning |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Loss function based second-order Jensen inequality and its application to particle variational inference |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Lossy Compression for Lossless Prediction |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Low-Fidelity Video Encoder Optimization for Temporal Action Localization |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Low-Rank Constraints for Fast Inference in Structured Models |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Low-Rank Extragradient Method for Nonsmooth and Low-Rank Matrix Optimization Problems |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Low-Rank Subspaces in GANs |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Low-dimensional Structure in the Space of Language Representations is Reflected in Brain Responses |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Lower Bounds and Optimal Algorithms for Smooth and Strongly Convex Decentralized Optimization Over Time-Varying Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Lower Bounds on Metropolized Sampling Methods for Well-Conditioned Distributions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Lower and Upper Bounds on the Pseudo-Dimension of Tensor Network Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Luna: Linear Unified Nested Attention |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| M-FAC: Efficient Matrix-Free Approximations of Second-Order Information |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| MADE: Exploration via Maximizing Deviation from Explored Regions |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| MAP Propagation Algorithm: Faster Learning with a Team of Reinforcement Learning Agents |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| MAU: A Motion-Aware Unit for Video Prediction and Beyond |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| MAUVE: Measuring the Gap Between Neural Text and Human Text using Divergence Frontiers |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| MCMC Variational Inference via Uncorrected Hamiltonian Annealing |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| MERLOT: Multimodal Neural Script Knowledge Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| MEST: Accurate and Fast Memory-Economic Sparse Training Framework on the Edge |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| MICo: Improved representations via sampling-based state similarity for Markov decision processes |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| MIRACLE: Causally-Aware Imputation via Learning Missing Data Mechanisms |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| MLP-Mixer: An all-MLP Architecture for Vision |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| MOMA: Multi-Object Multi-Actor Activity Parsing |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| MST: Masked Self-Supervised Transformer for Visual Representation |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Machine Learning for Variance Reduction in Online Experiments |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Machine learning structure preserving brackets for forecasting irreversible processes |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Machine versus Human Attention in Deep Reinforcement Learning Tasks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| MagNet: A Neural Network for Directed Graphs |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Make Sure You're Unsure: A Framework for Verifying Probabilistic Specifications |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Making a (Counterfactual) Difference One Rationale at a Time |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Making the most of your day: online learning for optimal allocation of time |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Manifold Topology Divergence: a Framework for Comparing Data Manifolds. |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Manipulating SGD with Data Ordering Attacks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Margin-Independent Online Multiclass Learning via Convex Geometry |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Marginalised Gaussian Processes with Nested Sampling |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| MarioNette: Self-Supervised Sprite Learning |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Mastering Atari Games with Limited Data |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Matching a Desired Causal State via Shift Interventions |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Matrix encoding networks for neural combinatorial optimization |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Matrix factorisation and the interpretation of geodesic distance |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Maximum Likelihood Training of Score-Based Diffusion Models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Mean-based Best Arm Identification in Stochastic Bandits under Reward Contamination |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Measuring Generalization with Optimal Transport |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Medical Dead-ends and Learning to Identify High-Risk States and Treatments |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Memory Efficient Meta-Learning with Large Images |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Memory-Efficient Approximation Algorithms for Max-k-Cut and Correlation Clustering |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| Memory-efficient Patch-based Inference for Tiny Deep Learning |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
3 |
| Meta Internal Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Meta Learning Backpropagation And Improving It |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Meta Two-Sample Testing: Learning Kernels for Testing with Limited Data |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Meta-Adaptive Nonlinear Control: Theory and Algorithms |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Meta-Learning Reliable Priors in the Function Space |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Meta-Learning Sparse Implicit Neural Representations |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Meta-Learning for Relative Density-Ratio Estimation |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Meta-learning to Improve Pre-training |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Meta-learning with an Adaptive Task Scheduler |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| MetaAvatar: Learning Animatable Clothed Human Models from Few Depth Images |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Metadata-based Multi-Task Bandits with Bayesian Hierarchical Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Metropolis-Hastings Data Augmentation for Graph Neural Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Mind the Gap: Assessing Temporal Generalization in Neural Language Models |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Mini-Batch Consistent Slot Set Encoder for Scalable Set Encoding |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Minibatch and Momentum Model-based Methods for Stochastic Weakly Convex Optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Minimax Optimal Quantile and Semi-Adversarial Regret via Root-Logarithmic Regularizers |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Minimax Regret for Stochastic Shortest Path |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Minimizing Polarization and Disagreement in Social Networks via Link Recommendation |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Mining the Benefits of Two-stage and One-stage HOI Detection |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Mirror Langevin Monte Carlo: the Case Under Isoperimetry |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Misspecified Gaussian Process Bandit Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Mitigating Covariate Shift in Imitation Learning via Offline Data With Partial Coverage |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Mitigating Forgetting in Online Continual Learning with Neuron Calibration |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| MixACM: Mixup-Based Robustness Transfer via Distillation of Activated Channel Maps |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| MixSeq: Connecting Macroscopic Time Series Forecasting with Microscopic Time Series Data |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Mixability made efficient: Fast online multiclass logistic regression |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Mixed Supervised Object Detection by Transferring Mask Prior and Semantic Similarity |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Mixture Proportion Estimation and PU Learning:A Modern Approach |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Mixture weights optimisation for Alpha-Divergence Variational Inference |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| MobILE: Model-Based Imitation Learning From Observation Alone |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| MobTCast: Leveraging Auxiliary Trajectory Forecasting for Human Mobility Prediction |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Modality-Agnostic Topology Aware Localization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Model Adaptation: Historical Contrastive Learning for Unsupervised Domain Adaptation without Source Data |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Model Selection for Bayesian Autoencoders |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Model, sample, and epoch-wise descents: exact solution of gradient flow in the random feature model |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Model-Based Domain Generalization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Model-Based Episodic Memory Induces Dynamic Hybrid Controls |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Model-Based Reinforcement Learning via Imagination with Derived Memory |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Modeling Heterogeneous Hierarchies with Relation-specific Hyperbolic Cones |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Modified Frank Wolfe in Probability Space |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
5 |
| Modular Gaussian Processes for Transfer Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Momentum Centering and Asynchronous Update for Adaptive Gradient Methods |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Monte Carlo Tree Search With Iteratively Refining State Abstractions |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Morié Attack (MA): A New Potential Risk of Screen Photos |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Mosaicking to Distill: Knowledge Distillation from Out-of-Domain Data |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Moser Flow: Divergence-based Generative Modeling on Manifolds |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Moshpit SGD: Communication-Efficient Decentralized Training on Heterogeneous Unreliable Devices |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Motif-based Graph Self-Supervised Learning for Molecular Property Prediction |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Multi-Agent Reinforcement Learning for Active Voltage Control on Power Distribution Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multi-Agent Reinforcement Learning in Stochastic Networked Systems |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Multi-Armed Bandits with Bounded Arm-Memory: Near-Optimal Guarantees for Best-Arm Identification and Regret Minimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Multi-Facet Clustering Variational Autoencoders |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multi-Label Learning with Pairwise Relevance Ordering |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Multi-Objective Meta Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Multi-Objective SPIBB: Seldonian Offline Policy Improvement with Safety Constraints in Finite MDPs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Multi-Person 3D Motion Prediction with Multi-Range Transformers |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multi-Scale Representation Learning on Proteins |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Multi-Step Budgeted Bayesian Optimization with Unknown Evaluation Costs |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multi-View Representation Learning via Total Correlation Objective |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multi-armed Bandit Requiring Monotone Arm Sequences |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Multi-modal Dependency Tree for Video Captioning |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Multi-task Learning of Order-Consistent Causal Graphs |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multi-view Contrastive Graph Clustering |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Multiclass Boosting and the Cost of Weak Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Multiclass versus Binary Differentially Private PAC Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Multilingual Pre-training with Universal Dependency Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multimodal Few-Shot Learning with Frozen Language Models |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Multimodal Virtual Point 3D Detection |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Multimodal and Multilingual Embeddings for Large-Scale Speech Mining |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Multiple Descent: Design Your Own Generalization Curve |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Multiwavelet-based Operator Learning for Differential Equations |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| NAS-Bench-x11 and the Power of Learning Curves |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| NEO: Non Equilibrium Sampling on the Orbits of a Deterministic Transform |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| NN-Baker: A Neural-network Infused Algorithmic Framework for Optimization Problems on Geometric Intersection Graphs |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| NORESQA: A Framework for Speech Quality Assessment using Non-Matching References |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| NTopo: Mesh-free Topology Optimization using Implicit Neural Representations |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Natural continual learning: success is a journey, not (just) a destination |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Navigating to the Best Policy in Markov Decision Processes |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| NeRS: Neural Reflectance Surfaces for Sparse-view 3D Reconstruction in the Wild |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| NeRV: Neural Representations for Videos |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Near Optimal Policy Optimization via REPS |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Near-Optimal Lower Bounds For Convex Optimization For All Orders of Smoothness |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Near-Optimal Multi-Perturbation Experimental Design for Causal Structure Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Near-Optimal No-Regret Learning in General Games |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Near-Optimal Offline Reinforcement Learning via Double Variance Reduction |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Near-optimal Offline and Streaming Algorithms for Learning Non-Linear Dynamical Systems |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Nearly Horizon-Free Offline Reinforcement Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Nearly Minimax Optimal Reinforcement Learning for Discounted MDPs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Nearly-Tight and Oblivious Algorithms for Explainable Clustering |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Necessary and sufficient graphical conditions for optimal adjustment sets in causal graphical models with hidden variables |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Neighborhood Reconstructing Autoencoders |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Neo-GNNs: Neighborhood Overlap-aware Graph Neural Networks for Link Prediction |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Nested Counterfactual Identification from Arbitrary Surrogate Experiments |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Nested Graph Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Nested Variational Inference |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Network-to-Network Regularization: Enforcing Occam's Razor to Improve Generalization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| NeuS: Learning Neural Implicit Surfaces by Volume Rendering for Multi-view Reconstruction |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| NeurWIN: Neural Whittle Index Network For Restless Bandits Via Deep RL |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Neural Active Learning with Performance Guarantees |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Neural Additive Models: Interpretable Machine Learning with Neural Nets |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Neural Algorithmic Reasoners are Implicit Planners |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Neural Analysis and Synthesis: Reconstructing Speech from Self-Supervised Representations |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Neural Architecture Dilation for Adversarial Robustness |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Neural Auto-Curricula in Two-Player Zero-Sum Games |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Neural Bootstrapper |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Neural Circuit Synthesis from Specification Patterns |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Neural Distance Embeddings for Biological Sequences |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Neural Dubber: Dubbing for Videos According to Scripts |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Neural Ensemble Search for Uncertainty Estimation and Dataset Shift |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Neural Flows: Efficient Alternative to Neural ODEs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Neural Human Performer: Learning Generalizable Radiance Fields for Human Performance Rendering |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Neural Hybrid Automata: Learning Dynamics With Multiple Modes and Stochastic Transitions |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
