| (Nearly) Efficient Algorithms for the Graph Matching Problem on Correlated Random Graphs |
✅ |
❌ |
❌ |
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❌ |
❌ |
1 |
| A Bayesian Theory of Conformity in Collective Decision Making |
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✅ |
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3 |
| A Benchmark for Interpretability Methods in Deep Neural Networks |
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2 |
| A Communication Efficient Stochastic Multi-Block Alternating Direction Method of Multipliers |
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✅ |
✅ |
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4 |
| A Composable Specification Language for Reinforcement Learning Tasks |
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3 |
| A Condition Number for Joint Optimization of Cycle-Consistent Networks |
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✅ |
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2 |
| A Convex Relaxation Barrier to Tight Robustness Verification of Neural Networks |
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4 |
| A Debiased MDI Feature Importance Measure for Random Forests |
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4 |
| A Direct tilde{O}(1/epsilon) Iteration Parallel Algorithm for Optimal Transport |
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3 |
| A Domain Agnostic Measure for Monitoring and Evaluating GANs |
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✅ |
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3 |
| A Family of Robust Stochastic Operators for Reinforcement Learning |
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1 |
| A First-Order Algorithmic Framework for Distributionally Robust Logistic Regression |
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✅ |
✅ |
✅ |
7 |
| A Flexible Generative Framework for Graph-based Semi-supervised Learning |
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✅ |
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3 |
| A Fourier Perspective on Model Robustness in Computer Vision |
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3 |
| A Game Theoretic Approach to Class-wise Selective Rationalization |
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✅ |
3 |
| A General Framework for Symmetric Property Estimation |
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4 |
| A General Theory of Equivariant CNNs on Homogeneous Spaces |
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0 |
| A Generalized Algorithm for Multi-Objective Reinforcement Learning and Policy Adaptation |
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✅ |
❌ |
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❌ |
✅ |
4 |
| A Generic Acceleration Framework for Stochastic Composite Optimization |
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✅ |
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✅ |
4 |
| A Geometric Perspective on Optimal Representations for Reinforcement Learning |
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3 |
| A Graph Theoretic Additive Approximation of Optimal Transport |
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4 |
| A Graph Theoretic Framework of Recomputation Algorithms for Memory-Efficient Backpropagation |
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4 |
| A Kernel Loss for Solving the Bellman Equation |
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1 |
| A Latent Variational Framework for Stochastic Optimization |
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0 |
| A Linearly Convergent Method for Non-Smooth Non-Convex Optimization on the Grassmannian with Applications to Robust Subspace and Dictionary Learning |
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2 |
| A Linearly Convergent Proximal Gradient Algorithm for Decentralized Optimization |
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1 |
| A Little Is Enough: Circumventing Defenses For Distributed Learning |
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2 |
| A Mean Field Theory of Quantized Deep Networks: The Quantization-Depth Trade-Off |
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3 |
| A Meta-Analysis of Overfitting in Machine Learning |
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✅ |
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2 |
| A Meta-MDP Approach to Exploration for Lifelong Reinforcement Learning |
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4 |
| A Model to Search for Synthesizable Molecules |
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4 |
| A Model-Based Reinforcement Learning with Adversarial Training for Online Recommendation |
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4 |
| A Necessary and Sufficient Stability Notion for Adaptive Generalization |
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0 |
| A New Defense Against Adversarial Images: Turning a Weakness into a Strength |
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4 |
| A New Distribution on the Simplex with Auto-Encoding Applications |
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5 |
| A New Perspective on Pool-Based Active Classification and False-Discovery Control |
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1 |
| A Nonconvex Approach for Exact and Efficient Multichannel Sparse Blind Deconvolution |
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✅ |
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1 |
| A Normative Theory for Causal Inference and Bayes Factor Computation in Neural Circuits |
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1 |
| A Polynomial Time Algorithm for Log-Concave Maximum Likelihood via Locally Exponential Families |
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1 |
| A Primal Dual Formulation For Deep Learning With Constraints |
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4 |
| A Primal-Dual link between GANs and Autoencoders |
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0 |
| A Prior of a Googol Gaussians: a Tensor Ring Induced Prior for Generative Models |
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3 |
| A Refined Margin Distribution Analysis for Forest Representation Learning |
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4 |
| A Regularized Approach to Sparse Optimal Policy in Reinforcement Learning |
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3 |
| A Robust Non-Clairvoyant Dynamic Mechanism for Contextual Auctions |
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1 |
| A Self Validation Network for Object-Level Human Attention Estimation |
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3 |
| A Similarity-preserving Network Trained on Transformed Images Recapitulates Salient Features of the Fly Motion Detection Circuit |
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0 |
| A Simple Baseline for Bayesian Uncertainty in Deep Learning |
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❌ |
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5 |
| A Solvable High-Dimensional Model of GAN |
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1 |
| A Step Toward Quantifying Independently Reproducible Machine Learning Research |
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✅ |
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✅ |
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3 |
| A Stochastic Composite Gradient Method with Incremental Variance Reduction |
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3 |
| A Structured Prediction Approach for Generalization in Cooperative Multi-Agent Reinforcement Learning |
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1 |
| A Tensorized Transformer for Language Modeling |
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3 |
| A Unified Bellman Optimality Principle Combining Reward Maximization and Empowerment |
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2 |
| A Unified Framework for Data Poisoning Attack to Graph-based Semi-supervised Learning |
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4 |
| A Unifying Framework for Spectrum-Preserving Graph Sparsification and Coarsening |
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✅ |
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4 |
| A Universally Optimal Multistage Accelerated Stochastic Gradient Method |
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3 |
| A Zero-Positive Learning Approach for Diagnosing Software Performance Regressions |
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3 |
| A coupled autoencoder approach for multi-modal analysis of cell types |
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4 |
| A neurally plausible model for online recognition and postdiction in a dynamical environment |
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3 |
| A neurally plausible model learns successor representations in partially observable environments |
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1 |
| A state-space model for inferring effective connectivity of latent neural dynamics from simultaneous EEG/fMRI |
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2 |
| A unified theory for the origin of grid cells through the lens of pattern formation |
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1 |
| A unified variance-reduced accelerated gradient method for convex optimization |
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3 |
| ADDIS: an adaptive discarding algorithm for online FDR control with conservative nulls |
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3 |
| AGEM: Solving Linear Inverse Problems via Deep Priors and Sampling |
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5 |
| ANODEV2: A Coupled Neural ODE Framework |
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✅ |
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2 |
| Abstract Reasoning with Distracting Features |
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❌ |
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2 |
| Abstraction based Output Range Analysis for Neural Networks |
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✅ |
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2 |
| Accelerating Rescaled Gradient Descent: Fast Optimization of Smooth Functions |
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2 |
| Acceleration via Symplectic Discretization of High-Resolution Differential Equations |
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❌ |
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0 |
| Accurate Layerwise Interpretable Competence Estimation |
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✅ |
✅ |
❌ |
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3 |
| Accurate Uncertainty Estimation and Decomposition in Ensemble Learning |
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0 |
| Accurate, reliable and fast robustness evaluation |
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4 |
| Adapting Neural Networks for the Estimation of Treatment Effects |
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4 |
| Adaptive Auxiliary Task Weighting for Reinforcement Learning |
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3 |
| Adaptive Cross-Modal Few-shot Learning |
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4 |
| Adaptive Density Estimation for Generative Models |
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3 |
| Adaptive GNN for Image Analysis and Editing |
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1 |
| Adaptive Gradient-Based Meta-Learning Methods |
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3 |
| Adaptive Influence Maximization with Myopic Feedback |
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1 |
| Adaptive Sequence Submodularity |
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4 |
| Adaptive Temporal-Difference Learning for Policy Evaluation with Per-State Uncertainty Estimates |
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3 |
| Adaptively Aligned Image Captioning via Adaptive Attention Time |
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4 |
| Addressing Failure Prediction by Learning Model Confidence |
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4 |
| Addressing Sample Complexity in Visual Tasks Using HER and Hallucinatory GANs |
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1 |
| Adversarial Examples Are Not Bugs, They Are Features |
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2 |
| Adversarial Fisher Vectors for Unsupervised Representation Learning |
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3 |
| Adversarial Music: Real world Audio Adversary against Wake-word Detection System |
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4 |
| Adversarial Robustness through Local Linearization |
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4 |
| Adversarial Self-Defense for Cycle-Consistent GANs |
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✅ |
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1 |
| Adversarial Training and Robustness for Multiple Perturbations |
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4 |
| Adversarial training for free! |
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✅ |
✅ |
❌ |
✅ |
6 |
| Algorithm-Dependent Generalization Bounds for Overparameterized Deep Residual Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Algorithmic Analysis and Statistical Estimation of SLOPE via Approximate Message Passing |
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❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Algorithmic Guarantees for Inverse Imaging with Untrained Network Priors |
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❌ |
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3 |
| Aligning Visual Regions and Textual Concepts for Semantic-Grounded Image Representations |
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✅ |
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4 |
| Alleviating Label Switching with Optimal Transport |
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2 |
| Almost Horizon-Free Structure-Aware Best Policy Identification with a Generative Model |
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1 |
| Amortized Bethe Free Energy Minimization for Learning MRFs |
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✅ |
✅ |
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❌ |
✅ |
6 |
| An Inexact Augmented Lagrangian Framework for Nonconvex Optimization with Nonlinear Constraints |
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❌ |
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❌ |
✅ |
3 |
| An Accelerated Decentralized Stochastic Proximal Algorithm for Finite Sums |
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✅ |
✅ |
❌ |
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❌ |
✅ |
4 |
| An Adaptive Empirical Bayesian Method for Sparse Deep Learning |
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3 |
| An Algorithm to Learn Polytree Networks with Hidden Nodes |
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1 |
| An Algorithmic Framework For Differentially Private Data Analysis on Trusted Processors |
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1 |
| An Embedding Framework for Consistent Polyhedral Surrogates |
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0 |
| An Improved Analysis of Training Over-parameterized Deep Neural Networks |
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1 |
| An adaptive Mirror-Prox method for variational inequalities with singular operators |
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✅ |
2 |
| An adaptive nearest neighbor rule for classification |
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3 |
| Anti-efficient encoding in emergent communication |
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2 |
| Approximate Bayesian Inference for a Mechanistic Model of Vesicle Release at a Ribbon Synapse |
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✅ |
❌ |
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❌ |
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2 |
| Approximate Feature Collisions in Neural Nets |
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❌ |
✅ |
❌ |
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1 |
| Approximate Inference Turns Deep Networks into Gaussian Processes |
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3 |
| Approximating Interactive Human Evaluation with Self-Play for Open-Domain Dialog Systems |
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3 |
| Approximating the Permanent by Sampling from Adaptive Partitions |
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2 |
| Approximation Ratios of Graph Neural Networks for Combinatorial Problems |
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1 |
| Arbicon-Net: Arbitrary Continuous Geometric Transformation Networks for Image Registration |
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4 |
| Are Anchor Points Really Indispensable in Label-Noise Learning? |
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5 |
| Are Disentangled Representations Helpful for Abstract Visual Reasoning? |
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5 |
| Are Labels Required for Improving Adversarial Robustness? |
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5 |
| Are Sixteen Heads Really Better than One? |
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4 |
| Are deep ResNets provably better than linear predictors? |
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1 |
| Are sample means in multi-armed bandits positively or negatively biased? |
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1 |
| Ask not what AI can do, but what AI should do: Towards a framework of task delegability |
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1 |
| Assessing Disparate Impact of Personalized Interventions: Identifiability and Bounds |
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2 |
| Assessing Social and Intersectional Biases in Contextualized Word Representations |
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2 |
| Asymmetric Valleys: Beyond Sharp and Flat Local Minima |
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❌ |
2 |
| Asymptotic Guarantees for Learning Generative Models with the Sliced-Wasserstein Distance |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Asymptotics for Sketching in Least Squares Regression |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| AttentionXML: Label Tree-based Attention-Aware Deep Model for High-Performance Extreme Multi-Label Text Classification |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Attentive State-Space Modeling of Disease Progression |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Attribution-Based Confidence Metric For Deep Neural Networks |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Augmented Neural ODEs |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| AutoAssist: A Framework to Accelerate Training of Deep Neural Networks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| AutoPrune: Automatic Network Pruning by Regularizing Auxiliary Parameters |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Average Case Column Subset Selection for Entrywise $\ell_1$-Norm Loss |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Average Individual Fairness: Algorithms, Generalization and Experiments |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Average-Case Averages: Private Algorithms for Smooth Sensitivity and Mean Estimation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| BIVA: A Very Deep Hierarchy of Latent Variables for Generative Modeling |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Backprop with Approximate Activations for Memory-efficient Network Training |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Backpropagation-Friendly Eigendecomposition |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Balancing Efficiency and Fairness in On-Demand Ridesourcing |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Band-Limited Gaussian Processes: The Sinc Kernel |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Bandits with Feedback Graphs and Switching Costs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Bat-G net: Bat-inspired High-Resolution 3D Image Reconstruction using Ultrasonic Echoes |
❌ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
3 |
| BatchBALD: Efficient and Diverse Batch Acquisition for Deep Bayesian Active Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Batched Multi-armed Bandits Problem |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Bayesian Batch Active Learning as Sparse Subset Approximation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Bayesian Joint Estimation of Multiple Graphical Models |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Bayesian Layers: A Module for Neural Network Uncertainty |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
4 |