2 |
| Neural Population Geometry Reveals the Role of Stochasticity in Robust Perception |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Neural Production Systems |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Neural Program Generation Modulo Static Analysis |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Neural Pseudo-Label Optimism for the Bank Loan Problem |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Neural Regression, Representational Similarity, Model Zoology & Neural Taskonomy at Scale in Rodent Visual Cortex |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Neural Relightable Participating Media Rendering |
❌ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
3 |
| Neural Routing by Memory |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Neural Rule-Execution Tracking Machine For Transformer-Based Text Generation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Neural Scene Flow Prior |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Neural Symplectic Form: Learning Hamiltonian Equations on General Coordinate Systems |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Neural Tangent Kernel Maximum Mean Discrepancy |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Neural Trees for Learning on Graphs |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Neural View Synthesis and Matching for Semi-Supervised Few-Shot Learning of 3D Pose |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Neural optimal feedback control with local learning rules |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Neural-PIL: Neural Pre-Integrated Lighting for Reflectance Decomposition |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| NeuroLKH: Combining Deep Learning Model with Lin-Kernighan-Helsgaun Heuristic for Solving the Traveling Salesman Problem |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| NeuroMLR: Robust & Reliable Route Recommendation on Road Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Never Go Full Batch (in Stochastic Convex Optimization) |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Newton-LESS: Sparsification without Trade-offs for the Sketched Newton Update |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| No Fear of Heterogeneity: Classifier Calibration for Federated Learning with Non-IID Data |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| No RL, No Simulation: Learning to Navigate without Navigating |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| No Regrets for Learning the Prior in Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| No-Press Diplomacy from Scratch |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| No-regret Online Learning over Riemannian Manifolds |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Node Dependent Local Smoothing for Scalable Graph Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Noether Networks: meta-learning useful conserved quantities |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Noether’s Learning Dynamics: Role of Symmetry Breaking in Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Noise2Score: Tweedie’s Approach to Self-Supervised Image Denoising without Clean Images |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Noisy Adaptation Generates Lévy Flights in Attractor Neural Networks |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Noisy Recurrent Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Non-Asymptotic Analysis for Two Time-scale TDC with General Smooth Function Approximation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Non-Gaussian Gaussian Processes for Few-Shot Regression |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Non-approximate Inference for Collective Graphical Models on Path Graphs via Discrete Difference of Convex Algorithm |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Non-asymptotic Error Bounds for Bidirectional GANs |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Non-asymptotic convergence bounds for Wasserstein approximation using point clouds |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Non-convex Distributionally Robust Optimization: Non-asymptotic Analysis |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Non-local Latent Relation Distillation for Self-Adaptive 3D Human Pose Estimation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Nonparametric estimation of continuous DPPs with kernel methods |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Nonsmooth Implicit Differentiation for Machine-Learning and Optimization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Nonuniform Negative Sampling and Log Odds Correction with Rare Events Data |
✅ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Not All Images are Worth 16x16 Words: Dynamic Transformers for Efficient Image Recognition |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Not All Low-Pass Filters are Robust in Graph Convolutional Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Novel Upper Bounds for the Constrained Most Probable Explanation Task |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Novel Visual Category Discovery with Dual Ranking Statistics and Mutual Knowledge Distillation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| NovelD: A Simple yet Effective Exploration Criterion |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Numerical Composition of Differential Privacy |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Numerical influence of ReLU’(0) on backpropagation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| NxMTransformer: Semi-Structured Sparsification for Natural Language Understanding via ADMM |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| OSOA: One-Shot Online Adaptation of Deep Generative Models for Lossless Compression |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Object DGCNN: 3D Object Detection using Dynamic Graphs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Object-Aware Regularization for Addressing Causal Confusion in Imitation Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Object-Centric Representation Learning with Generative Spatial-Temporal Factorization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Object-aware Contrastive Learning for Debiased Scene Representation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Observation-Free Attacks on Stochastic Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| OctField: Hierarchical Implicit Functions for 3D Modeling |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Off-Policy Risk Assessment in Contextual Bandits |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Offline Constrained Multi-Objective Reinforcement Learning via Pessimistic Dual Value Iteration |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Offline Meta Reinforcement Learning -- Identifiability Challenges and Effective Data Collection Strategies |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Offline Model-based Adaptable Policy Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Offline RL Without Off-Policy Evaluation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Offline Reinforcement Learning as One Big Sequence Modeling Problem |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Offline Reinforcement Learning with Reverse Model-based Imagination |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On Blame Attribution for Accountable Multi-Agent Sequential Decision Making |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| On Calibration and Out-of-Domain Generalization |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| On Component Interactions in Two-Stage Recommender Systems |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| On Contrastive Representations of Stochastic Processes |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On Effective Scheduling of Model-based Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| On Empirical Risk Minimization with Dependent and Heavy-Tailed Data |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On Episodes, Prototypical Networks, and Few-Shot Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On Inductive Biases for Heterogeneous Treatment Effect Estimation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| On Interaction Between Augmentations and Corruptions in Natural Corruption Robustness |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On Joint Learning for Solving Placement and Routing in Chip Design |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| On Large-Cohort Training for Federated Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| On Learning Domain-Invariant Representations for Transfer Learning with Multiple Sources |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On Linear Stability of SGD and Input-Smoothness of Neural Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| On Locality of Local Explanation Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| On Margin-Based Cluster Recovery with Oracle Queries |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On Memorization in Probabilistic Deep Generative Models |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| On Model Calibration for Long-Tailed Object Detection and Instance Segmentation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On Optimal Interpolation in Linear Regression |
❌ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| On Optimal Robustness to Adversarial Corruption in Online Decision Problems |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On Path Integration of Grid Cells: Group Representation and Isotropic Scaling |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| On Pathologies in KL-Regularized Reinforcement Learning from Expert Demonstrations |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| On Plasticity, Invariance, and Mutually Frozen Weights in Sequential Task Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On Provable Benefits of Depth in Training Graph Convolutional Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On Riemannian Optimization over Positive Definite Matrices with the Bures-Wasserstein Geometry |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| On Robust Optimal Transport: Computational Complexity and Barycenter Computation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| On Success and Simplicity: A Second Look at Transferable Targeted Attacks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| On The Structure of Parametric Tournaments with Application to Ranking from Pairwise Comparisons |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| On Training Implicit Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On UMAP's True Loss Function |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| On learning sparse vectors from mixture of responses |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On sensitivity of meta-learning to support data |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| On the Algorithmic Stability of Adversarial Training |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Bias-Variance-Cost Tradeoff of Stochastic Optimization |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Convergence Theory of Debiased Model-Agnostic Meta-Reinforcement Learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| On the Convergence and Sample Efficiency of Variance-Reduced Policy Gradient Method |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Convergence of Prior-Guided Zeroth-Order Optimization Algorithms |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| On the Convergence of Step Decay Step-Size for Stochastic Optimization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On the Cryptographic Hardness of Learning Single Periodic Neurons |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On the Equivalence between Neural Network and Support Vector Machine |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On the Estimation Bias in Double Q-Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Existence of The Adversarial Bayes Classifier |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Expected Complexity of Maxout Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| On the Expressivity of Markov Reward |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On the Frequency Bias of Generative Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Generative Utility of Cyclic Conditionals |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Importance of Gradients for Detecting Distributional Shifts in the Wild |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Out-of-distribution Generalization of Probabilistic Image Modelling |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Periodic Behavior of Neural Network Training with Batch Normalization and Weight Decay |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Power of Differentiable Learning versus PAC and SQ Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On the Power of Edge Independent Graph Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| On the Provable Generalization of Recurrent Neural Networks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On the Rate of Convergence of Regularized Learning in Games: From Bandits and Uncertainty to Optimism and Beyond |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| On the Representation Power of Set Pooling Networks |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| On the Representation of Solutions to Elliptic PDEs in Barron Spaces |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Role of Optimization in Double Descent: A Least Squares Study |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On the Sample Complexity of Learning under Geometric Stability |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| On the Sample Complexity of Privately Learning Axis-Aligned Rectangles |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On the Second-order Convergence Properties of Random Search Methods |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Stochastic Stability of Deep Markov Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Suboptimality of Thompson Sampling in High Dimensions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| On the Theory of Reinforcement Learning with Once-per-Episode Feedback |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| On the Universality of Graph Neural Networks on Large Random Graphs |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On the Validity of Modeling SGD with Stochastic Differential Equations (SDEs) |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Value of Infinite Gradients in Variational Autoencoder Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| On the Value of Interaction and Function Approximation in Imitation Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On the Variance of the Fisher Information for Deep Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the interplay between data structure and loss function in classification problems |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| One Explanation is Not Enough: Structured Attention Graphs for Image Classification |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| One Loss for All: Deep Hashing with a Single Cosine Similarity based Learning Objective |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| One More Step Towards Reality: Cooperative Bandits with Imperfect Communication |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| One Question Answering Model for Many Languages with Cross-lingual Dense Passage Retrieval |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Online Active Learning with Surrogate Loss Functions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Online Adaptation to Label Distribution Shift |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Online Control of Unknown Time-Varying Dynamical Systems |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Online Convex Optimization with Continuous Switching Constraint |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Online Facility Location with Multiple Advice |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Online Knapsack with Frequency Predictions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Online Learning Of Neural Computations From Sparse Temporal Feedback |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Online Learning and Control of Complex Dynamical Systems from Sensory Input |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Online Learning in Periodic Zero-Sum Games |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Online Market Equilibrium with Application to Fair Division |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Online Matching in Sparse Random Graphs: Non-Asymptotic Performances of Greedy Algorithm |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Online Meta-Learning via Learning with Layer-Distributed Memory |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Online Multi-Armed Bandits with Adaptive Inference |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Online Robust Reinforcement Learning with Model Uncertainty |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Online Selective Classification with Limited Feedback |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Online Sign Identification: Minimization of the Number of Errors in Thresholding Bandits |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
5 |
| Online Variational Filtering and Parameter Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Online and Offline Reinforcement Learning by Planning with a Learned Model |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Online false discovery rate control for anomaly detection in time series |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Online learning in MDPs with linear function approximation and bandit feedback. |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Only Train Once: A One-Shot Neural Network Training And Pruning Framework |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Open Rule Induction |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Open-set Label Noise Can Improve Robustness Against Inherent Label Noise |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| OpenMatch: Open-Set Semi-supervised Learning with Open-set Consistency Regularization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Optimal Algorithms for Stochastic Contextual Preference Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Optimal Best-Arm Identification Methods for Tail-Risk Measures |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Optimal Gradient-based Algorithms for Non-concave Bandit Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimal Order Simple Regret for Gaussian Process Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Optimal Policies Tend To Seek Power |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Optimal Rates for Nonparametric Density Estimation under Communication Constraints |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimal Rates for Random Order Online Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimal Sketching for Trace Estimation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Optimal Underdamped Langevin MCMC Method |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Optimal Uniform OPE and Model-based Offline Reinforcement Learning in Time-Homogeneous, Reward-Free and Task-Agnostic Settings |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Optimal prediction of Markov chains with and without spectral gap |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Optimality and Stability in Federated Learning: A Game-theoretic Approach |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimality of variational inference for stochasticblock model with missing links |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Optimization-Based Algebraic Multigrid Coarsening Using Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Optimizing Conditional Value-At-Risk of Black-Box Functions |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Optimizing Information-theoretical Generalization Bound via Anisotropic Noise of SGLD |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Optimizing Reusable Knowledge for Continual Learning via Metalearning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Oracle Complexity in Nonsmooth Nonconvex Optimization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Oracle-Efficient Regret Minimization in Factored MDPs with Unknown Structure |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Out-of-Distribution Generalization in Kernel Regression |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Outcome-Driven Reinforcement Learning via Variational Inference |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Overcoming Catastrophic Forgetting in Incremental Few-Shot Learning by Finding Flat Minima |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Overcoming the Convex Barrier for Simplex Inputs |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Overcoming the curse of dimensionality with Laplacian regularization in semi-supervised learning |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Overinterpretation reveals image classification model pathologies |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Overlapping Spaces for Compact Graph Representations |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Overparameterization Improves Robustness to Covariate Shift in High Dimensions |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| PARP: Prune, Adjust and Re-Prune for Self-Supervised Speech Recognition |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| PCA Initialization for Approximate Message Passing in Rotationally Invariant Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| PDE-GCN: Novel Architectures for Graph Neural Networks Motivated by Partial Differential Equations |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| PLUGIn: A simple algorithm for inverting generative models with recovery guarantees |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| PLUR: A Unifying, Graph-Based View of Program Learning, Understanding, and Repair |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| POODLE: Improving Few-shot Learning via Penalizing Out-of-Distribution Samples |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| PSD Representations for Effective Probability Models |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| PTR: A Benchmark for Part-based Conceptual, Relational, and Physical Reasoning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Panoptic 3D Scene Reconstruction From a Single RGB Image |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| ParK: Sound and Efficient Kernel Ridge Regression by Feature Space Partitions |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Parallel Bayesian Optimization of Multiple Noisy Objectives with Expected Hypervolume Improvement |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Parallel and Efficient Hierarchical k-Median Clustering |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Parallelizing Thompson Sampling |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Parameter Inference with Bifurcation Diagrams |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Parameter Prediction for Unseen Deep Architectures |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Parameter-free HE-friendly Logistic Regression |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Parameterized Knowledge Transfer for Personalized Federated Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Parametric Complexity Bounds for Approximating PDEs with Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Parametrized Quantum Policies for Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Pareto Domain Adaptation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Pareto-Optimal Learning-Augmented Algorithms for Online Conversion Problems |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Partial success in closing the gap between human and machine vision |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| PartialFed: Cross-Domain Personalized Federated Learning via Partial Initialization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Particle Cloud Generation with Message Passing Generative Adversarial Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Particle Dual Averaging: Optimization of Mean Field Neural Network with Global Convergence Rate Analysis |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Partition and Code: learning how to compress graphs |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Partition-Based Formulations for Mixed-Integer Optimization of Trained ReLU Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Passive attention in artificial neural networks predicts human visual selectivity |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| PatchGame: Learning to Signal Mid-level Patches in Referential Games |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Pay Attention to MLPs |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Pay Better Attention to Attention: Head Selection in Multilingual and Multi-Domain Sequence Modeling |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Per-Pixel Classification is Not All You Need for Semantic Segmentation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| PerSim: Data-Efficient Offline Reinforcement Learning with Heterogeneous Agents via Personalized Simulators |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Perceptual Score: What Data Modalities Does Your Model Perceive? |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Periodic Activation Functions Induce Stationarity |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Permutation-Invariant Variational Autoencoder for Graph-Level Representation Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Permuton-induced Chinese Restaurant Process |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Personalized Federated Learning With Gaussian Processes |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Perturb-and-max-product: Sampling and learning in discrete energy-based models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Perturbation Theory for the Information Bottleneck |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Perturbation-based Regret Analysis of Predictive Control in Linear Time Varying Systems |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Pessimism Meets Invariance: Provably Efficient Offline Mean-Field Multi-Agent RL |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| PettingZoo: Gym for Multi-Agent Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Photonic Differential Privacy with Direct Feedback Alignment |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Physics-Aware Downsampling with Deep Learning for Scalable Flood Modeling |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Physics-Integrated Variational Autoencoders for Robust and Interpretable Generative Modeling |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| PiRank: Scalable Learning To Rank via Differentiable Sorting |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Pipeline Combinators for Gradual AutoML |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Piper: Multidimensional Planner for DNN Parallelization |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Planning from Pixels in Environments with Combinatorially Hard Search Spaces |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Play to Grade: Testing Coding Games as Classifying Markov Decision Process |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| PlayVirtual: Augmenting Cycle-Consistent Virtual Trajectories for Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Pointwise Bounds for Distribution Estimation under Communication Constraints |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| PolarStream: Streaming Object Detection and Segmentation with Polar Pillars |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Policy Finetuning: Bridging Sample-Efficient Offline and Online Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Policy Learning Using Weak Supervision |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Policy Optimization in Adversarial MDPs: Improved Exploration via Dilated Bonuses |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Pooling by Sliced-Wasserstein Embedding |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| PortaSpeech: Portable and High-Quality Generative Text-to-Speech |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Post-Contextual-Bandit Inference |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Post-Training Quantization for Vision Transformer |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Post-Training Sparsity-Aware Quantization |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Post-processing for Individual Fairness |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Posterior Collapse and Latent Variable Non-identifiability |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Posterior Meta-Replay for Continual Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Powerpropagation: A sparsity inducing weight reparameterisation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Practical Large-Scale Linear Programming using Primal-Dual Hybrid Gradient |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Practical Near Neighbor Search via Group Testing |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Practical, Provably-Correct Interactive Learning in the Realizable Setting: The Power of True Believers |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Pragmatic Image Compression for Human-in-the-Loop Decision-Making |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Precise characterization of the prior predictive distribution of deep ReLU networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Preconditioned Gradient Descent for Over-Parameterized Nonconvex Matrix Factorization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Predicting Deep Neural Network Generalization with Perturbation Response Curves |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Predicting Event Memorability from Contextual Visual Semantics |
❌ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Predicting Molecular Conformation via Dynamic Graph Score Matching |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Predicting What You Already Know Helps: Provable Self-Supervised Learning |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Predify: Augmenting deep neural networks with brain-inspired predictive coding dynamics |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| PreferenceNet: Encoding Human Preferences in Auction Design with Deep Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Preserved central model for faster bidirectional compression in distributed settings |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Pretraining Representations for Data-Efficient Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Prior-independent Dynamic Auctions for a Value-maximizing Buyer |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Private Non-smooth ERM and SCO in Subquadratic Steps |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Private and Non-private Uniformity Testing for Ranking Data |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Private learning implies quantum stability |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Privately Learning Mixtures of Axis-Aligned Gaussians |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Privately Learning Subspaces |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Privately Publishable Per-instance Privacy |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| ProTo: Program-Guided Transformer for Program-Guided Tasks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Probabilistic Attention for Interactive Segmentation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Probabilistic Entity Representation Model for Reasoning over Knowledge Graphs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
4 |