| Bayesian Learning of Sum-Product Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Bayesian Optimization under Heavy-tailed Payoffs |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Bayesian Optimization with Unknown Search Space |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Beating SGD Saturation with Tail-Averaging and Minibatching |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| BehaveNet: nonlinear embedding and Bayesian neural decoding of behavioral videos |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Better Exploration with Optimistic Actor Critic |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Better Transfer Learning with Inferred Successor Maps |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Beyond Alternating Updates for Matrix Factorization with Inertial Bregman Proximal Gradient Algorithms |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Beyond Confidence Regions: Tight Bayesian Ambiguity Sets for Robust MDPs |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Beyond Online Balanced Descent: An Optimal Algorithm for Smoothed Online Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Beyond Vector Spaces: Compact Data Representation as Differentiable Weighted Graphs |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Beyond temperature scaling: Obtaining well-calibrated multi-class probabilities with Dirichlet calibration |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Beyond the Single Neuron Convex Barrier for Neural Network Certification |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Bias Correction of Learned Generative Models using Likelihood-Free Importance Weighting |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Biases for Emergent Communication in Multi-agent Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Bipartite expander Hopfield networks as self-decoding high-capacity error correcting codes |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
3 |
| Blended Matching Pursuit |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Blind Super-Resolution Kernel Estimation using an Internal-GAN |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Block Coordinate Regularization by Denoising |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Blocking Bandits |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Blow: a single-scale hyperconditioned flow for non-parallel raw-audio voice conversion |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Bootstrapping Upper Confidence Bound |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Brain-Like Object Recognition with High-Performing Shallow Recurrent ANNs |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Break the Ceiling: Stronger Multi-scale Deep Graph Convolutional Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Breaking the Glass Ceiling for Embedding-Based Classifiers for Large Output Spaces |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Bridging Machine Learning and Logical Reasoning by Abductive Learning |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Budgeted Reinforcement Learning in Continuous State Space |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| CNN^{2}: Viewpoint Generalization via a Binocular Vision |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
3 |
| CPM-Nets: Cross Partial Multi-View Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| CXPlain: Causal Explanations for Model Interpretation under Uncertainty |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| Calculating Optimistic Likelihoods Using (Geodesically) Convex Optimization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Calibration tests in multi-class classification: A unifying framework |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
3 |
| Can SGD Learn Recurrent Neural Networks with Provable Generalization? |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Can Unconditional Language Models Recover Arbitrary Sentences? |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Can you trust your model's uncertainty? Evaluating predictive uncertainty under dataset shift |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Capacity Bounded Differential Privacy |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Cascade RPN: Delving into High-Quality Region Proposal Network with Adaptive Convolution |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Cascaded Dilated Dense Network with Two-step Data Consistency for MRI Reconstruction |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Categorized Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Category Anchor-Guided Unsupervised Domain Adaptation for Semantic Segmentation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Causal Confusion in Imitation Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Causal Regularization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Censored Semi-Bandits: A Framework for Resource Allocation with Censored Feedback |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Certainty Equivalence is Efficient for Linear Quadratic Control |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Certifiable Robustness to Graph Perturbations |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Certified Adversarial Robustness with Additive Noise |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Certifying Geometric Robustness of Neural Networks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Channel Gating Neural Networks |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Characterization and Learning of Causal Graphs with Latent Variables from Soft Interventions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Characterizing Bias in Classifiers using Generative Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Characterizing the Exact Behaviors of Temporal Difference Learning Algorithms Using Markov Jump Linear System Theory |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Chasing Ghosts: Instruction Following as Bayesian State Tracking |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Chirality Nets for Human Pose Regression |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Classification Accuracy Score for Conditional Generative Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Classification-by-Components: Probabilistic Modeling of Reasoning over a Set of Components |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Co-Generation with GANs using AIS based HMC |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Coda: An End-to-End Neural Program Decompiler |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Code Generation as a Dual Task of Code Summarization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Cold Case: The Lost MNIST Digits |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Combinatorial Bandits with Relative Feedback |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Combinatorial Bayesian Optimization using the Graph Cartesian Product |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Combinatorial Inference against Label Noise |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Combining Generative and Discriminative Models for Hybrid Inference |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Communication trade-offs for Local-SGD with large step size |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Communication-Efficient Distributed Blockwise Momentum SGD with Error-Feedback |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Communication-Efficient Distributed Learning via Lazily Aggregated Quantized Gradients |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Communication-efficient Distributed SGD with Sketching |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Compacting, Picking and Growing for Unforgetting Continual Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Comparing Unsupervised Word Translation Methods Step by Step |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Comparing distributions: $\ell_1$ geometry improves kernel two-sample testing |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Comparison Against Task Driven Artificial Neural Networks Reveals Functional Properties in Mouse Visual Cortex |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Competitive Gradient Descent |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Compiler Auto-Vectorization with Imitation Learning |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
4 |
| Complexity of Highly Parallel Non-Smooth Convex Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Compositional De-Attention Networks |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Compositional Plan Vectors |
❌ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
3 |
| Compositional generalization through meta sequence-to-sequence learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Compression with Flows via Local Bits-Back Coding |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Computational Mirrors: Blind Inverse Light Transport by Deep Matrix Factorization |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Computational Separations between Sampling and Optimization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Computing Full Conformal Prediction Set with Approximate Homotopy |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Computing Linear Restrictions of Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Concentration of risk measures: A Wasserstein distance approach |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| CondConv: Conditionally Parameterized Convolutions for Efficient Inference |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Conditional Independence Testing using Generative Adversarial Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Conditional Structure Generation through Graph Variational Generative Adversarial Nets |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Conformal Prediction Under Covariate Shift |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Conformalized Quantile Regression |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Connections Between Mirror Descent, Thompson Sampling and the Information Ratio |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Connective Cognition Network for Directional Visual Commonsense Reasoning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Consistency-based Semi-supervised Learning for Object detection |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Constrained Reinforcement Learning Has Zero Duality Gap |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Constrained deep neural network architecture search for IoT devices accounting for hardware calibration |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Constraint-based Causal Structure Learning with Consistent Separating Sets |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Contextual Bandits with Cross-Learning |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Continual Unsupervised Representation Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Continuous Hierarchical Representations with Poincaré Variational Auto-Encoders |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Continuous-time Models for Stochastic Optimization Algorithms |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Control Batch Size and Learning Rate to Generalize Well: Theoretical and Empirical Evidence |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Control What You Can: Intrinsically Motivated Task-Planning Agent |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Controllable Text-to-Image Generation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Controllable Unsupervised Text Attribute Transfer via Editing Entangled Latent Representation |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Controlling Neural Level Sets |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Convergence Guarantees for Adaptive Bayesian Quadrature Methods |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Convergence of Adversarial Training in Overparametrized Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Convergence-Rate-Matching Discretization of Accelerated Optimization Flows Through Opportunistic State-Triggered Control |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Convergent Policy Optimization for Safe Reinforcement Learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Convolution with even-sized kernels and symmetric padding |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Coordinated hippocampal-entorhinal replay as structural inference |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Copula Multi-label Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Copula-like Variational Inference |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Copulas as High-Dimensional Generative Models: Vine Copula Autoencoders |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Coresets for Archetypal Analysis |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Coresets for Clustering with Fairness Constraints |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Cormorant: Covariant Molecular Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Correlated Uncertainty for Learning Dense Correspondences from Noisy Labels |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Correlation Clustering with Adaptive Similarity Queries |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Correlation Priors for Reinforcement Learning |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Correlation clustering with local objectives |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Correlation in Extensive-Form Games: Saddle-Point Formulation and Benchmarks |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
3 |
| Cost Effective Active Search |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Counting the Optimal Solutions in Graphical Models |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Covariate-Powered Empirical Bayes Estimation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Cross Attention Network for Few-shot Classification |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Cross-Domain Transferability of Adversarial Perturbations |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Cross-Modal Learning with Adversarial Samples |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Cross-channel Communication Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Cross-lingual Language Model Pretraining |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Cross-sectional Learning of Extremal Dependence among Financial Assets |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Crowdsourcing via Pairwise Co-occurrences: Identifiability and Algorithms |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Curriculum-guided Hindsight Experience Replay |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Curvilinear Distance Metric Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| D-VAE: A Variational Autoencoder for Directed Acyclic Graphs |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| DAC: The Double Actor-Critic Architecture for Learning Options |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| DATA: Differentiable ArchiTecture Approximation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| DETOX: A Redundancy-based Framework for Faster and More Robust Gradient Aggregation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| DFNets: Spectral CNNs for Graphs with Feedback-Looped Filters |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| DINGO: Distributed Newton-Type Method for Gradient-Norm Optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| DISN: Deep Implicit Surface Network for High-quality Single-view 3D Reconstruction |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| DM2C: Deep Mixed-Modal Clustering |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| DRUM: End-To-End Differentiable Rule Mining On Knowledge Graphs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| DTWNet: a Dynamic Time Warping Network |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Dancing to Music |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Data Cleansing for Models Trained with SGD |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Data Parameters: A New Family of Parameters for Learning a Differentiable Curriculum |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Data-Dependence of Plateau Phenomenon in Learning with Neural Network --- Statistical Mechanical Analysis |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Data-dependent Sample Complexity of Deep Neural Networks via Lipschitz Augmentation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Data-driven Estimation of Sinusoid Frequencies |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Debiased Bayesian inference for average treatment effects |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Decentralized Cooperative Stochastic Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Decentralized sketching of low rank matrices |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Deconstructing Lottery Tickets: Zeros, Signs, and the Supermask |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Deep Active Learning with a Neural Architecture Search |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Deep Equilibrium Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Deep Gamblers: Learning to Abstain with Portfolio Theory |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Deep Generalized Method of Moments for Instrumental Variable Analysis |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Deep Generative Video Compression |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Deep Leakage from Gradients |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Deep Learning without Weight Transport |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Deep Model Transferability from Attribution Maps |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Deep Multi-State Dynamic Recurrent Neural Networks Operating on Wavelet Based Neural Features for Robust Brain Machine Interfaces |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
3 |
| Deep Multimodal Multilinear Fusion with High-order Polynomial Pooling |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Deep RGB-D Canonical Correlation Analysis For Sparse Depth Completion |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Deep Random Splines for Point Process Intensity Estimation of Neural Population Data |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Deep ReLU Networks Have Surprisingly Few Activation Patterns |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Deep Scale-spaces: Equivariance Over Scale |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Deep Set Prediction Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Deep Signature Transforms |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Deep Structured Prediction for Facial Landmark Detection |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Deep Supervised Summarization: Algorithm and Application to Learning Instructions |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Deep imitation learning for molecular inverse problems |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| DeepUSPS: Deep Robust Unsupervised Saliency Prediction via Self-supervision |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| DeepWave: A Recurrent Neural-Network for Real-Time Acoustic Imaging |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Defending Against Neural Fake News |
❌ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Defending Neural Backdoors via Generative Distribution Modeling |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Defense Against Adversarial Attacks Using Feature Scattering-based Adversarial Training |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Deliberative Explanations: visualizing network insecurities |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Demystifying Black-box Models with Symbolic Metamodels |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Depth-First Proof-Number Search with Heuristic Edge Cost and Application to Chemical Synthesis Planning |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
4 |