| Probabilistic Forecasting: A Level-Set Approach |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Probabilistic Margins for Instance Reweighting in Adversarial Training |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Probabilistic Tensor Decomposition of Neural Population Spiking Activity |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Probabilistic Transformer For Time Series Analysis |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Probability Paths and the Structure of Predictions over Time |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Probing Inter-modality: Visual Parsing with Self-Attention for Vision-and-Language Pre-training |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Process for Adapting Language Models to Society (PALMS) with Values-Targeted Datasets |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Profiling Pareto Front With Multi-Objective Stein Variational Gradient Descent |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Program Synthesis Guided Reinforcement Learning for Partially Observed Environments |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Progressive Coordinate Transforms for Monocular 3D Object Detection |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Progressive Feature Interaction Search for Deep Sparse Network |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Projected GANs Converge Faster |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Proper Value Equivalence |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Property-Aware Relation Networks for Few-Shot Molecular Property Prediction |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Proportional Participatory Budgeting with Additive Utilities |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Prototypical Cross-Attention Networks for Multiple Object Tracking and Segmentation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Provable Benefits of Actor-Critic Methods for Offline Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Provable Guarantees for Self-Supervised Deep Learning with Spectral Contrastive Loss |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Provable Model-based Nonlinear Bandit and Reinforcement Learning: Shelve Optimism, Embrace Virtual Curvature |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Provable Representation Learning for Imitation with Contrastive Fourier Features |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Provably Efficient Black-Box Action Poisoning Attacks Against Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Provably Efficient Causal Reinforcement Learning with Confounded Observational Data |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Provably Efficient Reinforcement Learning with Linear Function Approximation under Adaptivity Constraints |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Provably Faster Algorithms for Bilevel Optimization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Provably Strict Generalisation Benefit for Invariance in Kernel Methods |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Provably efficient multi-task reinforcement learning with model transfer |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Provably efficient, succinct, and precise explanations |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Proxy Convexity: A Unified Framework for the Analysis of Neural Networks Trained by Gradient Descent |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Proxy-Normalizing Activations to Match Batch Normalization while Removing Batch Dependence |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Pruning Randomly Initialized Neural Networks with Iterative Randomization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Pseudo-Spherical Contrastive Divergence |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Pure Exploration in Kernel and Neural Bandits |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Qimera: Data-free Quantization with Synthetic Boundary Supporting Samples |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Qu-ANTI-zation: Exploiting Quantization Artifacts for Achieving Adversarial Outcomes |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| QuPeD: Quantized Personalization via Distillation with Applications to Federated Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Quantifying and Improving Transferability in Domain Generalization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| R-Drop: Regularized Dropout for Neural Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| RED : Looking for Redundancies for Data-FreeStructured Compression of Deep Neural Networks |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| REMIPS: Physically Consistent 3D Reconstruction of Multiple Interacting People under Weak Supervision |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| RETRIEVE: Coreset Selection for Efficient and Robust Semi-Supervised Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| RIM: Reliable Influence-based Active Learning on Graphs |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| RL for Latent MDPs: Regret Guarantees and a Lower Bound |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| RLlib Flow: Distributed Reinforcement Learning is a Dataflow Problem |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| RMIX: Learning Risk-Sensitive Policies for Cooperative Reinforcement Learning Agents |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| RMM: Reinforced Memory Management for Class-Incremental Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| ROI Maximization in Stochastic Online Decision-Making |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Random Noise Defense Against Query-Based Black-Box Attacks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Random Shuffling Beats SGD Only After Many Epochs on Ill-Conditioned Problems |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Rank Overspecified Robust Matrix Recovery: Subgradient Method and Exact Recovery |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Ranking Policy Decisions |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Rate-Optimal Subspace Estimation on Random Graphs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Rates of Estimation of Optimal Transport Maps using Plug-in Estimators via Barycentric Projections |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Raw Nav-merge Seismic Data to Subsurface Properties with MLP based Multi-Modal Information Unscrambler |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Re-ranking for image retrieval and transductive few-shot classification |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| ReAct: Out-of-distribution Detection With Rectified Activations |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| ReLU Regression with Massart Noise |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| ReSSL: Relational Self-Supervised Learning with Weak Augmentation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Realistic evaluation of transductive few-shot learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Rebooting ACGAN: Auxiliary Classifier GANs with Stable Training |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Rebounding Bandits for Modeling Satiation Effects |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
3 |
| Recognizing Vector Graphics without Rasterization |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Reconstruction for Powerful Graph Representations |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Recovering Latent Causal Factor for Generalization to Distributional Shifts |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Recovery Analysis for Plug-and-Play Priors using the Restricted Eigenvalue Condition |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Rectangular Flows for Manifold Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Rectifying the Shortcut Learning of Background for Few-Shot Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Recurrence along Depth: Deep Convolutional Neural Networks with Recurrent Layer Aggregation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Recurrent Bayesian Classifier Chains for Exact Multi-Label Classification |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Recurrent Submodular Welfare and Matroid Blocking Semi-Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Recursive Bayesian Networks: Generalising and Unifying Probabilistic Context-Free Grammars and Dynamic Bayesian Networks |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Recursive Causal Structure Learning in the Presence of Latent Variables and Selection Bias |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Redesigning the Transformer Architecture with Insights from Multi-particle Dynamical Systems |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Reducing Collision Checking for Sampling-Based Motion Planning Using Graph Neural Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Reducing Information Bottleneck for Weakly Supervised Semantic Segmentation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Reducing the Covariate Shift by Mirror Samples in Cross Domain Alignment |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Referring Transformer: A One-step Approach to Multi-task Visual Grounding |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Refined Learning Bounds for Kernel and Approximate $k$-Means |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Refining Language Models with Compositional Explanations |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Reformulating Zero-shot Action Recognition for Multi-label Actions |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Regime Switching Bandits |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Regret Bounds for Gaussian-Process Optimization in Large Domains |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Regret Minimization Experience Replay in Off-Policy Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Regularization in ResNet with Stochastic Depth |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Regularized Frank-Wolfe for Dense CRFs: Generalizing Mean Field and Beyond |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Regularized Softmax Deep Multi-Agent Q-Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Regulating algorithmic filtering on social media |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Reinforced Few-Shot Acquisition Function Learning for Bayesian Optimization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Reinforcement Learning Enhanced Explainer for Graph Neural Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Reinforcement Learning based Disease Progression Model for Alzheimer’s Disease |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Reinforcement Learning in Linear MDPs: Constant Regret and Representation Selection |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Reinforcement Learning in Newcomblike Environments |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Reinforcement Learning in Reward-Mixing MDPs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Reinforcement Learning with Latent Flow |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Reinforcement Learning with State Observation Costs in Action-Contingent Noiselessly Observable Markov Decision Processes |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Reinforcement learning for optimization of variational quantum circuit architectures |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Relational Self-Attention: What's Missing in Attention for Video Understanding |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Relative Flatness and Generalization |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Relative Uncertainty Learning for Facial Expression Recognition |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Relative stability toward diffeomorphisms indicates performance in deep nets |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Relaxed Marginal Consistency for Differentially Private Query Answering |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Relaxing Local Robustness |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| RelaySum for Decentralized Deep Learning on Heterogeneous Data |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Reliable Causal Discovery with Improved Exact Search and Weaker Assumptions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Reliable Decisions with Threshold Calibration |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
5 |
| Reliable Estimation of KL Divergence using a Discriminator in Reproducing Kernel Hilbert Space |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Reliable Post hoc Explanations: Modeling Uncertainty in Explainability |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Reliable and Trustworthy Machine Learning for Health Using Dataset Shift Detection |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Remember What You Want to Forget: Algorithms for Machine Unlearning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Removing Inter-Experimental Variability from Functional Data in Systems Neuroscience |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Renyi Differential Privacy of The Subsampled Shuffle Model In Distributed Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Replacing Rewards with Examples: Example-Based Policy Search via Recursive Classification |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Replay-Guided Adversarial Environment Design |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
6 |
| Representation Costs of Linear Neural Networks: Analysis and Design |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Representation Learning Beyond Linear Prediction Functions |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Representation Learning for Event-based Visuomotor Policies |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Representation Learning on Spatial Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Representer Point Selection via Local Jacobian Expansion for Post-hoc Classifier Explanation of Deep Neural Networks and Ensemble Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Representing Hyperbolic Space Accurately using Multi-Component Floats |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Representing Long-Range Context for Graph Neural Networks with Global Attention |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Repulsive Deep Ensembles are Bayesian |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| ResNEsts and DenseNEsts: Block-based DNN Models with Improved Representation Guarantees |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| ResT: An Efficient Transformer for Visual Recognition |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Residual Pathway Priors for Soft Equivariance Constraints |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Residual Relaxation for Multi-view Representation Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Residual2Vec: Debiasing graph embedding with random graphs |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Rethinking Calibration of Deep Neural Networks: Do Not Be Afraid of Overconfidence |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Rethinking Graph Transformers with Spectral Attention |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Rethinking Neural Operations for Diverse Tasks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Rethinking Space-Time Networks with Improved Memory Coverage for Efficient Video Object Segmentation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Rethinking and Reweighting the Univariate Losses for Multi-Label Ranking: Consistency and Generalization |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Rethinking conditional GAN training: An approach using geometrically structured latent manifolds |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Rethinking gradient sparsification as total error minimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Rethinking the Pruning Criteria for Convolutional Neural Network |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Rethinking the Variational Interpretation of Accelerated Optimization Methods |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Retiring Adult: New Datasets for Fair Machine Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Reusing Combinatorial Structure: Faster Iterative Projections over Submodular Base Polytopes |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Revealing and Protecting Labels in Distributed Training |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Revenue maximization via machine learning with noisy data |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Reverse engineering learned optimizers reveals known and novel mechanisms |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Reverse engineering recurrent neural networks with Jacobian switching linear dynamical systems |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Reverse-Complement Equivariant Networks for DNA Sequences |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Revisit Multimodal Meta-Learning through the Lens of Multi-Task Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Revisiting 3D Object Detection From an Egocentric Perspective |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Revisiting Contrastive Methods for Unsupervised Learning of Visual Representations |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Revisiting Deep Learning Models for Tabular Data |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Revisiting Discriminator in GAN Compression: A Generator-discriminator Cooperative Compression Scheme |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Revisiting Hilbert-Schmidt Information Bottleneck for Adversarial Robustness |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Revisiting Model Stitching to Compare Neural Representations |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Revisiting ResNets: Improved Training and Scaling Strategies |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Revisiting Smoothed Online Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Revisiting the Calibration of Modern Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Revitalizing CNN Attention via Transformers in Self-Supervised Visual Representation Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Reward is enough for convex MDPs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Reward-Free Model-Based Reinforcement Learning with Linear Function Approximation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Risk Bounds and Calibration for a Smart Predict-then-Optimize Method |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Risk Bounds for Over-parameterized Maximum Margin Classification on Sub-Gaussian Mixtures |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Risk Minimization from Adaptively Collected Data: Guarantees for Supervised and Policy Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Risk Monotonicity in Statistical Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Risk-Averse Bayes-Adaptive Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Risk-Aware Transfer in Reinforcement Learning using Successor Features |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Risk-averse Heteroscedastic Bayesian Optimization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| RoMA: Robust Model Adaptation for Offline Model-based Optimization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Robust Allocations with Diversity Constraints |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Robust Auction Design in the Auto-bidding World |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Robust Compressed Sensing MRI with Deep Generative Priors |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Robust Contrastive Learning Using Negative Samples with Diminished Semantics |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Robust Counterfactual Explanations on Graph Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Robust Deep Reinforcement Learning through Adversarial Loss |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Robust Generalization despite Distribution Shift via Minimum Discriminating Information |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| Robust Implicit Networks via Non-Euclidean Contractions |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Robust Inverse Reinforcement Learning under Transition Dynamics Mismatch |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Robust Learning of Optimal Auctions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Robust Online Correlation Clustering |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Robust Optimization for Multilingual Translation with Imbalanced Data |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Robust Pose Estimation in Crowded Scenes with Direct Pose-Level Inference |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Robust Predictable Control |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Robust Regression Revisited: Acceleration and Improved Estimation Rates |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Robust Visual Reasoning via Language Guided Neural Module Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Robust and Decomposable Average Precision for Image Retrieval |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Robust and Fully-Dynamic Coreset for Continuous-and-Bounded Learning (With Outliers) Problems |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
2 |
| Robust and differentially private mean estimation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Robustifying Algorithms of Learning Latent Trees with Vector Variables |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Robustness between the worst and average case |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Robustness of Graph Neural Networks at Scale |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Robustness via Uncertainty-aware Cycle Consistency |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Rot-Pro: Modeling Transitivity by Projection in Knowledge Graph Embedding |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Roto-translated Local Coordinate Frames For Interacting Dynamical Systems |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Row-clustering of a Point Process-valued Matrix |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| S$^3$: Sign-Sparse-Shift Reparametrization for Effective Training of Low-bit Shift Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| SADGA: Structure-Aware Dual Graph Aggregation Network for Text-to-SQL |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| SAPE: Spatially-Adaptive Progressive Encoding for Neural Optimization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| SBO-RNN: Reformulating Recurrent Neural Networks via Stochastic Bilevel Optimization |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| SE(3)-equivariant prediction of molecular wavefunctions and electronic densities |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| SEAL: Self-supervised Embodied Active Learning using Exploration and 3D Consistency |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| SGD: The Role of Implicit Regularization, Batch-size and Multiple-epochs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| SILG: The Multi-domain Symbolic Interactive Language Grounding Benchmark |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| SIMILAR: Submodular Information Measures Based Active Learning In Realistic Scenarios |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| SIMONe: View-Invariant, Temporally-Abstracted Object Representations via Unsupervised Video Decomposition |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| SLOE: A Faster Method for Statistical Inference in High-Dimensional Logistic Regression |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| SNIPS: Solving Noisy Inverse Problems Stochastically |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| SOAT: A Scene- and Object-Aware Transformer for Vision-and-Language Navigation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| SOFT: Softmax-free Transformer with Linear Complexity |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| SOLQ: Segmenting Objects by Learning Queries |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| SOPE: Spectrum of Off-Policy Estimators |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| SPANN: Highly-efficient Billion-scale Approximate Nearest Neighborhood Search |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| SQALER: Scaling Question Answering by Decoupling Multi-Hop and Logical Reasoning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| SSAL: Synergizing between Self-Training and Adversarial Learning for Domain Adaptive Object Detection |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| SSMF: Shifting Seasonal Matrix Factorization |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| SSUL: Semantic Segmentation with Unknown Label for Exemplar-based Class-Incremental Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| STEM: A Stochastic Two-Sided Momentum Algorithm Achieving Near-Optimal Sample and Communication Complexities for Federated Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| STEP: Out-of-Distribution Detection in the Presence of Limited In-Distribution Labeled Data |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| STORM+: Fully Adaptive SGD with Recursive Momentum for Nonconvex Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| SUPER-ADAM: Faster and Universal Framework of Adaptive Gradients |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| SWAD: Domain Generalization by Seeking Flat Minima |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Safe Policy Optimization with Local Generalized Linear Function Approximations |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Safe Pontryagin Differentiable Programming |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Safe Reinforcement Learning by Imagining the Near Future |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Safe Reinforcement Learning with Natural Language Constraints |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Sageflow: Robust Federated Learning against Both Stragglers and Adversaries |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| SalKG: Learning From Knowledge Graph Explanations for Commonsense Reasoning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Sample Complexity Bounds for Active Ranking from Multi-wise Comparisons |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Sample Complexity of Tree Search Configuration: Cutting Planes and Beyond |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Sample Selection for Fair and Robust Training |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Sample-Efficient Learning of Stackelberg Equilibria in General-Sum Games |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Sample-Efficient Reinforcement Learning Is Feasible for Linearly Realizable MDPs with Limited Revisiting |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Sample-Efficient Reinforcement Learning for Linearly-Parameterized MDPs with a Generative Model |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Sampling with Trusthworthy Constraints: A Variational Gradient Framework |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Sanity Checks for Lottery Tickets: Does Your Winning Ticket Really Win the Jackpot? |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Scalable Bayesian GPFA with automatic relevance determination and discrete noise models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Scalable Diverse Model Selection for Accessible Transfer Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Scalable Inference in SDEs by Direct Matching of the Fokker–Planck–Kolmogorov Equation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Scalable Inference of Sparsely-changing Gaussian Markov Random Fields |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Scalable Intervention Target Estimation in Linear Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Scalable Neural Data Server: A Data Recommender for Transfer Learning |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Scalable Online Planning via Reinforcement Learning Fine-Tuning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Scalable Quasi-Bayesian Inference for Instrumental Variable Regression |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Scalable Rule-Based Representation Learning for Interpretable Classification |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Scalable Thompson Sampling using Sparse Gaussian Process Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Scalable and Stable Surrogates for Flexible Classifiers with Fairness Constraints |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Scalars are universal: Equivariant machine learning, structured like classical physics |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| ScaleCert: Scalable Certified Defense against Adversarial Patches with Sparse Superficial Layers |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Scaling Ensemble Distribution Distillation to Many Classes with Proxy Targets |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Scaling Gaussian Processes with Derivative Information Using Variational Inference |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Scaling Neural Tangent Kernels via Sketching and Random Features |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Scaling Up Exact Neural Network Compression by ReLU Stability |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Scaling Vision with Sparse Mixture of Experts |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Scaling up Continuous-Time Markov Chains Helps Resolve Underspecification |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Scallop: From Probabilistic Deductive Databases to Scalable Differentiable Reasoning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Scatterbrain: Unifying Sparse and Low-rank Attention |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Scheduling jobs with stochastic holding costs |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Score-based Generative Modeling in Latent Space |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Score-based Generative Neural Networks for Large-Scale Optimal Transport |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Searching Parameterized AP Loss for Object Detection |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Searching for Efficient Transformers for Language Modeling |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Searching the Search Space of Vision Transformer |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Second-Order Neural ODE Optimizer |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| See More for Scene: Pairwise Consistency Learning for Scene Classification |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| SegFormer: Simple and Efficient Design for Semantic Segmentation with Transformers |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Selective Sampling for Online Best-arm Identification |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Self-Adaptable Point Processes with Nonparametric Time Decays |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Self-Consistent Models and Values |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Self-Diagnosing GAN: Diagnosing Underrepresented Samples in Generative Adversarial Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Self-Instantiated Recurrent Units with Dynamic Soft Recursion |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Self-Interpretable Model with Transformation Equivariant Interpretation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Self-Paced Contrastive Learning for Semi-supervised Medical Image Segmentation with Meta-labels |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Self-Supervised Bug Detection and Repair |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Self-Supervised GANs with Label Augmentation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Self-Supervised Learning Disentangled Group Representation as Feature |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Self-Supervised Learning of Event-Based Optical Flow with Spiking Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Self-Supervised Learning with Kernel Dependence Maximization |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Self-Supervised Multi-Object Tracking with Cross-input Consistency |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Self-Supervised Representation Learning on Neural Network Weights for Model Characteristic Prediction |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Semi-Supervised Semantic Segmentation via Adaptive Equalization Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Semialgebraic Representation of Monotone Deep Equilibrium Models and Applications to Certification |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Separation Results between Fixed-Kernel and Feature-Learning Probability Metrics |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Sequence-to-Sequence Learning with Latent Neural Grammars |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Sequential Algorithms for Testing Closeness of Distributions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Sequential Causal Imitation Learning with Unobserved Confounders |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Set Prediction in the Latent Space |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Settling the Variance of Multi-Agent Policy Gradients |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Shape As Points: A Differentiable Poisson Solver |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Shape Registration in the Time of Transformers |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Shape from Blur: Recovering Textured 3D Shape and Motion of Fast Moving Objects |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Shape your Space: A Gaussian Mixture Regularization Approach to Deterministic Autoencoders |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Shapeshifter: a Parameter-efficient Transformer using Factorized Reshaped Matrices |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Shaping embodied agent behavior with activity-context priors from egocentric video |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Shapley Residuals: Quantifying the limits of the Shapley value for explanations |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Shared Independent Component Analysis for Multi-Subject Neuroimaging |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Sharp Impossibility Results for Hyper-graph Testing |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Shift Invariance Can Reduce Adversarial Robustness |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Shift-Robust GNNs: Overcoming the Limitations of Localized Graph Training data |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Shifted Chunk Transformer for Spatio-Temporal Representational Learning |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Sifting through the noise: Universal first-order methods for stochastic variational inequalities |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Sim and Real: Better Together |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| SimiGrad: Fine-Grained Adaptive Batching for Large Scale Training using Gradient Similarity Measurement |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Similarity and Matching of Neural Network Representations |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Simple Stochastic and Online Gradient Descent Algorithms for Pairwise Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Simple steps are all you need: Frank-Wolfe and generalized self-concordant functions |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Single Layer Predictive Normalized Maximum Likelihood for Out-of-Distribution Detection |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| SketchGen: Generating Constrained CAD Sketches |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Skipping the Frame-Level: Event-Based Piano Transcription With Neural Semi-CRFs |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Skyformer: Remodel Self-Attention with Gaussian Kernel and Nystr\"om Method |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Slice Sampling Reparameterization Gradients |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Sliced Mutual Information: A Scalable Measure of Statistical Dependence |