| DetNAS: Backbone Search for Object Detection |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Detecting Overfitting via Adversarial Examples |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Devign: Effective Vulnerability Identification by Learning Comprehensive Program Semantics via Graph Neural Networks |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Dichotomize and Generalize: PAC-Bayesian Binary Activated Deep Neural Networks |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Diffeomorphic Temporal Alignment Nets |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Differentiable Cloth Simulation for Inverse Problems |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Differentiable Convex Optimization Layers |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Differentiable Ranking and Sorting using Optimal Transport |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Differential Privacy Has Disparate Impact on Model Accuracy |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Differentially Private Algorithms for Learning Mixtures of Separated Gaussians |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Differentially Private Anonymized Histograms |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Differentially Private Bagging: Improved utility and cheaper privacy than subsample-and-aggregate |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Differentially Private Bayesian Linear Regression |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Differentially Private Covariance Estimation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Differentially Private Distributed Data Summarization under Covariate Shift |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Differentially Private Markov Chain Monte Carlo |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Diffusion Improves Graph Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Dimension-Free Bounds for Low-Precision Training |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Dimensionality reduction: theoretical perspective on practical measures |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Direct Estimation of Differential Functional Graphical Models |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Direct Optimization through $\arg \max$ for Discrete Variational Auto-Encoder |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Discovering Neural Wirings |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Discovery of Useful Questions as Auxiliary Tasks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Discrete Flows: Invertible Generative Models of Discrete Data |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Discrete Object Generation with Reversible Inductive Construction |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Discrimination in Online Markets: Effects of Social Bias on Learning from Reviews and Policy Design |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Discriminative Topic Modeling with Logistic LDA |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Discriminator optimal transport |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Disentangled behavioural representations |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Disentangling Influence: Using disentangled representations to audit model predictions |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| DiskANN: Fast Accurate Billion-point Nearest Neighbor Search on a Single Node |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Distinguishing Distributions When Samples Are Strategically Transformed |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Distributed Low-rank Matrix Factorization With Exact Consensus |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Distributed estimation of the inverse Hessian by determinantal averaging |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Distribution Learning of a Random Spatial Field with a Location-Unaware Mobile Sensor |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Distribution oblivious, risk-aware algorithms for multi-armed bandits with unbounded rewards |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Distribution-Independent PAC Learning of Halfspaces with Massart Noise |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Distributional Policy Optimization: An Alternative Approach for Continuous Control |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Distributional Reward Decomposition for Reinforcement Learning |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Distributionally Robust Optimization and Generalization in Kernel Methods |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Divergence-Augmented Policy Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Divide and Couple: Using Monte Carlo Variational Objectives for Posterior Approximation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Domain Generalization via Model-Agnostic Learning of Semantic Features |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Domes to Drones: Self-Supervised Active Triangulation for 3D Human Pose Reconstruction |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Don't Blame the ELBO! A Linear VAE Perspective on Posterior Collapse |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Don't take it lightly: Phasing optical random projections with unknown operators |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Double Quantization for Communication-Efficient Distributed Optimization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Doubly-Robust Lasso Bandit |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| DppNet: Approximating Determinantal Point Processes with Deep Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Drill-down: Interactive Retrieval of Complex Scenes using Natural Language Queries |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Dual Adversarial Semantics-Consistent Network for Generalized Zero-Shot Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Dual Variational Generation for Low Shot Heterogeneous Face Recognition |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| DualDICE: Behavior-Agnostic Estimation of Discounted Stationary Distribution Corrections |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Dying Experts: Efficient Algorithms with Optimal Regret Bounds |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Dynamic Ensemble Modeling Approach to Nonstationary Neural Decoding in Brain-Computer Interfaces |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Dynamic Incentive-Aware Learning: Robust Pricing in Contextual Auctions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Dynamic Local Regret for Non-convex Online Forecasting |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Dynamics of stochastic gradient descent for two-layer neural networks in the teacher-student setup |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| E2-Train: Training State-of-the-art CNNs with Over 80% Energy Savings |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| ETNet: Error Transition Network for Arbitrary Style Transfer |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Ease-of-Teaching and Language Structure from Emergent Communication |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Effective End-to-end Unsupervised Outlier Detection via Inlier Priority of Discriminative Network |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient Algorithms for Smooth Minimax Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
3 |
| Efficient Approximation of Deep ReLU Networks for Functions on Low Dimensional Manifolds |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Efficient Communication in Multi-Agent Reinforcement Learning via Variance Based Control |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Efficient Convex Relaxations for Streaming PCA |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient Deep Approximation of GMMs |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Efficient Forward Architecture Search |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Efficient Graph Generation with Graph Recurrent Attention Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Efficient Identification in Linear Structural Causal Models with Instrumental Cutsets |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Efficient Meta Learning via Minibatch Proximal Update |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Efficient Near-Optimal Testing of Community Changes in Balanced Stochastic Block Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient Neural Architecture Transformation Search in Channel-Level for Object Detection |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Efficient Probabilistic Inference in the Quest for Physics Beyond the Standard Model |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Efficient Pure Exploration in Adaptive Round model |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Efficient Regret Minimization Algorithm for Extensive-Form Correlated Equilibrium |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Efficient Rematerialization for Deep Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient Smooth Non-Convex Stochastic Compositional Optimization via Stochastic Recursive Gradient Descent |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Efficient Symmetric Norm Regression via Linear Sketching |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Efficient and Accurate Estimation of Lipschitz Constants for Deep Neural Networks |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Efficient and Thrifty Voting by Any Means Necessary |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Efficient characterization of electrically evoked responses for neural interfaces |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Efficient online learning with kernels for adversarial large scale problems |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Efficiently Estimating Erdos-Renyi Graphs with Node Differential Privacy |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Efficiently Learning Fourier Sparse Set Functions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Efficiently avoiding saddle points with zero order methods: No gradients required |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Efficiently escaping saddle points on manifolds |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Elliptical Perturbations for Differential Privacy |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Embedding Symbolic Knowledge into Deep Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Emergence of Object Segmentation in Perturbed Generative Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Empirically Measuring Concentration: Fundamental Limits on Intrinsic Robustness |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Enabling hyperparameter optimization in sequential autoencoders for spiking neural data |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| End to end learning and optimization on graphs |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| End-to-End Learning on 3D Protein Structure for Interface Prediction |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Energy-Inspired Models: Learning with Sampler-Induced Distributions |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Enhancing the Locality and Breaking the Memory Bottleneck of Transformer on Time Series Forecasting |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Envy-Free Classification |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Episodic Memory in Lifelong Language Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Epsilon-Best-Arm Identification in Pay-Per-Reward Multi-Armed Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Equal Opportunity in Online Classification with Partial Feedback |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Equipping Experts/Bandits with Long-term Memory |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Equitable Stable Matchings in Quadratic Time |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Error Correcting Output Codes Improve Probability Estimation and Adversarial Robustness of Deep Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Escaping from saddle points on Riemannian manifolds |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Estimating Convergence of Markov chains with L-Lag Couplings |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| Estimating Entropy of Distributions in Constant Space |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Evaluating Protein Transfer Learning with TAPE |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Exact Combinatorial Optimization with Graph Convolutional Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
4 |
| Exact Gaussian Processes on a Million Data Points |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Exact Rate-Distortion in Autoencoders via Echo Noise |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Exact inference in structured prediction |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Exact sampling of determinantal point processes with sublinear time preprocessing |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Experience Replay for Continual Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Explaining Landscape Connectivity of Low-cost Solutions for Multilayer Nets |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Explanations can be manipulated and geometry is to blame |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Explicit Disentanglement of Appearance and Perspective in Generative Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Explicit Explore-Exploit Algorithms in Continuous State Spaces |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Explicit Planning for Efficient Exploration in Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Explicitly disentangling image content from translation and rotation with spatial-VAE |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Exploiting Local and Global Structure for Point Cloud Semantic Segmentation with Contextual Point Representations |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Exploration Bonus for Regret Minimization in Discrete and Continuous Average Reward MDPs |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Exploration via Hindsight Goal Generation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Exploring Algorithmic Fairness in Robust Graph Covering Problems |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
3 |
| Exploring Unexplored Tensor Network Decompositions for Convolutional Neural Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Exponential Family Estimation via Adversarial Dynamics Embedding |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Exponentially convergent stochastic k-PCA without variance reduction |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Expressive power of tensor-network factorizations for probabilistic modeling |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Extending Stein's unbiased risk estimator to train deep denoisers with correlated pairs of noisy images |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Extreme Classification in Log Memory using Count-Min Sketch: A Case Study of Amazon Search with 50M Products |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Face Reconstruction from Voice using Generative Adversarial Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Facility Location Problem in Differential Privacy Model Revisited |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Factor Group-Sparse Regularization for Efficient Low-Rank Matrix Recovery |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Failing Loudly: An Empirical Study of Methods for Detecting Dataset Shift |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Fair Algorithms for Clustering |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Fast AutoAugment |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Fast Convergence of Belief Propagation to Global Optima: Beyond Correlation Decay |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Fast Convergence of Natural Gradient Descent for Over-Parameterized Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Fast Decomposable Submodular Function Minimization using Constrained Total Variation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Fast Efficient Hyperparameter Tuning for Policy Gradient Methods |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Fast Low-rank Metric Learning for Large-scale and High-dimensional Data |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Fast Parallel Algorithms for Statistical Subset Selection Problems |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Fast Sparse Group Lasso |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Fast Structured Decoding for Sequence Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Fast and Accurate Least-Mean-Squares Solvers |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Fast and Accurate Stochastic Gradient Estimation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fast and Flexible Multi-Task Classification using Conditional Neural Adaptive Processes |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Fast and Furious Learning in Zero-Sum Games: Vanishing Regret with Non-Vanishing Step Sizes |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Fast and Provable ADMM for Learning with Generative Priors |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fast structure learning with modular regularization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Fast, Provably convergent IRLS Algorithm for p-norm Linear Regression |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Fast-rate PAC-Bayes Generalization Bounds via Shifted Rademacher Processes |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| FastSpeech: Fast, Robust and Controllable Text to Speech |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Faster Boosting with Smaller Memory |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Faster width-dependent algorithm for mixed packing and covering LPs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Few-shot Video-to-Video Synthesis |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Finding Friend and Foe in Multi-Agent Games |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Finding the Needle in the Haystack with Convolutions: on the benefits of architectural bias |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fine-grained Optimization of Deep Neural Networks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Finite-Sample Analysis for SARSA with Linear Function Approximation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Finite-Time Performance Bounds and Adaptive Learning Rate Selection for Two Time-Scale Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Finite-time Analysis of Approximate Policy Iteration for the Linear Quadratic Regulator |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient Noise |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| First Order Motion Model for Image Animation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| First order expansion of convex regularized estimators |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| First-order methods almost always avoid saddle points: The case of vanishing step-sizes |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Fisher Efficient Inference of Intractable Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fixing Implicit Derivatives: Trust-Region Based Learning of Continuous Energy Functions |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Fixing the train-test resolution discrepancy |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Flattening a Hierarchical Clustering through Active Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Flexible Modeling of Diversity with Strongly Log-Concave Distributions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Flexible information routing in neural populations through stochastic comodulation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Flow-based Image-to-Image Translation with Feature Disentanglement |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Focused Quantization for Sparse CNNs |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Fooling Neural Network Interpretations via Adversarial Model Manipulation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Foundations of Comparison-Based Hierarchical Clustering |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| FreeAnchor: Learning to Match Anchors for Visual Object Detection |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| From Complexity to Simplicity: Adaptive ES-Active Subspaces for Blackbox Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| From deep learning to mechanistic understanding in neuroscience: the structure of retinal prediction |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| From voxels to pixels and back: Self-supervision in natural-image reconstruction from fMRI |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Full-Gradient Representation for Neural Network Visualization |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Fully Dynamic Consistent Facility Location |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Fully Neural Network based Model for General Temporal Point Processes |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Fully Parameterized Quantile Function for Distributional Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Function-Space Distributions over Kernels |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Functional Adversarial Attacks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| G2SAT: Learning to Generate SAT Formulas |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| GENO -- GENeric Optimization for Classical Machine Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| GIFT: Learning Transformation-Invariant Dense Visual Descriptors via Group CNNs |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| GNNExplainer: Generating Explanations for Graph Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| GOT: An Optimal Transport framework for Graph comparison |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| GPipe: Efficient Training of Giant Neural Networks using Pipeline Parallelism |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| GRU-ODE-Bayes: Continuous Modeling of Sporadically-Observed Time Series |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Game Design for Eliciting Distinguishable Behavior |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Gate Decorator: Global Filter Pruning Method for Accelerating Deep Convolutional Neural Networks |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Gaussian-Based Pooling for Convolutional Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| General E(2)-Equivariant Steerable CNNs |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| General Proximal Incremental Aggregated Gradient Algorithms: Better and Novel Results under General Scheme |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Generalization Bounds for Neural Networks via Approximate Description Length |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Generalization Bounds in the Predict-then-Optimize Framework |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Generalization Bounds of Stochastic Gradient Descent for Wide and Deep Neural Networks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Generalization Error Analysis of Quantized Compressive Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Generalization in Generative Adversarial Networks: A Novel Perspective from Privacy Protection |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Generalization in Reinforcement Learning with Selective Noise Injection and Information Bottleneck |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Generalization in multitask deep neural classifiers: a statistical physics approach |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Generalization of Reinforcement Learners with Working and Episodic Memory |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Generalized Block-Diagonal Structure Pursuit: Learning Soft Latent Task Assignment against Negative Transfer |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