✅ |
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✅ |
❌ |
❌ |
❌ |
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3 |
| Slow Learning and Fast Inference: Efficient Graph Similarity Computation via Knowledge Distillation |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Small random initialization is akin to spectral learning: Optimization and generalization guarantees for overparameterized low-rank matrix reconstruction |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Smooth Bilevel Programming for Sparse Regularization |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
4 |
| Smooth Normalizing Flows |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
2 |
| SmoothMix: Training Confidence-calibrated Smoothed Classifiers for Certified Robustness |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Smoothness Matrices Beat Smoothness Constants: Better Communication Compression Techniques for Distributed Optimization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Snowflake: Scaling GNNs to high-dimensional continuous control via parameter freezing |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Soft Calibration Objectives for Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Solving Graph-based Public Goods Games with Tree Search and Imitation Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Solving Min-Max Optimization with Hidden Structure via Gradient Descent Ascent |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Solving Soft Clustering Ensemble via $k$-Sparse Discrete Wasserstein Barycenter |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Space-time Mixing Attention for Video Transformer |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Sparse Deep Learning: A New Framework Immune to Local Traps and Miscalibration |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Sparse Flows: Pruning Continuous-depth Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Sparse Quadratic Optimisation over the Stiefel Manifold with Application to Permutation Synchronisation |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Sparse Spiking Gradient Descent |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
2 |
| Sparse Steerable Convolutions: An Efficient Learning of SE(3)-Equivariant Features for Estimation and Tracking of Object Poses in 3D Space |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Sparse Training via Boosting Pruning Plasticity with Neuroregeneration |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Sparse Uncertainty Representation in Deep Learning with Inducing Weights |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Sparse is Enough in Scaling Transformers |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Sparsely Changing Latent States for Prediction and Planning in Partially Observable Domains |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Spatial Ensemble: a Novel Model Smoothing Mechanism for Student-Teacher Framework |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Spatial-Temporal Super-Resolution of Satellite Imagery via Conditional Pixel Synthesis |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Spatio-Temporal Variational Gaussian Processes |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Spatiotemporal Joint Filter Decomposition in 3D Convolutional Neural Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Spectral embedding for dynamic networks with stability guarantees |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Spectrum-to-Kernel Translation for Accurate Blind Image Super-Resolution |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Speech Separation Using an Asynchronous Fully Recurrent Convolutional Neural Network |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Speech-T: Transducer for Text to Speech and Beyond |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Speedy Performance Estimation for Neural Architecture Search |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Spherical Motion Dynamics: Learning Dynamics of Normalized Neural Network using SGD and Weight Decay |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Spot the Difference: Detection of Topological Changes via Geometric Alignment |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Square Root Principal Component Pursuit: Tuning-Free Noisy Robust Matrix Recovery |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Stability & Generalisation of Gradient Descent for Shallow Neural Networks without the Neural Tangent Kernel |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Stability and Deviation Optimal Risk Bounds with Convergence Rate $O(1/n)$ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Stability and Generalization of Bilevel Programming in Hyperparameter Optimization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Stabilizing Deep Q-Learning with ConvNets and Vision Transformers under Data Augmentation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Stabilizing Dynamical Systems via Policy Gradient Methods |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Stable Neural ODE with Lyapunov-Stable Equilibrium Points for Defending Against Adversarial Attacks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Stable, Fast and Accurate: Kernelized Attention with Relative Positional Encoding |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Stateful ODE-Nets using Basis Function Expansions |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Stateful Strategic Regression |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Statistical Inference with M-Estimators on Adaptively Collected Data |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Statistical Query Lower Bounds for List-Decodable Linear Regression |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Statistical Regeneration Guarantees of the Wasserstein Autoencoder with Latent Space Consistency |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Statistical Undecidability in Linear, Non-Gaussian Causal Models in the Presence of Latent Confounders |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Statistically and Computationally Efficient Linear Meta-representation Learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Stochastic $L^\natural$-convex Function Minimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Stochastic Anderson Mixing for Nonconvex Stochastic Optimization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Stochastic Bias-Reduced Gradient Methods |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Stochastic Gradient Descent-Ascent and Consensus Optimization for Smooth Games: Convergence Analysis under Expected Co-coercivity |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Stochastic Multi-Armed Bandits with Control Variates |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Stochastic Online Linear Regression: the Forward Algorithm to Replace Ridge |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Stochastic Optimization of Areas Under Precision-Recall Curves with Provable Convergence |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Stochastic Shortest Path: Minimax, Parameter-Free and Towards Horizon-Free Regret |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Stochastic Solutions for Linear Inverse Problems using the Prior Implicit in a Denoiser |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Stochastic bandits with groups of similar arms. |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Stochastic optimization under time drift: iterate averaging, step-decay schedules, and high probability guarantees |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Storchastic: A Framework for General Stochastic Automatic Differentiation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Strategic Behavior is Bliss: Iterative Voting Improves Social Welfare |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Streaming Belief Propagation for Community Detection |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Streaming Linear System Identification with Reverse Experience Replay |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Stronger NAS with Weaker Predictors |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Structural Credit Assignment in Neural Networks using Reinforcement Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Structure learning in polynomial time: Greedy algorithms, Bregman information, and exponential families |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Structure-Aware Random Fourier Kernel for Graphs |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Structured Denoising Diffusion Models in Discrete State-Spaces |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Structured Dropout Variational Inference for Bayesian Neural Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Structured Reordering for Modeling Latent Alignments in Sequence Transduction |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Structured in Space, Randomized in Time: Leveraging Dropout in RNNs for Efficient Training |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Stylized Dialogue Generation with Multi-Pass Dual Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Sub-Linear Memory: How to Make Performers SLiM |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| SubTab: Subsetting Features of Tabular Data for Self-Supervised Representation Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Subgame solving without common knowledge |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Subgaussian and Differentiable Importance Sampling for Off-Policy Evaluation and Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Subgoal Search For Complex Reasoning Tasks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Subgraph Federated Learning with Missing Neighbor Generation |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Subgroup Generalization and Fairness of Graph Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
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3 |
| Submodular + Concave |
✅ |
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✅ |
❌ |
✅ |
❌ |
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5 |
| Subquadratic Overparameterization for Shallow Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Successor Feature Landmarks for Long-Horizon Goal-Conditioned Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Supercharging Imbalanced Data Learning With Energy-based Contrastive Representation Transfer |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Supervising the Transfer of Reasoning Patterns in VQA |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Support Recovery of Sparse Signals from a Mixture of Linear Measurements |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
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1 |
| Support vector machines and linear regression coincide with very high-dimensional features |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Surrogate Regret Bounds for Polyhedral Losses |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| SurvITE: Learning Heterogeneous Treatment Effects from Time-to-Event Data |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| SyMetric: Measuring the Quality of Learnt Hamiltonian Dynamics Inferred from Vision |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Symbolic Regression via Deep Reinforcement Learning Enhanced Genetic Programming Seeding |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Symplectic Adjoint Method for Exact Gradient of Neural ODE with Minimal Memory |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| SyncTwin: Treatment Effect Estimation with Longitudinal Outcomes |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Synthetic Design: An Optimization Approach to Experimental Design with Synthetic Controls |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
3 |
| Systematic Generalization with Edge Transformers |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| T-LoHo: A Bayesian Regularization Model for Structured Sparsity and Smoothness on Graphs |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| TAAC: Temporally Abstract Actor-Critic for Continuous Control |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| TNASP: A Transformer-based NAS Predictor with a Self-evolution Framework |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| TOHAN: A One-step Approach towards Few-shot Hypothesis Adaptation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| TRS: Transferability Reduced Ensemble via Promoting Gradient Diversity and Model Smoothness |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| TTT++: When Does Self-Supervised Test-Time Training Fail or Thrive? |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| TacticZero: Learning to Prove Theorems from Scratch with Deep Reinforcement Learning |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Tactical Optimism and Pessimism for Deep Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Tailoring: encoding inductive biases by optimizing unsupervised objectives at prediction time |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Taming Communication and Sample Complexities in Decentralized Policy Evaluation for Cooperative Multi-Agent Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Targeted Neural Dynamical Modeling |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Task-Adaptive Neural Network Search with Meta-Contrastive Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Task-Agnostic Undesirable Feature Deactivation Using Out-of-Distribution Data |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Taxonomizing local versus global structure in neural network loss landscapes |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Teachable Reinforcement Learning via Advice Distillation |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Teaching an Active Learner with Contrastive Examples |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Teaching via Best-Case Counterexamples in the Learning-with-Equivalence-Queries Paradigm |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Techniques for Symbol Grounding with SATNet |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Temporal-attentive Covariance Pooling Networks for Video Recognition |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Temporally Abstract Partial Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Tensor Normal Training for Deep Learning Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Tensor decompositions of higher-order correlations by nonlinear Hebbian plasticity |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Terra: Imperative-Symbolic Co-Execution of Imperative Deep Learning Programs |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
❌ |
3 |
| Test-Time Classifier Adjustment Module for Model-Agnostic Domain Generalization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Test-Time Personalization with a Transformer for Human Pose Estimation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Test-time Collective Prediction |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| TestRank: Bringing Order into Unlabeled Test Instances for Deep Learning Tasks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Testing Probabilistic Circuits |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| The Adaptive Doubly Robust Estimator and a Paradox Concerning Logging Policy |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| The Benefits of Implicit Regularization from SGD in Least Squares Problems |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| The Causal-Neural Connection: Expressiveness, Learnability, and Inference |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Complexity of Bayesian Network Learning: Revisiting the Superstructure |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| The Complexity of Sparse Tensor PCA |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| The Difficulty of Passive Learning in Deep Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| The Effect of the Intrinsic Dimension on the Generalization of Quadratic Classifiers |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Elastic Lottery Ticket Hypothesis |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| The Emergence of Objectness: Learning Zero-shot Segmentation from Videos |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| The Flip Side of the Reweighted Coin: Duality of Adaptive Dropout and Regularization |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| The Hardness Analysis of Thompson Sampling for Combinatorial Semi-bandits with Greedy Oracle |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| The Image Local Autoregressive Transformer |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Implicit Bias of Minima Stability: A View from Function Space |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| The Inductive Bias of Quantum Kernels |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Lazy Online Subgradient Algorithm is Universal on Strongly Convex Domains |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Limitations of Large Width in Neural Networks: A Deep Gaussian Process Perspective |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| The Limits of Optimal Pricing in the Dark |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| The Many Faces of Adversarial Risk |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| The Out-of-Distribution Problem in Explainability and Search Methods for Feature Importance Explanations |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| The Pareto Frontier of model selection for general Contextual Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| The Role of Global Labels in Few-Shot Classification and How to Infer Them |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| The Semi-Random Satisfaction of Voting Axioms |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Sensory Neuron as a Transformer: Permutation-Invariant Neural Networks for Reinforcement Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Skellam Mechanism for Differentially Private Federated Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| The Unbalanced Gromov Wasserstein Distance: Conic Formulation and Relaxation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| The Utility of Explainable AI in Ad Hoc Human-Machine Teaming |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Value of Information When Deciding What to Learn |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| The balancing principle for parameter choice in distance-regularized domain adaptation |
✅ |
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✅ |
❌ |
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❌ |
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3 |
| The best of both worlds: stochastic and adversarial episodic MDPs with unknown transition |
✅ |
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❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| The decomposition of the higher-order homology embedding constructed from the $k$-Laplacian |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| The effectiveness of feature attribution methods and its correlation with automatic evaluation scores |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| The functional specialization of visual cortex emerges from training parallel pathways with self-supervised predictive learning |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| The future is log-Gaussian: ResNets and their infinite-depth-and-width limit at initialization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| The staircase property: How hierarchical structure can guide deep learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| There Is No Turning Back: A Self-Supervised Approach for Reversibility-Aware Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Think Big, Teach Small: Do Language Models Distil Occam’s Razor? |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Three Operator Splitting with Subgradients, Stochastic Gradients, and Adaptive Learning Rates |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Three-dimensional spike localization and improved motion correction for Neuropixels recordings |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Tight High Probability Bounds for Linear Stochastic Approximation with Fixed Stepsize |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Tighter Expected Generalization Error Bounds via Wasserstein Distance |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Time Discretization-Invariant Safe Action Repetition for Policy Gradient Methods |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Time-independent Generalization Bounds for SGLD in Non-convex Settings |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Time-series Generation by Contrastive Imitation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| To Beam Or Not To Beam: That is a Question of Cooperation for Language GANs |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| To The Point: Correspondence-driven monocular 3D category reconstruction |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| ToAlign: Task-Oriented Alignment for Unsupervised Domain Adaptation |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| TokenLearner: Adaptive Space-Time Tokenization for Videos |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Topic Modeling Revisited: A Document Graph-based Neural Network Perspective |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| TopicNet: Semantic Graph-Guided Topic Discovery |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
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3 |
| Topographic VAEs learn Equivariant Capsules |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Topological Attention for Time Series Forecasting |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Topological Detection of Trojaned Neural Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Topological Relational Learning on Graphs |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Topology-Imbalance Learning for Semi-Supervised Node Classification |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Towards Best-of-All-Worlds Online Learning with Feedback Graphs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Towards Better Understanding of Training Certifiably Robust Models against Adversarial Examples |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Towards Biologically Plausible Convolutional Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Towards Calibrated Model for Long-Tailed Visual Recognition from Prior Perspective |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Towards Context-Agnostic Learning Using Synthetic Data |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Towards Deeper Deep Reinforcement Learning with Spectral Normalization |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Towards Efficient and Effective Adversarial Training |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Towards Enabling Meta-Learning from Target Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Towards Gradient-based Bilevel Optimization with Non-convex Followers and Beyond |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Towards Hyperparameter-free Policy Selection for Offline Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Towards Instance-Optimal Offline Reinforcement Learning with Pessimism |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Towards Lower Bounds on the Depth of ReLU Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
1 |
| Towards Multi-Grained Explainability for Graph Neural Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Towards Open-World Feature Extrapolation: An Inductive Graph Learning Approach |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Towards Optimal Strategies for Training Self-Driving Perception Models in Simulation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Towards Robust Bisimulation Metric Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Towards Robust and Reliable Algorithmic Recourse |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Towards Sample-Optimal Compressive Phase Retrieval with Sparse and Generative Priors |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Towards Sample-efficient Overparameterized Meta-learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Towards Scalable Unpaired Virtual Try-On via Patch-Routed Spatially-Adaptive GAN |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Towards Sharper Generalization Bounds for Structured Prediction |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Towards Stable and Robust AdderNets |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Towards Tight Communication Lower Bounds for Distributed Optimisation |
✅ |
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1 |
| Towards Understanding Cooperative Multi-Agent Q-Learning with Value Factorization |
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2 |
| Towards Understanding Why Lookahead Generalizes Better Than SGD and Beyond |
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❌ |
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6 |
| Towards Unifying Behavioral and Response Diversity for Open-ended Learning in Zero-sum Games |
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3 |
| Towards a Theoretical Framework of Out-of-Distribution Generalization |
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4 |
| Towards a Unified Game-Theoretic View of Adversarial Perturbations and Robustness |
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4 |
| Towards a Unified Information-Theoretic Framework for Generalization |
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0 |
| Towards mental time travel: a hierarchical memory for reinforcement learning agents |
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2 |
| Towards optimally abstaining from prediction with OOD test examples |
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1 |
| Towards robust vision by multi-task learning on monkey visual cortex |
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4 |
| Towards understanding retrosynthesis by energy-based models |
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3 |
| Tracking People with 3D Representations |
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❌ |
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❌ |
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5 |
| Tracking Without Re-recognition in Humans and Machines |
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✅ |
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✅ |
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❌ |
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5 |
| Tractable Density Estimation on Learned Manifolds with Conformal Embedding Flows |
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✅ |
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❌ |
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2 |
| Tractable Regularization of Probabilistic Circuits |
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4 |
| Training Certifiably Robust Neural Networks with Efficient Local Lipschitz Bounds |
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4 |
| Training Feedback Spiking Neural Networks by Implicit Differentiation on the Equilibrium State |
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4 |
| Training Neural Networks is ER-complete |
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0 |
| Training Neural Networks with Fixed Sparse Masks |
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5 |
| Training Over-parameterized Models with Non-decomposable Objectives |
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4 |
| Training for the Future: A Simple Gradient Interpolation Loss to Generalize Along Time |
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❌ |
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5 |
| TransGAN: Two Pure Transformers Can Make One Strong GAN, and That Can Scale Up |
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❌ |
✅ |
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5 |
| TransMIL: Transformer based Correlated Multiple Instance Learning for Whole Slide Image Classification |
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❌ |
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6 |
| TransMatcher: Deep Image Matching Through Transformers for Generalizable Person Re-identification |
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✅ |
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❌ |
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4 |
| Transfer Learning of Graph Neural Networks with Ego-graph Information Maximization |
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❌ |
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❌ |
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4 |
| Transformer in Transformer |
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✅ |
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✅ |
✅ |
❌ |
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5 |
| TransformerFusion: Monocular RGB Scene Reconstruction using Transformers |
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✅ |
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❌ |
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4 |
| Transformers Generalize DeepSets and Can be Extended to Graphs & Hypergraphs |
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✅ |
✅ |
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❌ |
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5 |
| Trash or Treasure? An Interactive Dual-Stream Strategy for Single Image Reflection Separation |
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❌ |
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❌ |
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4 |
| Tree in Tree: from Decision Trees to Decision Graphs |
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❌ |
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6 |
| TriBERT: Human-centric Audio-visual Representation Learning |
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✅ |
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❌ |
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5 |
| True Few-Shot Learning with Language Models |
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✅ |
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✅ |
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❌ |
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5 |
| Truncated Marginal Neural Ratio Estimation |
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❌ |
✅ |
❌ |
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5 |
| Trustworthy Multimodal Regression with Mixture of Normal-inverse Gamma Distributions |
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✅ |
✅ |
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❌ |
❌ |
✅ |
4 |
| Tuning Large Neural Networks via Zero-Shot Hyperparameter Transfer |
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❌ |
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6 |
| Tuning Mixed Input Hyperparameters on the Fly for Efficient Population Based AutoRL |
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✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Turing Completeness of Bounded-Precision Recurrent Neural Networks |
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❌ |
❌ |
❌ |
❌ |
❌ |
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0 |
| Twice regularized MDPs and the equivalence between robustness and regularization |
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❌ |
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✅ |
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4 |
| Twins: Revisiting the Design of Spatial Attention in Vision Transformers |
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❌ |
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6 |
| Two Sides of Meta-Learning Evaluation: In vs. Out of Distribution |
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5 |
| Two steps to risk sensitivity |
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2 |
| Two-sided fairness in rankings via Lorenz dominance |
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1 |
| TöRF: Time-of-Flight Radiance Fields for Dynamic Scene View Synthesis |
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2 |
| UCB-based Algorithms for Multinomial Logistic Regression Bandits |
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2 |
| UFC-BERT: Unifying Multi-Modal Controls for Conditional Image Synthesis |
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3 |
| USCO-Solver: Solving Undetermined Stochastic Combinatorial Optimization Problems |
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3 |
| Ultrahyperbolic Neural Networks |
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4 |
| Unadversarial Examples: Designing Objects for Robust Vision |
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6 |
| Unbalanced Optimal Transport through Non-negative Penalized Linear Regression |
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4 |
| Unbiased Classification through Bias-Contrastive and Bias-Balanced Learning |
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4 |
| Uncertain Decisions Facilitate Better Preference Learning |
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0 |
| Uncertainty Calibration for Ensemble-Based Debiasing Methods |
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3 |
| Uncertainty Quantification and Deep Ensembles |
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3 |
| Uncertainty-Based Offline Reinforcement Learning with Diversified Q-Ensemble |
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5 |
| Uncertainty-Driven Loss for Single Image Super-Resolution |
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3 |
| Understanding Adaptive, Multiscale Temporal Integration In Deep Speech Recognition Systems |
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❌ |
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4 |
| Understanding Bandits with Graph Feedback |
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❌ |
❌ |
❌ |
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1 |
| Understanding Deflation Process in Over-parametrized Tensor Decomposition |