2 |
| Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Generalized Off-Policy Actor-Critic |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Generalized Sliced Wasserstein Distances |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Generating Diverse High-Fidelity Images with VQ-VAE-2 |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Generative Modeling by Estimating Gradients of the Data Distribution |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Generative Models for Graph-Based Protein Design |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Generative Well-intentioned Networks |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Geometry-Aware Neural Rendering |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Global Convergence of Gradient Descent for Deep Linear Residual Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Global Convergence of Least Squares EM for Demixing Two Log-Concave Densities |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Global Guarantees for Blind Demodulation with Generative Priors |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Global Sparse Momentum SGD for Pruning Very Deep Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Globally Convergent Newton Methods for Ill-conditioned Generalized Self-concordant Losses |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Globally Optimal Learning for Structured Elliptical Losses |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Globally optimal score-based learning of directed acyclic graphs in high-dimensions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Glyce: Glyph-vectors for Chinese Character Representations |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Goal-conditioned Imitation Learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Gossip-based Actor-Learner Architectures for Deep Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Gradient Dynamics of Shallow Univariate ReLU Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Gradient Information for Representation and Modeling |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Gradient based sample selection for online continual learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Gradient-based Adaptive Markov Chain Monte Carlo |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Graph Agreement Models for Semi-Supervised Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Graph Neural Tangent Kernel: Fusing Graph Neural Networks with Graph Kernels |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Graph Normalizing Flows |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Graph Structured Prediction Energy Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Graph Transformer Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Graph-Based Semi-Supervised Learning with Non-ignorable Non-response |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Graph-based Discriminators: Sample Complexity and Expressiveness |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Greedy Sampling for Approximate Clustering in the Presence of Outliers |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Grid Saliency for Context Explanations of Semantic Segmentation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Group Retention when Using Machine Learning in Sequential Decision Making: the Interplay between User Dynamics and Fairness |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Guided Meta-Policy Search |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Guided Similarity Separation for Image Retrieval |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| HYPE: A Benchmark for Human eYe Perceptual Evaluation of Generative Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Hamiltonian Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Hamiltonian descent for composite objectives |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Handling correlated and repeated measurements with the smoothed multivariate square-root Lasso |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Heterogeneous Graph Learning for Visual Commonsense Reasoning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Hierarchical Decision Making by Generating and Following Natural Language Instructions |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Hierarchical Optimal Transport for Document Representation |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Hierarchical Optimal Transport for Multimodal Distribution Alignment |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Hierarchical Reinforcement Learning with Advantage-Based Auxiliary Rewards |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| High Fidelity Video Prediction with Large Stochastic Recurrent Neural Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| High-Dimensional Optimization in Adaptive Random Subspaces |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| High-Quality Self-Supervised Deep Image Denoising |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| High-dimensional multivariate forecasting with low-rank Gaussian Copula Processes |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Hindsight Credit Assignment |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| How degenerate is the parametrization of neural networks with the ReLU activation function? |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| How to Initialize your Network? Robust Initialization for WeightNorm & ResNets |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Hybrid 8-bit Floating Point (HFP8) Training and Inference for Deep Neural Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Hyper-Graph-Network Decoders for Block Codes |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| HyperGCN: A New Method For Training Graph Convolutional Networks on Hypergraphs |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Hyperbolic Graph Convolutional Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Hyperbolic Graph Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Hyperparameter Learning via Distributional Transfer |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Hyperspherical Prototype Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Hypothesis Set Stability and Generalization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Icebreaker: Element-wise Efficient Information Acquisition with a Bayesian Deep Latent Gaussian Model |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Identification of Conditional Causal Effects under Markov Equivalence |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Identifying Causal Effects via Context-specific Independence Relations |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Image Captioning: Transforming Objects into Words |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Image Synthesis with a Single (Robust) Classifier |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Imitation Learning from Observations by Minimizing Inverse Dynamics Disagreement |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Imitation-Projected Programmatic Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Implicit Generation and Modeling with Energy Based Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Implicit Posterior Variational Inference for Deep Gaussian Processes |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Implicit Regularization for Optimal Sparse Recovery |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Implicit Regularization in Deep Matrix Factorization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Implicit Regularization of Accelerated Methods in Hilbert Spaces |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Implicit Regularization of Discrete Gradient Dynamics in Linear Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Implicit Semantic Data Augmentation for Deep Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Implicitly learning to reason in first-order logic |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Importance Resampling for Off-policy Prediction |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Importance Weighted Hierarchical Variational Inference |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Improved Precision and Recall Metric for Assessing Generative Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Improved Regret Bounds for Bandit Combinatorial Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Improving Black-box Adversarial Attacks with a Transfer-based Prior |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Improving Textual Network Learning with Variational Homophilic Embeddings |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| In-Place Zero-Space Memory Protection for CNN |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
4 |
| Incremental Few-Shot Learning with Attention Attractor Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Incremental Scene Synthesis |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
3 |
| Individual Regret in Cooperative Nonstochastic Multi-Armed Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Inducing brain-relevant bias in natural language processing models |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Information Competing Process for Learning Diversified Representations |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Information-Theoretic Confidence Bounds for Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Information-Theoretic Generalization Bounds for SGLD via Data-Dependent Estimates |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Infra-slow brain dynamics as a marker for cognitive function and decline |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Inherent Tradeoffs in Learning Fair Representations |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Inherent Weight Normalization in Stochastic Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Initialization of ReLUs for Dynamical Isometry |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Input Similarity from the Neural Network Perspective |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Input-Cell Attention Reduces Vanishing Saliency of Recurrent Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Input-Output Equivalence of Unitary and Contractive RNNs |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Integer Discrete Flows and Lossless Compression |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Integrating Bayesian and Discriminative Sparse Kernel Machines for Multi-class Active Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Integrating Markov processes with structural causal modeling enables counterfactual inference in complex systems |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Interaction Hard Thresholding: Consistent Sparse Quadratic Regression in Sub-quadratic Time and Space |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Interior-Point Methods Strike Back: Solving the Wasserstein Barycenter Problem |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Interlaced Greedy Algorithm for Maximization of Submodular Functions in Nearly Linear Time |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Interpreting and improving natural-language processing (in machines) with natural language-processing (in the brain) |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Interval timing in deep reinforcement learning agents |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Intrinsic dimension of data representations in deep neural networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Intrinsically Efficient, Stable, and Bounded Off-Policy Evaluation for Reinforcement Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Invariance and identifiability issues for word embeddings |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Invert to Learn to Invert |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Invertible Convolutional Flow |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Inverting Deep Generative models, One layer at a time |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Is Deeper Better only when Shallow is Good? |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Iterative Least Trimmed Squares for Mixed Linear Regression |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Joint Optimization of Tree-based Index and Deep Model for Recommender Systems |
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4 |
| Joint-task Self-supervised Learning for Temporal Correspondence |
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4 |
| KNG: The K-Norm Gradient Mechanism |
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2 |
| Kalman Filter, Sensor Fusion, and Constrained Regression: Equivalences and Insights |
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4 |
| Keeping Your Distance: Solving Sparse Reward Tasks Using Self-Balancing Shaped Rewards |
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2 |
| KerGM: Kernelized Graph Matching |
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4 |
| Kernel Instrumental Variable Regression |
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2 |
| Kernel Stein Tests for Multiple Model Comparison |
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4 |
| Kernel Truncated Randomized Ridge Regression: Optimal Rates and Low Noise Acceleration |
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2 |
| Kernel quadrature with DPPs |
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1 |
| Kernel-Based Approaches for Sequence Modeling: Connections to Neural Methods |
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5 |
| Kernelized Bayesian Softmax for Text Generation |
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4 |
| Knowledge Extraction with No Observable Data |
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4 |
| LCA: Loss Change Allocation for Neural Network Training |
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3 |
| LIIR: Learning Individual Intrinsic Reward in Multi-Agent Reinforcement Learning |
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5 |
| L_DMI: A Novel Information-theoretic Loss Function for Training Deep Nets Robust to Label Noise |
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5 |
| Landmark Ordinal Embedding |
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3 |
| Language as an Abstraction for Hierarchical Deep Reinforcement Learning |
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✅ |
5 |
| Large Memory Layers with Product Keys |
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5 |
| Large Scale Adversarial Representation Learning |
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4 |
| Large Scale Markov Decision Processes with Changing Rewards |
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1 |
| Large Scale Structure of Neural Network Loss Landscapes |
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2 |
| Large-scale optimal transport map estimation using projection pursuit |
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5 |
| Latent Ordinary Differential Equations for Irregularly-Sampled Time Series |
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3 |
| Latent Weights Do Not Exist: Rethinking Binarized Neural Network Optimization |
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5 |
| Latent distance estimation for random geometric graphs |
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2 |
| Layer-Dependent Importance Sampling for Training Deep and Large Graph Convolutional Networks |
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5 |
| Leader Stochastic Gradient Descent for Distributed Training of Deep Learning Models |
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6 |
| Learn, Imagine and Create: Text-to-Image Generation from Prior Knowledge |
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3 |
| Learnable Tree Filter for Structure-preserving Feature Transform |
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5 |
| Learner-aware Teaching: Inverse Reinforcement Learning with Preferences and Constraints |
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2 |
| Learning Auctions with Robust Incentive Guarantees |
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1 |
| Learning Bayesian Networks with Low Rank Conditional Probability Tables |
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1 |
| Learning Compositional Neural Programs with Recursive Tree Search and Planning |
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✅ |
3 |
| Learning Conditional Deformable Templates with Convolutional Networks |
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5 |
| Learning Data Manipulation for Augmentation and Weighting |
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6 |
| Learning Deep Bilinear Transformation for Fine-grained Image Representation |
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4 |
| Learning Deterministic Weighted Automata with Queries and Counterexamples |
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3 |
| Learning Disentangled Representation for Robust Person Re-identification |
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4 |
| Learning Disentangled Representations for Recommendation |
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3 |
| Learning Distributions Generated by One-Layer ReLU Networks |
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3 |
| Learning Dynamics of Attention: Human Prior for Interpretable Machine Reasoning |
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4 |
| Learning Erdos-Renyi Random Graphs via Edge Detecting Queries |
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3 |
| Learning Fairness in Multi-Agent Systems |
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3 |
| Learning GANs and Ensembles Using Discrepancy |
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3 |
| Learning Generalizable Device Placement Algorithms for Distributed Machine Learning |
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4 |
| Learning Hawkes Processes from a handful of events |
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4 |
| Learning Hierarchical Priors in VAEs |
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3 |
| Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss |
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4 |
| Learning Latent Process from High-Dimensional Event Sequences via Efficient Sampling |
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4 |
| Learning Local Search Heuristics for Boolean Satisfiability |
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2 |
| Learning Macroscopic Brain Connectomes via Group-Sparse Factorization |
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4 |
| Learning Mean-Field Games |
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2 |
| Learning Mixtures of Plackett-Luce Models from Structured Partial Orders |
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4 |
| Learning Multiple Markov Chains via Adaptive Allocation |
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1 |
| Learning Nearest Neighbor Graphs from Noisy Distance Samples |
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4 |
| Learning Neural Networks with Adaptive Regularization |
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4 |
| Learning New Tricks From Old Dogs: Multi-Source Transfer Learning From Pre-Trained Networks |
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4 |
| Learning Non-Convergent Non-Persistent Short-Run MCMC Toward Energy-Based Model |
✅ |
✅ |
✅ |
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✅ |
4 |
| Learning Nonsymmetric Determinantal Point Processes |
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5 |
| Learning Object Bounding Boxes for 3D Instance Segmentation on Point Clouds |
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6 |
| Learning Perceptual Inference by Contrasting |
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✅ |
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3 |
| Learning Positive Functions with Pseudo Mirror Descent |
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3 |
| Learning Representations by Maximizing Mutual Information Across Views |
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5 |
| Learning Representations for Time Series Clustering |
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4 |
| Learning Reward Machines for Partially Observable Reinforcement Learning |
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4 |
| Learning Robust Global Representations by Penalizing Local Predictive Power |
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4 |
| Learning Robust Options by Conditional Value at Risk Optimization |
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4 |
| Learning Sample-Specific Models with Low-Rank Personalized Regression |
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5 |
| Learning Sparse Distributions using Iterative Hard Thresholding |