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2 |
| Understanding End-to-End Model-Based Reinforcement Learning Methods as Implicit Parameterization |
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❌ |
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3 |
| Understanding How Encoder-Decoder Architectures Attend |
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✅ |
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1 |
| Understanding Instance-based Interpretability of Variational Auto-Encoders |
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✅ |
✅ |
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❌ |
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5 |
| Understanding Interlocking Dynamics of Cooperative Rationalization |
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4 |
| Understanding Negative Samples in Instance Discriminative Self-supervised Representation Learning |
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5 |
| Understanding Partial Multi-Label Learning via Mutual Information |
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3 |
| Understanding and Improving Early Stopping for Learning with Noisy Labels |
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✅ |
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7 |
| Understanding the Effect of Stochasticity in Policy Optimization |
❌ |
❌ |
❌ |
❌ |
❌ |
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0 |
| Understanding the Generalization Benefit of Model Invariance from a Data Perspective |
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✅ |
✅ |
❌ |
❌ |
❌ |
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4 |
| Understanding the Limits of Unsupervised Domain Adaptation via Data Poisoning |
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✅ |
✅ |
❌ |
✅ |
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5 |
| Understanding the Under-Coverage Bias in Uncertainty Estimation |
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❌ |
✅ |
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❌ |
❌ |
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3 |
| Unfolding Taylor's Approximations for Image Restoration |
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❌ |
✅ |
❌ |
✅ |
❌ |
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3 |
| UniDoc: Unified Pretraining Framework for Document Understanding |
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❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Uniform Concentration Bounds toward a Unified Framework for Robust Clustering |
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❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Uniform Convergence of Interpolators: Gaussian Width, Norm Bounds and Benign Overfitting |
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❌ |
❌ |
❌ |
❌ |
❌ |
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1 |
| Uniform Sampling over Episode Difficulty |
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✅ |
✅ |
❌ |
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4 |
| Uniform-PAC Bounds for Reinforcement Learning with Linear Function Approximation |
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❌ |
❌ |
❌ |
❌ |
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1 |
| Unifying Gradient Estimators for Meta-Reinforcement Learning via Off-Policy Evaluation |
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✅ |
❌ |
❌ |
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3 |
| Unifying Width-Reduced Methods for Quasi-Self-Concordant Optimization |
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❌ |
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1 |
| Unifying lower bounds on prediction dimension of convex surrogates |
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0 |
| Unintended Selection: Persistent Qualification Rate Disparities and Interventions |
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❌ |
❌ |
❌ |
❌ |
❌ |
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1 |
| Unique sparse decomposition of low rank matrices |
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❌ |
❌ |
❌ |
❌ |
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2 |
| Universal Approximation Using Well-Conditioned Normalizing Flows |
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❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Universal Graph Convolutional Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| Universal Off-Policy Evaluation |
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❌ |
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5 |
| Universal Rate-Distortion-Perception Representations for Lossy Compression |
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❌ |
✅ |
❌ |
❌ |
❌ |
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2 |
| Universal Semi-Supervised Learning |
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❌ |
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3 |
| Unlabeled Principal Component Analysis |
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4 |
| Unleashing the Power of Contrastive Self-Supervised Visual Models via Contrast-Regularized Fine-Tuning |
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✅ |
✅ |
❌ |
❌ |
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5 |
| Unsupervised Domain Adaptation with Dynamics-Aware Rewards in Reinforcement Learning |
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❌ |
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2 |
| Unsupervised Foreground Extraction via Deep Region Competition |
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3 |
| Unsupervised Learning of Compositional Energy Concepts |
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❌ |
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3 |
| Unsupervised Motion Representation Learning with Capsule Autoencoders |
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5 |
| Unsupervised Noise Adaptive Speech Enhancement by Discriminator-Constrained Optimal Transport |
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3 |
| Unsupervised Object-Based Transition Models For 3D Partially Observable Environments |
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3 |
| Unsupervised Object-Level Representation Learning from Scene Images |
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4 |
| Unsupervised Part Discovery from Contrastive Reconstruction |
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❌ |
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❌ |
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2 |
| Unsupervised Representation Transfer for Small Networks: I Believe I Can Distill On-the-Fly |
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✅ |
❌ |
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4 |
| Unsupervised Speech Recognition |
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✅ |
❌ |
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3 |
| User-Level Differentially Private Learning via Correlated Sampling |
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❌ |
❌ |
❌ |
❌ |
❌ |
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1 |
| Using Random Effects to Account for High-Cardinality Categorical Features and Repeated Measures in Deep Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| VAST: Value Function Factorization with Variable Agent Sub-Teams |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
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5 |
| VATT: Transformers for Multimodal Self-Supervised Learning from Raw Video, Audio and Text |
❌ |
✅ |
✅ |
✅ |
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❌ |
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5 |
| VQ-GNN: A Universal Framework to Scale up Graph Neural Networks using Vector Quantization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
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6 |
| Validating the Lottery Ticket Hypothesis with Inertial Manifold Theory |
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❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Validation Free and Replication Robust Volume-based Data Valuation |
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✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Variance-Aware Off-Policy Evaluation with Linear Function Approximation |
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✅ |
❌ |
❌ |
❌ |
❌ |
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3 |
| Variational Automatic Curriculum Learning for Sparse-Reward Cooperative Multi-Agent Problems |
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✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Variational Bayesian Optimistic Sampling |
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❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Variational Bayesian Reinforcement Learning with Regret Bounds |
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❌ |
✅ |
✅ |
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5 |
| Variational Continual Bayesian Meta-Learning |
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✅ |
✅ |
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❌ |
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6 |
| Variational Diffusion Models |
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✅ |
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✅ |
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3 |
| Variational Inference for Continuous-Time Switching Dynamical Systems |
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✅ |
❌ |
❌ |
❌ |
❌ |
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3 |
| Variational Model Inversion Attacks |
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✅ |
✅ |
❌ |
❌ |
❌ |
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2 |
| Variational Multi-Task Learning with Gumbel-Softmax Priors |
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✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| Vector-valued Distance and Gyrocalculus on the Space of Symmetric Positive Definite Matrices |
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✅ |
✅ |
✅ |
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❌ |
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5 |
| Vector-valued Gaussian Processes on Riemannian Manifolds via Gauge Independent Projected Kernels |
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✅ |
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❌ |
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❌ |
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3 |
| ViSER: Video-Specific Surface Embeddings for Articulated 3D Shape Reconstruction |
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❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| ViTAE: Vision Transformer Advanced by Exploring Intrinsic Inductive Bias |
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❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| VidLanKD: Improving Language Understanding via Video-Distilled Knowledge Transfer |
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✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Video Instance Segmentation using Inter-Frame Communication Transformers |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
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6 |
| VigDet: Knowledge Informed Neural Temporal Point Process for Coordination Detection on Social Media |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| Visual Adversarial Imitation Learning using Variational Models |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
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3 |
| Visual Search Asymmetry: Deep Nets and Humans Share Similar Inherent Biases |
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✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Visualizing the Emergence of Intermediate Visual Patterns in DNNs |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| VoiceMixer: Adversarial Voice Style Mixup |
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✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Volume Rendering of Neural Implicit Surfaces |
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✅ |
❌ |
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❌ |
✅ |
3 |
| Voxel-based 3D Detection and Reconstruction of Multiple Objects from a Single Image |
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✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Wasserstein Flow Meets Replicator Dynamics: A Mean-Field Analysis of Representation Learning in Actor-Critic |
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❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Weak-shot Fine-grained Classification via Similarity Transfer |
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✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Weighted model estimation for offline model-based reinforcement learning |
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| Weisfeiler and Lehman Go Cellular: CW Networks |
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4 |
| Well-tuned Simple Nets Excel on Tabular Datasets |
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5 |
| What Makes Multi-Modal Learning Better than Single (Provably) |
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2 |
| What Matters for Adversarial Imitation Learning? |
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3 |
| What can linearized neural networks actually say about generalization? |
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3 |
| What training reveals about neural network complexity |
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2 |
| What’s a good imputation to predict with missing values? |
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| When Are Solutions Connected in Deep Networks? |
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| When Expressivity Meets Trainability: Fewer than $n$ Neurons Can Work |
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4 |
| When False Positive is Intolerant: End-to-End Optimization with Low FPR for Multipartite Ranking |
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6 |
| When Is Generalizable Reinforcement Learning Tractable? |
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| When Is Unsupervised Disentanglement Possible? |
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| When does Contrastive Learning Preserve Adversarial Robustness from Pretraining to Finetuning? |
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3 |
| When in Doubt: Neural Non-Parametric Uncertainty Quantification for Epidemic Forecasting |
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| Which Mutual-Information Representation Learning Objectives are Sufficient for Control? |
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| Who Leads and Who Follows in Strategic Classification? |
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| Why Do Better Loss Functions Lead to Less Transferable Features? |
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4 |
| Why Do Pretrained Language Models Help in Downstream Tasks? An Analysis of Head and Prompt Tuning |
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| Why Generalization in RL is Difficult: Epistemic POMDPs and Implicit Partial Observability |
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| Why Lottery Ticket Wins? A Theoretical Perspective of Sample Complexity on Sparse Neural Networks |
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| Why Spectral Normalization Stabilizes GANs: Analysis and Improvements |
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| Widening the Pipeline in Human-Guided Reinforcement Learning with Explanation and Context-Aware Data Augmentation |
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| Width-based Lookaheads with Learnt Base Policies and Heuristics Over the Atari-2600 Benchmark |
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| Wisdom of the Crowd Voting: Truthful Aggregation of Voter Information and Preferences |
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| Word2Fun: Modelling Words as Functions for Diachronic Word Representation |
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5 |
| XCiT: Cross-Covariance Image Transformers |
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5 |
| XDO: A Double Oracle Algorithm for Extensive-Form Games |
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| You Are the Best Reviewer of Your Own Papers: An Owner-Assisted Scoring Mechanism |
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| You Never Cluster Alone |
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5 |
| You Only Look at One Sequence: Rethinking Transformer in Vision through Object Detection |
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5 |
| You are caught stealing my winning lottery ticket! Making a lottery ticket claim its ownership |
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| Your head is there to move you around: Goal-driven models of the primate dorsal pathway |
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| Zero Time Waste: Recycling Predictions in Early Exit Neural Networks |
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| argmax centroid |
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4 |
| iFlow: Numerically Invertible Flows for Efficient Lossless Compression via a Uniform Coder |
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4 |