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3 |
| Learning Stable Deep Dynamics Models |
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1 |
| Learning Temporal Pose Estimation from Sparsely-Labeled Videos |
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5 |
| Learning Transferable Graph Exploration |
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3 |
| Learning about an exponential amount of conditional distributions |
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✅ |
✅ |
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3 |
| Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers |
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2 |
| Learning by Abstraction: The Neural State Machine |
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❌ |
✅ |
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2 |
| Learning dynamic polynomial proofs |
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1 |
| Learning elementary structures for 3D shape generation and matching |
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5 |
| Learning from Bad Data via Generation |
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4 |
| Learning from Label Proportions with Generative Adversarial Networks |
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5 |
| Learning from Trajectories via Subgoal Discovery |
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3 |
| Learning from brains how to regularize machines |
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4 |
| Learning in Generalized Linear Contextual Bandits with Stochastic Delays |
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1 |
| Learning low-dimensional state embeddings and metastable clusters from time series data |
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2 |
| Learning metrics for persistence-based summaries and applications for graph classification |
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✅ |
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3 |
| Learning nonlinear level sets for dimensionality reduction in function approximation |
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5 |
| Learning search spaces for Bayesian optimization: Another view of hyperparameter transfer learning |
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4 |
| Learning step sizes for unfolded sparse coding |
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3 |
| Learning to Confuse: Generating Training Time Adversarial Data with Auto-Encoder |
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3 |
| Learning to Control Self-Assembling Morphologies: A Study of Generalization via Modularity |
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3 |
| Learning to Correlate in Multi-Player General-Sum Sequential Games |
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❌ |
✅ |
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3 |
| Learning to Infer Implicit Surfaces without 3D Supervision |
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✅ |
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4 |
| Learning to Learn By Self-Critique |
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4 |
| Learning to Optimize in Swarms |
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3 |
| Learning to Perform Local Rewriting for Combinatorial Optimization |
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✅ |
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4 |
| Learning to Predict 3D Objects with an Interpolation-based Differentiable Renderer |
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✅ |
✅ |
✅ |
❌ |
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4 |
| Learning to Predict Layout-to-image Conditional Convolutions for Semantic Image Synthesis |
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✅ |
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5 |
| Learning to Predict Without Looking Ahead: World Models Without Forward Prediction |
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3 |
| Learning to Propagate for Graph Meta-Learning |
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6 |
| Learning to Screen |
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❌ |
❌ |
❌ |
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0 |
| Learning to Self-Train for Semi-Supervised Few-Shot Classification |
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✅ |
✅ |
✅ |
❌ |
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4 |
| Learning-Based Low-Rank Approximations |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
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2 |
| Learning-In-The-Loop Optimization: End-To-End Control And Co-Design Of Soft Robots Through Learned Deep Latent Representations |
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❌ |
✅ |
✅ |
❌ |
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3 |
| Legendre Memory Units: Continuous-Time Representation in Recurrent Neural Networks |
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✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| Levenshtein Transformer |
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4 |
| Leveraging Labeled and Unlabeled Data for Consistent Fair Binary Classification |
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✅ |
✅ |
❌ |
❌ |
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4 |
| Likelihood Ratios for Out-of-Distribution Detection |
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✅ |
✅ |
✅ |
❌ |
❌ |
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5 |
| Likelihood-Free Overcomplete ICA and Applications In Causal Discovery |
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✅ |
✅ |
❌ |
❌ |
❌ |
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3 |
| Limitations of Lazy Training of Two-layers Neural Network |
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❌ |
❌ |
❌ |
❌ |
❌ |
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1 |
| Limitations of the empirical Fisher approximation for natural gradient descent |
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❌ |
✅ |
✅ |
❌ |
❌ |
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3 |
| Limiting Extrapolation in Linear Approximate Value Iteration |
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❌ |
❌ |
❌ |
❌ |
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2 |
| Limits of Private Learning with Access to Public Data |
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❌ |
❌ |
❌ |
❌ |
❌ |
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1 |
| Linear Stochastic Bandits Under Safety Constraints |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
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1 |
| List-decodable Linear Regression |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| LiteEval: A Coarse-to-Fine Framework for Resource Efficient Video Recognition |
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❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Loaded DiCE: Trading off Bias and Variance in Any-Order Score Function Gradient Estimators for Reinforcement Learning |
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✅ |
✅ |
❌ |
❌ |
❌ |
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4 |
| Local SGD with Periodic Averaging: Tighter Analysis and Adaptive Synchronization |
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✅ |
❌ |
✅ |
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5 |
| Locality-Sensitive Hashing for f-Divergences: Mutual Information Loss and Beyond |
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4 |
| Localized Structured Prediction |
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4 |
| Locally Private Gaussian Estimation |
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❌ |
❌ |
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1 |
| Locally Private Learning without Interaction Requires Separation |
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❌ |
❌ |
❌ |
❌ |
0 |
| Logarithmic Regret for Online Control |
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❌ |
❌ |
❌ |
❌ |
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1 |
| Lookahead Optimizer: k steps forward, 1 step back |
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✅ |
✅ |
✅ |
❌ |
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6 |
| Low-Complexity Nonparametric Bayesian Online Prediction with Universal Guarantees |
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✅ |
❌ |
✅ |
❌ |
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4 |
| Low-Rank Bandit Methods for High-Dimensional Dynamic Pricing |
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✅ |
❌ |
❌ |
❌ |
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3 |
| Lower Bounds on Adversarial Robustness from Optimal Transport |
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❌ |
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3 |
| MAVEN: Multi-Agent Variational Exploration |
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3 |
| MCP: Learning Composable Hierarchical Control with Multiplicative Compositional Policies |
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✅ |
❌ |
❌ |
❌ |
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3 |
| MaCow: Masked Convolutional Generative Flow |
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✅ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Machine Learning Estimation of Heterogeneous Treatment Effects with Instruments |
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✅ |
✅ |
✅ |
✅ |
❌ |
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6 |
| Machine Teaching of Active Sequential Learners |
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✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Making AI Forget You: Data Deletion in Machine Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Making the Cut: A Bandit-based Approach to Tiered Interviewing |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Manifold denoising by Nonlinear Robust Principal Component Analysis |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Manifold-regression to predict from MEG/EEG brain signals without source modeling |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Manipulating a Learning Defender and Ways to Counteract |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Mapping State Space using Landmarks for Universal Goal Reaching |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Margin-Based Generalization Lower Bounds for Boosted Classifiers |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| MarginGAN: Adversarial Training in Semi-Supervised Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Markov Random Fields for Collaborative Filtering |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Massively scalable Sinkhorn distances via the Nyström method |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Max-value Entropy Search for Multi-Objective Bayesian Optimization |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| MaxGap Bandit: Adaptive Algorithms for Approximate Ranking |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Maximum Entropy Monte-Carlo Planning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Maximum Expected Hitting Cost of a Markov Decision Process and Informativeness of Rewards |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Maximum Mean Discrepancy Gradient Flow |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| McDiarmid-Type Inequalities for Graph-Dependent Variables and Stability Bounds |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| MelGAN: Generative Adversarial Networks for Conditional Waveform Synthesis |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Memory Efficient Adaptive Optimization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Memory-oriented Decoder for Light Field Salient Object Detection |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Meta Architecture Search |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Meta Learning with Relational Information for Short Sequences |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Meta-Curvature |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Meta-Inverse Reinforcement Learning with Probabilistic Context Variables |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Meta-Learning Representations for Continual Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Meta-Learning with Implicit Gradients |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Meta-Reinforced Synthetic Data for One-Shot Fine-Grained Visual Recognition |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Meta-Surrogate Benchmarking for Hyperparameter Optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Meta-Weight-Net: Learning an Explicit Mapping For Sample Weighting |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| MetaInit: Initializing learning by learning to initialize |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| MetaQuant: Learning to Quantize by Learning to Penetrate Non-differentiable Quantization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Metalearned Neural Memory |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Metamers of neural networks reveal divergence from human perceptual systems |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Metric Learning for Adversarial Robustness |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Minimal Variance Sampling in Stochastic Gradient Boosting |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Minimax Optimal Estimation of Approximate Differential Privacy on Neighboring Databases |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
3 |
| Minimizers of the Empirical Risk and Risk Monotonicity |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Minimum Stein Discrepancy Estimators |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Mining GOLD Samples for Conditional GANs |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| MintNet: Building Invertible Neural Networks with Masked Convolutions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Missing Not at Random in Matrix Completion: The Effectiveness of Estimating Missingness Probabilities Under a Low Nuclear Norm Assumption |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| MixMatch: A Holistic Approach to Semi-Supervised Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Mixtape: Breaking the Softmax Bottleneck Efficiently |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Mo' States Mo' Problems: Emergency Stop Mechanisms from Observation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Model Compression with Adversarial Robustness: A Unified Optimization Framework |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Model Selection for Contextual Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Model Similarity Mitigates Test Set Overuse |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Modeling Conceptual Understanding in Image Reference Games |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Modeling Dynamic Functional Connectivity with Latent Factor Gaussian Processes |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
4 |
| Modeling Expectation Violation in Intuitive Physics with Coarse Probabilistic Object Representations |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Modeling Tabular data using Conditional GAN |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Modeling Uncertainty by Learning a Hierarchy of Deep Neural Connections |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Modelling heterogeneous distributions with an Uncountable Mixture of Asymmetric Laplacians |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Modelling the Dynamics of Multiagent Q-Learning in Repeated Symmetric Games: a Mean Field Theoretic Approach |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Modular Universal Reparameterization: Deep Multi-task Learning Across Diverse Domains |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Momentum-Based Variance Reduction in Non-Convex SGD |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| MonoForest framework for tree ensemble analysis |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
❌ |
3 |
| More Is Less: Learning Efficient Video Representations by Big-Little Network and Depthwise Temporal Aggregation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Multi-Agent Common Knowledge Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Multi-Criteria Dimensionality Reduction with Applications to Fairness |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Multi-Resolution Weak Supervision for Sequential Data |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multi-View Reinforcement Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Multi-label Co-regularization for Semi-supervised Facial Action Unit Recognition |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Multi-mapping Image-to-Image Translation via Learning Disentanglement |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Multi-marginal Wasserstein GAN |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Multi-objective Bayesian optimisation with preferences over objectives |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multi-objects Generation with Amortized Structural Regularization |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multi-relational Poincaré Graph Embeddings |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multi-resolution Multi-task Gaussian Processes |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Multi-source Domain Adaptation for Semantic Segmentation |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Multi-task Learning for Aggregated Data using Gaussian Processes |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multiagent Evaluation under Incomplete Information |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multiclass Learning from Contradictions |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Multiclass Performance Metric Elicitation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multilabel reductions: what is my loss optimising? |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Multimodal Model-Agnostic Meta-Learning via Task-Aware Modulation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Multiple Futures Prediction |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multivariate Distributionally Robust Convex Regression under Absolute Error Loss |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Multivariate Sparse Coding of Nonstationary Covariances with Gaussian Processes |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Multivariate Triangular Quantile Maps for Novelty Detection |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Multiview Aggregation for Learning Category-Specific Shape Reconstruction |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Multiway clustering via tensor block models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Mutually Regressive Point Processes |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Möbius Transformation for Fast Inner Product Search on Graph |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| N-Gram Graph: Simple Unsupervised Representation for Graphs, with Applications to Molecules |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| NAOMI: Non-Autoregressive Multiresolution Sequence Imputation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| NAT: Neural Architecture Transformer for Accurate and Compact Architectures |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Near Neighbor: Who is the Fairest of Them All? |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Near-Optimal Reinforcement Learning in Dynamic Treatment Regimes |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Nearly Tight Bounds for Robust Proper Learning of Halfspaces with a Margin |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Necessary and Sufficient Geometries for Gradient Methods |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Network Pruning via Transformable Architecture Search |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| NeurVPS: Neural Vanishing Point Scanning via Conic Convolution |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Neural Attribution for Semantic Bug-Localization in Student Programs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Neural Diffusion Distance for Image Segmentation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Neural Jump Stochastic Differential Equations |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Neural Lyapunov Control |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Neural Machine Translation with Soft Prototype |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Neural Multisensory Scene Inference |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Neural Networks with Cheap Differential Operators |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Neural Relational Inference with Fast Modular Meta-learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Neural Shuffle-Exchange Networks - Sequence Processing in O(n log n) Time |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Neural Similarity Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Neural Spline Flows |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Neural Taskonomy: Inferring the Similarity of Task-Derived Representations from Brain Activity |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Neural Temporal-Difference Learning Converges to Global Optima |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Neural Trust Region/Proximal Policy Optimization Attains Globally Optimal Policy |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Neural networks grown and self-organized by noise |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Neuropathic Pain Diagnosis Simulator for Causal Discovery Algorithm Evaluation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| No Pressure! Addressing the Problem of Local Minima in Manifold Learning Algorithms |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| No-Press Diplomacy: Modeling Multi-Agent Gameplay |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| No-Regret Learning in Unknown Games with Correlated Payoffs |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Noise-tolerant fair classification |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Non-Asymptotic Gap-Dependent Regret Bounds for Tabular MDPs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Non-Asymptotic Pure Exploration by Solving Games |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Non-Cooperative Inverse Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Non-Stationary Markov Decision Processes, a Worst-Case Approach using Model-Based Reinforcement Learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Non-asymptotic Analysis of Stochastic Methods for Non-Smooth Non-Convex Regularized Problems |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Non-normal Recurrent Neural Network (nnRNN): learning long time dependencies while improving expressivity with transient dynamics |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Nonconvex Low-Rank Tensor Completion from Noisy Data |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Nonlinear scaling of resource allocation in sensory bottlenecks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Nonparametric Contextual Bandits in Metric Spaces with Unknown Metric |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Nonparametric Density Estimation & Convergence Rates for GANs under Besov IPM Losses |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Nonparametric Regressive Point Processes Based on Conditional Gaussian Processes |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Nonstochastic Multiarmed Bandits with Unrestricted Delays |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Nonzero-sum Adversarial Hypothesis Testing Games |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Normalization Helps Training of Quantized LSTM |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Novel positional encodings to enable tree-based transformers |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Numerically Accurate Hyperbolic Embeddings Using Tiling-Based Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| ODE2VAE: Deep generative second order ODEs with Bayesian neural networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Object landmark discovery through unsupervised adaptation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| ObjectNet: A large-scale bias-controlled dataset for pushing the limits of object recognition models |
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| Oblivious Sampling Algorithms for Private Data Analysis |
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| Off-Policy Evaluation via Off-Policy Classification |
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| Offline Contextual Bandits with High Probability Fairness Guarantees |
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| Offline Contextual Bayesian Optimization |
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| On Adversarial Mixup Resynthesis |
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| On Differentially Private Graph Sparsification and Applications |
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| On Distributed Averaging for Stochastic k-PCA |
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| On Exact Computation with an Infinitely Wide Neural Net |
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| On Fenchel Mini-Max Learning |
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5 |
| On Human-Aligned Risk Minimization |
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| On Lazy Training in Differentiable Programming |
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| On Learning Over-parameterized Neural Networks: A Functional Approximation Perspective |
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| On Making Stochastic Classifiers Deterministic |
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| On Mixup Training: Improved Calibration and Predictive Uncertainty for Deep Neural Networks |
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| On Relating Explanations and Adversarial Examples |
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6 |
| On Robustness of Principal Component Regression |
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| On Robustness to Adversarial Examples and Polynomial Optimization |
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| On Sample Complexity Upper and Lower Bounds for Exact Ranking from Noisy Comparisons |
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| On Single Source Robustness in Deep Fusion Models |
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| On Testing for Biases in Peer Review |
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| On The Classification-Distortion-Perception Tradeoff |
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| On Tractable Computation of Expected Predictions |
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| On the (In)fidelity and Sensitivity of Explanations |
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| On the Accuracy of Influence Functions for Measuring Group Effects |
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| On the Calibration of Multiclass Classification with Rejection |
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2 |
| On the Convergence Rate of Training Recurrent Neural Networks |
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| On the Correctness and Sample Complexity of Inverse Reinforcement Learning |
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| On the Curved Geometry of Accelerated Optimization |
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| On the Downstream Performance of Compressed Word Embeddings |
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3 |
| On the Expressive Power of Deep Polynomial Neural Networks |
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| On the Fairness of Disentangled Representations |
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| On the Global Convergence of (Fast) Incremental Expectation Maximization Methods |
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| On the Hardness of Robust Classification |
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| On the Inductive Bias of Neural Tangent Kernels |
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| On the Ineffectiveness of Variance Reduced Optimization for Deep Learning |
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| On the Optimality of Perturbations in Stochastic and Adversarial Multi-armed Bandit Problems |
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| On the Power and Limitations of Random Features for Understanding Neural Networks |
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| On the Transfer of Inductive Bias from Simulation to the Real World: a New Disentanglement Dataset |
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| On the Utility of Learning about Humans for Human-AI Coordination |
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| On the Value of Target Data in Transfer Learning |
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| On the convergence of single-call stochastic extra-gradient methods |
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| On the equivalence between graph isomorphism testing and function approximation with GNNs |
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| On the number of variables to use in principal component regression |
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| On two ways to use determinantal point processes for Monte Carlo integration |
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| One ticket to win them all: generalizing lottery ticket initializations across datasets and optimizers |
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| One-Shot Object Detection with Co-Attention and Co-Excitation |
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| Online Continual Learning with Maximal Interfered Retrieval |
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| Online Continuous Submodular Maximization: From Full-Information to Bandit Feedback |
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| Online Convex Matrix Factorization with Representative Regions |
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| Online EXP3 Learning in Adversarial Bandits with Delayed Feedback |
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| Online Forecasting of Total-Variation-bounded Sequences |
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| Online Learning via the Differential Privacy Lens |
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| Online Markov Decoding: Lower Bounds and Near-Optimal Approximation Algorithms |
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| Online Normalization for Training Neural Networks |
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| Online Optimal Control with Linear Dynamics and Predictions: Algorithms and Regret Analysis |
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| Online Prediction of Switching Graph Labelings with Cluster Specialists |
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| Online Stochastic Shortest Path with Bandit Feedback and Unknown Transition Function |
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| Online sampling from log-concave distributions |
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| Online-Within-Online Meta-Learning |
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| Optimal Analysis of Subset-Selection Based L_p Low-Rank Approximation |
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| Optimal Best Markovian Arm Identification with Fixed Confidence |
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| Optimal Decision Tree with Noisy Outcomes |
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| Optimal Pricing in Repeated Posted-Price Auctions with Different Patience of the Seller and the Buyer |
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| Optimal Sampling and Clustering in the Stochastic Block Model |
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| Optimal Sketching for Kronecker Product Regression and Low Rank Approximation |
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| Optimal Sparse Decision Trees |
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| Optimal Sparsity-Sensitive Bounds for Distributed Mean Estimation |
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| Optimal Statistical Rates for Decentralised Non-Parametric Regression with Linear Speed-Up |
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| Optimal Stochastic and Online Learning with Individual Iterates |
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| Optimistic Distributionally Robust Optimization for Nonparametric Likelihood Approximation |
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| Optimistic Regret Minimization for Extensive-Form Games via Dilated Distance-Generating Functions |
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| Optimizing Generalized PageRank Methods for Seed-Expansion Community Detection |
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| Optimizing Generalized Rate Metrics with Three Players |
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| Oracle-Efficient Algorithms for Online Linear Optimization with Bandit Feedback |
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| Order Optimal One-Shot Distributed Learning |
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| Ordered Memory |
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| Ouroboros: On Accelerating Training of Transformer-Based Language Models |
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| Outlier Detection and Robust PCA Using a Convex Measure of Innovation |
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| Outlier-Robust High-Dimensional Sparse Estimation via Iterative Filtering |
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| Outlier-robust estimation of a sparse linear model using $\ell_1$-penalized Huber's $M$-estimator |
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| PAC-Bayes Un-Expected Bernstein Inequality |
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| PAC-Bayes under potentially heavy tails |
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| PC-Fairness: A Unified Framework for Measuring Causality-based Fairness |
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3 |
| PHYRE: A New Benchmark for Physical Reasoning |
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| PIDForest: Anomaly Detection via Partial Identification |
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| PRNet: Self-Supervised Learning for Partial-to-Partial Registration |
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| Painless Stochastic Gradient: Interpolation, Line-Search, and Convergence Rates |
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4 |
| Paradoxes in Fair Machine Learning |
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2 |
| Parameter elimination in particle Gibbs sampling |
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3 |
| Paraphrase Generation with Latent Bag of Words |
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3 |
| Pareto Multi-Task Learning |
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3 |
| Park: An Open Platform for Learning-Augmented Computer Systems |
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| Partially Encrypted Deep Learning using Functional Encryption |
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| Partitioning Structure Learning for Segmented Linear Regression Trees |
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| PasteGAN: A Semi-Parametric Method to Generate Image from Scene Graph |
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| Perceiving the arrow of time in autoregressive motion |
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2 |
| Personalizing Many Decisions with High-Dimensional Covariates |
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| PerspectiveNet: 3D Object Detection from a Single RGB Image via Perspective Points |
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| PerspectiveNet: A Scene-consistent Image Generator for New View Synthesis in Real Indoor Environments |
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| Phase Transitions and Cyclic Phenomena in Bandits with Switching Constraints |
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| Piecewise Strong Convexity of Neural Networks |
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| Planning in entropy-regularized Markov decision processes and games |
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| Planning with Goal-Conditioned Policies |
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3 |
| Poincaré Recurrence, Cycles and Spurious Equilibria in Gradient-Descent-Ascent for Non-Convex Non-Concave Zero-Sum Games |
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| Point-Voxel CNN for Efficient 3D Deep Learning |
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| PointDAN: A Multi-Scale 3D Domain Adaption Network for Point Cloud Representation |
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| Poisson-Minibatching for Gibbs Sampling with Convergence Rate Guarantees |
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3 |
| Poisson-Randomized Gamma Dynamical Systems |
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4 |
| Policy Continuation with Hindsight Inverse Dynamics |
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4 |
| Policy Evaluation with Latent Confounders via Optimal Balance |
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4 |
| Policy Learning for Fairness in Ranking |
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2 |
| Policy Optimization Provably Converges to Nash Equilibria in Zero-Sum Linear Quadratic Games |
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| Policy Poisoning in Batch Reinforcement Learning and Control |
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2 |
| Polynomial Cost of Adaptation for X-Armed Bandits |
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1 |
| Positional Normalization |
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4 |
| Positive-Unlabeled Compression on the Cloud |
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| Post training 4-bit quantization of convolutional networks for rapid-deployment |
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4 |
| Power analysis of knockoff filters for correlated designs |
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1 |
| PowerSGD: Practical Low-Rank Gradient Compression for Distributed Optimization |
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4 |
| Powerset Convolutional Neural Networks |
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4 |
| Practical Deep Learning with Bayesian Principles |
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6 |
| Practical Differentially Private Top-k Selection with Pay-what-you-get Composition |
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1 |
| Practical Two-Step Lookahead Bayesian Optimization |
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4 |
| Practical and Consistent Estimation of f-Divergences |
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3 |
| Precision-Recall Balanced Topic Modelling |
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3 |
| Predicting the Politics of an Image Using Webly Supervised Data |
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3 |
| Prediction of Spatial Point Processes: Regularized Method with Out-of-Sample Guarantees |
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3 |
| Preference-Based Batch and Sequential Teaching: Towards a Unified View of Models |
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1 |
| Preventing Gradient Attenuation in Lipschitz Constrained Convolutional Networks |
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❌ |
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❌ |
✅ |
4 |
| Primal-Dual Block Generalized Frank-Wolfe |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Prior-Free Dynamic Auctions with Low Regret Buyers |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Privacy Amplification by Mixing and Diffusion Mechanisms |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Privacy-Preserving Classification of Personal Text Messages with Secure Multi-Party Computation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Privacy-Preserving Q-Learning with Functional Noise in Continuous Spaces |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Private Hypothesis Selection |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Private Learning Implies Online Learning: An Efficient Reduction |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Private Stochastic Convex Optimization with Optimal Rates |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Private Testing of Distributions via Sample Permutations |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Probabilistic Logic Neural Networks for Reasoning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Probabilistic Watershed: Sampling all spanning forests for seeded segmentation and semi-supervised learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Procrastinating with Confidence: Near-Optimal, Anytime, Adaptive Algorithm Configuration |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Program Synthesis and Semantic Parsing with Learned Code Idioms |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Progressive Augmentation of GANs |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Projected Stein Variational Newton: A Fast and Scalable Bayesian Inference Method in High Dimensions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Propagating Uncertainty in Reinforcement Learning via Wasserstein Barycenters |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Provable Certificates for Adversarial Examples: Fitting a Ball in the Union of Polytopes |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Provable Gradient Variance Guarantees for Black-Box Variational Inference |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Provable Non-linear Inductive Matrix Completion |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Provably Efficient Q-Learning with Low Switching Cost |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Provably Efficient Q-learning with Function Approximation via Distribution Shift Error Checking Oracle |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Provably Global Convergence of Actor-Critic: A Case for Linear Quadratic Regulator with Ergodic Cost |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Provably Powerful Graph Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Provably Robust Deep Learning via Adversarially Trained Smoothed Classifiers |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Provably robust boosted decision stumps and trees against adversarial attacks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Pseudo-Extended Markov chain Monte Carlo |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Pure Exploration with Multiple Correct Answers |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Push-pull Feedback Implements Hierarchical Information Retrieval Efficiently |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Putting An End to End-to-End: Gradient-Isolated Learning of Representations |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| PyTorch: An Imperative Style, High-Performance Deep Learning Library |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Qsparse-local-SGD: Distributed SGD with Quantization, Sparsification and Local Computations |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Quadratic Video Interpolation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Quality Aware Generative Adversarial Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Quantum Embedding of Knowledge for Reasoning |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Quantum Entropy Scoring for Fast Robust Mean Estimation and Improved Outlier Detection |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Quantum Wasserstein Generative Adversarial Networks |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
2 |
| Quaternion Knowledge Graph Embeddings |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| R2D2: Reliable and Repeatable Detector and Descriptor |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| REM: From Structural Entropy to Community Structure Deception |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| RSN: Randomized Subspace Newton |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| RUBi: Reducing Unimodal Biases for Visual Question Answering |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| RUDDER: Return Decomposition for Delayed Rewards |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Random Path Selection for Continual Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Random Projections and Sampling Algorithms for Clustering of High-Dimensional Polygonal Curves |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Random Projections with Asymmetric Quantization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Random Quadratic Forms with Dependence: Applications to Restricted Isometry and Beyond |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Random Tessellation Forests |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Random deep neural networks are biased towards simple functions |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Rapid Convergence of the Unadjusted Langevin Algorithm: Isoperimetry Suffices |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Rates of Convergence for Large-scale Nearest Neighbor Classification |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Re-examination of the Role of Latent Variables in Sequence Modeling |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Re-randomized Densification for One Permutation Hashing and Bin-wise Consistent Weighted Sampling |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Real-Time Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Reconciling meta-learning and continual learning with online mixtures of tasks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Reconciling λ-Returns with Experience Replay |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Recovering Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Recurrent Kernel Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Recurrent Registration Neural Networks for Deformable Image Registration |
❌ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
3 |
| Recurrent Space-time Graph Neural Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Reducing Noise in GAN Training with Variance Reduced Extragradient |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Reducing the variance in online optimization by transporting past gradients |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Reflection Separation using a Pair of Unpolarized and Polarized Images |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Region Mutual Information Loss for Semantic Segmentation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Region-specific Diffeomorphic Metric Mapping |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Regression Planning Networks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Regret Bounds for Learning State Representations in Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Regret Bounds for Thompson Sampling in Episodic Restless Bandit Problems |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Regret Minimization for Reinforcement Learning by Evaluating the Optimal Bias Function |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Regret Minimization for Reinforcement Learning with Vectorial Feedback and Complex Objectives |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Regularization Matters: Generalization and Optimization of Neural Nets v.s. their Induced Kernel |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Regularized Anderson Acceleration for Off-Policy Deep Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Regularized Gradient Boosting |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Regularized Weighted Low Rank Approximation |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Regularizing Trajectory Optimization with Denoising Autoencoders |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Reinforcement Learning with Convex Constraints |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Reliable training and estimation of variance networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| ResNets Ensemble via the Feynman-Kac Formalism to Improve Natural and Robust Accuracies |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Residual Flows for Invertible Generative Modeling |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Rethinking Deep Neural Network Ownership Verification: Embedding Passports to Defeat Ambiguity Attacks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Rethinking Generative Mode Coverage: A Pointwise Guaranteed Approach |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Rethinking Kernel Methods for Node Representation Learning on Graphs |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Rethinking the CSC Model for Natural Images |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Retrosynthesis Prediction with Conditional Graph Logic Network |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Reverse KL-Divergence Training of Prior Networks: Improved Uncertainty and Adversarial Robustness |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Reverse engineering recurrent networks for sentiment classification reveals line attractor dynamics |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Revisiting the Bethe-Hessian: Improved Community Detection in Sparse Heterogeneous Graphs |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Riemannian batch normalization for SPD neural networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Robust Attribution Regularization |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Robust Bi-Tempered Logistic Loss Based on Bregman Divergences |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Robust Multi-agent Counterfactual Prediction |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Robust Principal Component Analysis with Adaptive Neighbors |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Robust and Communication-Efficient Collaborative Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Robust exploration in linear quadratic reinforcement learning |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Robustness Verification of Tree-based Models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Robustness to Adversarial Perturbations in Learning from Incomplete Data |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Root Mean Square Layer Normalization |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
4 |
| SCAN: A Scalable Neural Networks Framework Towards Compact and Efficient Models |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| SGD on Neural Networks Learns Functions of Increasing Complexity |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| SHE: A Fast and Accurate Deep Neural Network for Encrypted Data |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| SIC-MMAB: Synchronisation Involves Communication in Multiplayer Multi-Armed Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| SMILe: Scalable Meta Inverse Reinforcement Learning through Context-Conditional Policies |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| SPoC: Search-based Pseudocode to Code |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| SSRGD: Simple Stochastic Recursive Gradient Descent for Escaping Saddle Points |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| STAR-Caps: Capsule Networks with Straight-Through Attentive Routing |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| STREETS: A Novel Camera Network Dataset for Traffic Flow |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Saccader: Improving Accuracy of Hard Attention Models for Vision |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Safe Exploration for Interactive Machine Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Same-Cluster Querying for Overlapping Clusters |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Sample Adaptive MCMC |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Sample Complexity of Learning Mixture of Sparse Linear Regressions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Sample Efficient Active Learning of Causal Trees |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Sample-Efficient Deep Reinforcement Learning via Episodic Backward Update |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Sampled Softmax with Random Fourier Features |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Sampling Networks and Aggregate Simulation for Online POMDP Planning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Sampling Sketches for Concave Sublinear Functions of Frequencies |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Scalable Bayesian dynamic covariance modeling with variational Wishart and inverse Wishart processes |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Scalable Bayesian inference of dendritic voltage via spatiotemporal recurrent state space models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Scalable Deep Generative Relational Model with High-Order Node Dependence |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Scalable Global Optimization via Local Bayesian Optimization |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Scalable Gromov-Wasserstein Learning for Graph Partitioning and Matching |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Scalable Spike Source Localization in Extracellular Recordings using Amortized Variational Inference |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Scalable Structure Learning of Continuous-Time Bayesian Networks from Incomplete Data |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Scalable inference of topic evolution via models for latent geometric structures |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Scene Representation Networks: Continuous 3D-Structure-Aware Neural Scene Representations |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Screening Sinkhorn Algorithm for Regularized Optimal Transport |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Search on the Replay Buffer: Bridging Planning and Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Search-Guided, Lightly-Supervised Training of Structured Prediction Energy Networks |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
4 |
| Secretary Ranking with Minimal Inversions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
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1 |
| Seeing the Wind: Visual Wind Speed Prediction with a Coupled Convolutional and Recurrent Neural Network |
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| Selecting Optimal Decisions via Distributionally Robust Nearest-Neighbor Regression |
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| Selecting causal brain features with a single conditional independence test per feature |
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| Selecting the independent coordinates of manifolds with large aspect ratios |
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| Selective Sampling-based Scalable Sparse Subspace Clustering |
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5 |
| Self-Critical Reasoning for Robust Visual Question Answering |
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| Self-Routing Capsule Networks |
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| Self-Supervised Deep Learning on Point Clouds by Reconstructing Space |
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| Self-Supervised Generalisation with Meta Auxiliary Learning |
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4 |
| Self-attention with Functional Time Representation Learning |
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3 |
| Self-supervised GAN: Analysis and Improvement with Multi-class Minimax Game |
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| Semantic Conditioned Dynamic Modulation for Temporal Sentence Grounding in Videos |
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4 |
| Semantic-Guided Multi-Attention Localization for Zero-Shot Learning |
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4 |
| Semi-Implicit Graph Variational Auto-Encoders |
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4 |
| Semi-Parametric Dynamic Contextual Pricing |
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1 |
| Semi-Parametric Efficient Policy Learning with Continuous Actions |
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3 |
| Semi-flat minima and saddle points by embedding neural networks to overparameterization |
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2 |
| Semi-supervisedly Co-embedding Attributed Networks |
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4 |
| Sequence Modeling with Unconstrained Generation Order |
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4 |
| Sequential Experimental Design for Transductive Linear Bandits |
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3 |
| Sequential Neural Processes |
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1 |
| Shadowing Properties of Optimization Algorithms |
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2 |
| Shallow RNN: Accurate Time-series Classification on Resource Constrained Devices |
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4 |
| Shape and Time Distortion Loss for Training Deep Time Series Forecasting Models |
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4 |
| Shaping Belief States with Generative Environment Models for RL |
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3 |
| Sim2real transfer learning for 3D human pose estimation: motion to the rescue |
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2 |
| Single-Model Uncertainties for Deep Learning |
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4 |
| Singleshot : a scalable Tucker tensor decomposition |
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4 |
| Sinkhorn Barycenters with Free Support via Frank-Wolfe Algorithm |
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4 |
| Slice-based Learning: A Programming Model for Residual Learning in Critical Data Slices |
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| Sliced Gromov-Wasserstein |
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3 |
| Small ReLU networks are powerful memorizers: a tight analysis of memorization capacity |
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| Smoothing Structured Decomposable Circuits |
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4 |
| Sobolev Independence Criterion |
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5 |
| Social-BiGAT: Multimodal Trajectory Forecasting using Bicycle-GAN and Graph Attention Networks |
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| Solving Interpretable Kernel Dimensionality Reduction |
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6 |
| Solving a Class of Non-Convex Min-Max Games Using Iterative First Order Methods |
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| Solving graph compression via optimal transport |
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| SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained Microcontrollers |
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| Space and Time Efficient Kernel Density Estimation in High Dimensions |
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4 |
| Sparse High-Dimensional Isotonic Regression |
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| Sparse Logistic Regression Learns All Discrete Pairwise Graphical Models |
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| Sparse Variational Inference: Bayesian Coresets from Scratch |
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| Spatial-Aware Feature Aggregation for Image based Cross-View Geo-Localization |
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| Spatially Aggregated Gaussian Processes with Multivariate Areal Outputs |
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| Specific and Shared Causal Relation Modeling and Mechanism-Based Clustering |
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2 |
| Spectral Modification of Graphs for Improved Spectral Clustering |
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5 |
| Spherical Text Embedding |
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| SpiderBoost and Momentum: Faster Variance Reduction Algorithms |
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| Spike-Train Level Backpropagation for Training Deep Recurrent Spiking Neural Networks |
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4 |
| Splitting Steepest Descent for Growing Neural Architectures |
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| Stability of Graph Scattering Transforms |
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| Stabilizing Off-Policy Q-Learning via Bootstrapping Error Reduction |
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2 |
| Stacked Capsule Autoencoders |
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| Stagewise Training Accelerates Convergence of Testing Error Over SGD |
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3 |
| Stand-Alone Self-Attention in Vision Models |
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4 |
| State Aggregation Learning from Markov Transition Data |
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2 |
| Statistical Analysis of Nearest Neighbor Methods for Anomaly Detection |
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3 |
| Statistical Model Aggregation via Parameter Matching |
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| Statistical bounds for entropic optimal transport: sample complexity and the central limit theorem |
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| Statistical-Computational Tradeoff in Single Index Models |
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| Staying up to Date with Online Content Changes Using Reinforcement Learning for Scheduling |
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4 |
| Stein Variational Gradient Descent With Matrix-Valued Kernels |
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5 |
| Stochastic Bandits with Context Distributions |
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3 |
| Stochastic Continuous Greedy ++: When Upper and Lower Bounds Match |
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1 |
| Stochastic Frank-Wolfe for Composite Convex Minimization |
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7 |
| Stochastic Gradient Hamiltonian Monte Carlo Methods with Recursive Variance Reduction |
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| Stochastic Proximal Langevin Algorithm: Potential Splitting and Nonasymptotic Rates |
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| Stochastic Runge-Kutta Accelerates Langevin Monte Carlo and Beyond |
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| Stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers |
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| Stochastic Variance Reduced Primal Dual Algorithms for Empirical Composition Optimization |
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| Strategizing against No-regret Learners |
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| Streaming Bayesian Inference for Crowdsourced Classification |
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| Structure Learning with Side Information: Sample Complexity |
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| Structured Graph Learning Via Laplacian Spectral Constraints |
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| Structured Prediction with Projection Oracles |
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| Structured Variational Inference in Continuous Cox Process Models |
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| Structured and Deep Similarity Matching via Structured and Deep Hebbian Networks |
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| Submodular Function Minimization with Noisy Evaluation Oracle |
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| Subquadratic High-Dimensional Hierarchical Clustering |
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4 |
| Subspace Attack: Exploiting Promising Subspaces for Query-Efficient Black-box Attacks |
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| Subspace Detours: Building Transport Plans that are Optimal on Subspace Projections |
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3 |
| Successor Uncertainties: Exploration and Uncertainty in Temporal Difference Learning |
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4 |
| SuperGLUE: A Stickier Benchmark for General-Purpose Language Understanding Systems |
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| Superposition of many models into one |
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| Superset Technique for Approximate Recovery in One-Bit Compressed Sensing |
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| Surfing: Iterative Optimization Over Incrementally Trained Deep Networks |
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| Surrogate Objectives for Batch Policy Optimization in One-step Decision Making |
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| Surround Modulation: A Bio-inspired Connectivity Structure for Convolutional Neural Networks |
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| SySCD: A System-Aware Parallel Coordinate Descent Algorithm |
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5 |
| Symmetry-Based Disentangled Representation Learning requires Interaction with Environments |
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| Symmetry-adapted generation of 3d point sets for the targeted discovery of molecules |
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4 |
| TAB-VCR: Tags and Attributes based VCR Baselines |
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| Teaching Multiple Concepts to a Forgetful Learner |
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2 |
| Temporal FiLM: Capturing Long-Range Sequence Dependencies with Feature-Wise Modulations. |
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| Tensor Monte Carlo: Particle Methods for the GPU era |
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| Text-Based Interactive Recommendation via Constraint-Augmented Reinforcement Learning |
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4 |
| The Broad Optimality of Profile Maximum Likelihood |
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| The Case for Evaluating Causal Models Using Interventional Measures and Empirical Data |
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1 |
| The Cells Out of Sample (COOS) dataset and benchmarks for measuring out-of-sample generalization of image classifiers |
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| The Convergence Rate of Neural Networks for Learned Functions of Different Frequencies |
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| The Fairness of Risk Scores Beyond Classification: Bipartite Ranking and the XAUC Metric |
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| The Functional Neural Process |
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| The Geometry of Deep Networks: Power Diagram Subdivision |
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| The Impact of Regularization on High-dimensional Logistic Regression |
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| The Implicit Bias of AdaGrad on Separable Data |
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| The Implicit Metropolis-Hastings Algorithm |
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| The Label Complexity of Active Learning from Observational Data |
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| The Landscape of Non-convex Empirical Risk with Degenerate Population Risk |
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1 |
| The Normalization Method for Alleviating Pathological Sharpness in Wide Neural Networks |
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| The Option Keyboard: Combining Skills in Reinforcement Learning |
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| The Parameterized Complexity of Cascading Portfolio Scheduling |
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| The Point Where Reality Meets Fantasy: Mixed Adversarial Generators for Image Splice Detection |
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4 |
| The Randomized Midpoint Method for Log-Concave Sampling |
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| The Step Decay Schedule: A Near Optimal, Geometrically Decaying Learning Rate Procedure For Least Squares |
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4 |
| The Synthesis of XNOR Recurrent Neural Networks with Stochastic Logic |
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3 |
| The Thermodynamic Variational Objective |
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| The continuous Bernoulli: fixing a pervasive error in variational autoencoders |
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| The spiked matrix model with generative priors |
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5 |
| Theoretical Analysis of Adversarial Learning: A Minimax Approach |
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| Theoretical Limits of Pipeline Parallel Optimization and Application to Distributed Deep Learning |
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3 |
| Theoretical evidence for adversarial robustness through randomization |
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3 |
| Think Globally, Act Locally: A Deep Neural Network Approach to High-Dimensional Time Series Forecasting |
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6 |
| Think out of the "Box": Generically-Constrained Asynchronous Composite Optimization and Hedging |
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1 |
| Thinning for Accelerating the Learning of Point Processes |
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4 |
| Third-Person Visual Imitation Learning via Decoupled Hierarchical Controller |
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4 |
| This Looks Like That: Deep Learning for Interpretable Image Recognition |
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4 |
| Thompson Sampling and Approximate Inference |
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1 |
| Thompson Sampling for Multinomial Logit Contextual Bandits |
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✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Thompson Sampling with Information Relaxation Penalties |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Thresholding Bandit with Optimal Aggregate Regret |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Tight Certificates of Adversarial Robustness for Randomly Smoothed Classifiers |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Tight Dimension Independent Lower Bound on the Expected Convergence Rate for Diminishing Step Sizes in SGD |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Tight Dimensionality Reduction for Sketching Low Degree Polynomial Kernels |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Tight Regret Bounds for Model-Based Reinforcement Learning with Greedy Policies |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Tight Sample Complexity of Learning One-hidden-layer Convolutional Neural Networks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Time Matters in Regularizing Deep Networks: Weight Decay and Data Augmentation Affect Early Learning Dynamics, Matter Little Near Convergence |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Time-series Generative Adversarial Networks |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Time/Accuracy Tradeoffs for Learning a ReLU with respect to Gaussian Marginals |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
2 |
| Topology-Preserving Deep Image Segmentation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Total Least Squares Regression in Input Sparsity Time |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Toward a Characterization of Loss Functions for Distribution Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Towards Automatic Concept-based Explanations |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Towards Explaining the Regularization Effect of Initial Large Learning Rate in Training Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Towards Hardware-Aware Tractable Learning of Probabilistic Models |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Towards Interpretable Reinforcement Learning Using Attention Augmented Agents |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Towards Optimal Off-Policy Evaluation for Reinforcement Learning with Marginalized Importance Sampling |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Towards Practical Alternating Least-Squares for CCA |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Towards Understanding the Importance of Shortcut Connections in Residual Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Towards a Zero-One Law for Column Subset Selection |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Towards closing the gap between the theory and practice of SVRG |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Towards modular and programmable architecture search |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Training Image Estimators without Image Ground Truth |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Training Language GANs from Scratch |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Trajectory of Alternating Direction Method of Multipliers and Adaptive Acceleration |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Transductive Zero-Shot Learning with Visual Structure Constraint |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Transfer Anomaly Detection by Inferring Latent Domain Representations |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Transfer Learning via Minimizing the Performance Gap Between Domains |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Transferable Normalization: Towards Improving Transferability of Deep Neural Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Transfusion: Understanding Transfer Learning for Medical Imaging |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Tree-Sliced Variants of Wasserstein Distances |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Triad Constraints for Learning Causal Structure of Latent Variables |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Trivializations for Gradient-Based Optimization on Manifolds |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Trust Region-Guided Proximal Policy Optimization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Turbo Autoencoder: Deep learning based channel codes for point-to-point communication channels |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Twin Auxilary Classifiers GAN |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
4 |
| Two Generator Game: Learning to Sample via Linear Goodness-of-Fit Test |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Two Time-scale Off-Policy TD Learning: Non-asymptotic Analysis over Markovian Samples |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| U-Time: A Fully Convolutional Network for Time Series Segmentation Applied to Sleep Staging |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Ultra Fast Medoid Identification via Correlated Sequential Halving |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Ultrametric Fitting by Gradient Descent |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Uncertainty on Asynchronous Time Event Prediction |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Uncertainty-based Continual Learning with Adaptive Regularization |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Unconstrained Monotonic Neural Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Uncoupled Regression from Pairwise Comparison Data |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Understanding Attention and Generalization in Graph Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Understanding Sparse JL for Feature Hashing |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Understanding and Improving Layer Normalization |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Understanding the Role of Momentum in Stochastic Gradient Methods |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| UniXGrad: A Universal, Adaptive Algorithm with Optimal Guarantees for Constrained Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Unified Language Model Pre-training for Natural Language Understanding and Generation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Unified Sample-Optimal Property Estimation in Near-Linear Time |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Uniform Error Bounds for Gaussian Process Regression with Application to Safe Control |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Uniform convergence may be unable to explain generalization in deep learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Universal Approximation of Input-Output Maps by Temporal Convolutional Nets |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Universal Boosting Variational Inference |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Universal Invariant and Equivariant Graph Neural Networks |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Universality and individuality in neural dynamics across large populations of recurrent networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Universality in Learning from Linear Measurements |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Unlabeled Data Improves Adversarial Robustness |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Unlocking Fairness: a Trade-off Revisited |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Unsupervised Co-Learning on $G$-Manifolds Across Irreducible Representations |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Unsupervised Curricula for Visual Meta-Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Unsupervised Discovery of Temporal Structure in Noisy Data with Dynamical Components Analysis |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Unsupervised Emergence of Egocentric Spatial Structure from Sensorimotor Prediction |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Unsupervised Keypoint Learning for Guiding Class-Conditional Video Prediction |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Unsupervised Learning of Object Keypoints for Perception and Control |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Unsupervised Meta-Learning for Few-Shot Image Classification |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Unsupervised Object Segmentation by Redrawing |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Unsupervised Scalable Representation Learning for Multivariate Time Series |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| Unsupervised Scale-consistent Depth and Ego-motion Learning from Monocular Video |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Unsupervised State Representation Learning in Atari |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Unsupervised learning of object structure and dynamics from videos |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Untangling in Invariant Speech Recognition |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Updates of Equilibrium Prop Match Gradients of Backprop Through Time in an RNN with Static Input |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| User-Specified Local Differential Privacy in Unconstrained Adaptive Online Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Using Embeddings to Correct for Unobserved Confounding in Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Using Self-Supervised Learning Can Improve Model Robustness and Uncertainty |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Using Statistics to Automate Stochastic Optimization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Using a Logarithmic Mapping to Enable Lower Discount Factors in Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| VIREL: A Variational Inference Framework for Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Value Function in Frequency Domain and the Characteristic Value Iteration Algorithm |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Value Propagation for Decentralized Networked Deep Multi-agent Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Variance Reduced Policy Evaluation with Smooth Function Approximation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Variance Reduction for Matrix Games |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Variance Reduction in Bipartite Experiments through Correlation Clustering |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Variational Bayes under Model Misspecification |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Variational Bayesian Decision-making for Continuous Utilities |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Variational Bayesian Optimal Experimental Design |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Variational Denoising Network: Toward Blind Noise Modeling and Removal |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Variational Graph Recurrent Neural Networks |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Variational Mixture-of-Experts Autoencoders for Multi-Modal Deep Generative Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Variational Structured Semantic Inference for Diverse Image Captioning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Variational Temporal Abstraction |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Verified Uncertainty Calibration |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| ViLBERT: Pretraining Task-Agnostic Visiolinguistic Representations for Vision-and-Language Tasks |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Visual Concept-Metaconcept Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Visual Sequence Learning in Hierarchical Prediction Networks and Primate Visual Cortex |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Visualizing and Measuring the Geometry of BERT |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Visualizing the PHATE of Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Volumetric Correspondence Networks for Optical Flow |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Wasserstein Dependency Measure for Representation Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Wasserstein Weisfeiler-Lehman Graph Kernels |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Weakly Supervised Instance Segmentation using the Bounding Box Tightness Prior |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Weight Agnostic Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Weighted Linear Bandits for Non-Stationary Environments |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| What Can ResNet Learn Efficiently, Going Beyond Kernels? |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| What the Vec? Towards Probabilistically Grounded Embeddings |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| When does label smoothing help? |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| When to Trust Your Model: Model-Based Policy Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| When to use parametric models in reinforcement learning? |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Which Algorithmic Choices Matter at Which Batch Sizes? Insights From a Noisy Quadratic Model |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Who is Afraid of Big Bad Minima? Analysis of gradient-flow in spiked matrix-tensor models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Why Can't I Dance in the Mall? Learning to Mitigate Scene Bias in Action Recognition |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Wide Feedforward or Recurrent Neural Networks of Any Architecture are Gaussian Processes |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Wide Neural Networks of Any Depth Evolve as Linear Models Under Gradient Descent |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Worst-Case Regret Bounds for Exploration via Randomized Value Functions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Write, Execute, Assess: Program Synthesis with a REPL |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| XLNet: Generalized Autoregressive Pretraining for Language Understanding |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| XNAS: Neural Architecture Search with Expert Advice |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| You Only Propagate Once: Accelerating Adversarial Training via Maximal Principle |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| ZO-AdaMM: Zeroth-Order Adaptive Momentum Method for Black-Box Optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Zero-Shot Semantic Segmentation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Zero-shot Knowledge Transfer via Adversarial Belief Matching |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Zero-shot Learning via Simultaneous Generating and Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| iSplit LBI: Individualized Partial Ranking with Ties via Split LBI |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| k-Means Clustering of Lines for Big Data |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| muSSP: Efficient Min-cost Flow Algorithm for Multi-object Tracking |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| q-means: A quantum algorithm for unsupervised machine learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| vGraph: A Generative Model for Joint Community Detection and Node Representation Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |