| (De)Randomized Smoothing for Certifiable Defense against Patch Attacks |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| 3D Multi-bodies: Fitting Sets of Plausible 3D Human Models to Ambiguous Image Data |
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❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| 3D Self-Supervised Methods for Medical Imaging |
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✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| 3D Shape Reconstruction from Vision and Touch |
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✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Bandit Learning Algorithm and Applications to Auction Design |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Bayesian Nonparametrics View into Deep Representations |
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❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Bayesian Perspective on Training Speed and Model Selection |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| A Benchmark for Systematic Generalization in Grounded Language Understanding |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A Biologically Plausible Neural Network for Slow Feature Analysis |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| A Boolean Task Algebra for Reinforcement Learning |
✅ |
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❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Catalyst Framework for Minimax Optimization |
✅ |
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❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| A Causal View on Robustness of Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Class of Algorithms for General Instrumental Variable Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| A Closer Look at Accuracy vs. Robustness |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Closer Look at the Training Strategy for Modern Meta-Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A Combinatorial Perspective on Transfer Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A Computational Separation between Private Learning and Online Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Continuous-Time Mirror Descent Approach to Sparse Phase Retrieval |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| A Contour Stochastic Gradient Langevin Dynamics Algorithm for Simulations of Multi-modal Distributions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Convolutional Auto-Encoder for Haplotype Assembly and Viral Quasispecies Reconstruction |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| A Decentralized Parallel Algorithm for Training Generative Adversarial Nets |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Dictionary Approach to Domain-Invariant Learning in Deep Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| A Discrete Variational Recurrent Topic Model without the Reparametrization Trick |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A Dynamical Central Limit Theorem for Shallow Neural Networks |
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❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| A Fair Classifier Using Kernel Density Estimation |
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❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| A Feasible Level Proximal Point Method for Nonconvex Sparse Constrained Optimization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A Finite-Time Analysis of Two Time-Scale Actor-Critic Methods |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Flexible Framework for Designing Trainable Priors with Adaptive Smoothing and Game Encoding |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| A Game Theoretic Analysis of Additive Adversarial Attacks and Defenses |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Game-Theoretic Analysis of the Empirical Revenue Maximization Algorithm with Endogenous Sampling |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A General Large Neighborhood Search Framework for Solving Integer Linear Programs |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| A General Method for Robust Learning from Batches |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A Generalized Neural Tangent Kernel Analysis for Two-layer Neural Networks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Group-Theoretic Framework for Data Augmentation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| A Limitation of the PAC-Bayes Framework |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A Local Temporal Difference Code for Distributional Reinforcement Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A Loss Function for Generative Neural Networks Based on Watson’s Perceptual Model |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| A Matrix Chernoff Bound for Markov Chains and Its Application to Co-occurrence Matrices |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Maximum-Entropy Approach to Off-Policy Evaluation in Average-Reward MDPs |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| A Measure-Theoretic Approach to Kernel Conditional Mean Embeddings |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| A Non-Asymptotic Analysis for Stein Variational Gradient Descent |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A Novel Approach for Constrained Optimization in Graphical Models |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| A Novel Automated Curriculum Strategy to Solve Hard Sokoban Planning Instances |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Randomized Algorithm to Reduce the Support of Discrete Measures |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| A Ranking-based, Balanced Loss Function Unifying Classification and Localisation in Object Detection |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| A Robust Functional EM Algorithm for Incomplete Panel Count Data |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Scalable Approach for Privacy-Preserving Collaborative Machine Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| A Scalable MIP-based Method for Learning Optimal Multivariate Decision Trees |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| A Self-Tuning Actor-Critic Algorithm |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Simple Language Model for Task-Oriented Dialogue |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| A Simple and Efficient Smoothing Method for Faster Optimization and Local Exploration |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| A Single Recipe for Online Submodular Maximization with Adversarial or Stochastic Constraints |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Single-Loop Smoothed Gradient Descent-Ascent Algorithm for Nonconvex-Concave Min-Max Problems |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| A Spectral Energy Distance for Parallel Speech Synthesis |
❌ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| A Statistical Framework for Low-bitwidth Training of Deep Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| A Statistical Mechanics Framework for Task-Agnostic Sample Design in Machine Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| A Stochastic Path Integral Differential EstimatoR Expectation Maximization Algorithm |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Study on Encodings for Neural Architecture Search |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| A Theoretical Framework for Target Propagation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A Tight Lower Bound and Efficient Reduction for Swap Regret |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Topological Filter for Learning with Label Noise |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| A Unified Switching System Perspective and Convergence Analysis of Q-Learning Algorithms |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| A Unified View of Label Shift Estimation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| A Unifying View of Optimism in Episodic Reinforcement Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A Universal Approximation Theorem of Deep Neural Networks for Expressing Probability Distributions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A Variational Approach for Learning from Positive and Unlabeled Data |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| A causal view of compositional zero-shot recognition |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| A convex optimization formulation for multivariate regression |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| A game-theoretic analysis of networked system control for common-pool resource management using multi-agent reinforcement learning |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| A graph similarity for deep learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| A kernel test for quasi-independence |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| A mathematical model for automatic differentiation in machine learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| A mathematical theory of cooperative communication |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| A mean-field analysis of two-player zero-sum games |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| A meta-learning approach to (re)discover plasticity rules that carve a desired function into a neural network |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| A new convergent variant of Q-learning with linear function approximation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| A new inference approach for training shallow and deep generalized linear models of noisy interacting neurons |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| A novel variational form of the Schatten-$p$ quasi-norm |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
4 |
| A polynomial-time algorithm for learning nonparametric causal graphs |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| A shooting formulation of deep learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| A simple normative network approximates local non-Hebbian learning in the cortex |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| A/B Testing in Dense Large-Scale Networks: Design and Inference |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| AI Feynman 2.0: Pareto-optimal symbolic regression exploiting graph modularity |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| AOT: Appearance Optimal Transport Based Identity Swapping for Forgery Detection |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| ARMA Nets: Expanding Receptive Field for Dense Prediction |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| AViD Dataset: Anonymized Videos from Diverse Countries |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Accelerating Reinforcement Learning through GPU Atari Emulation |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Accelerating Training of Transformer-Based Language Models with Progressive Layer Dropping |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Acceleration with a Ball Optimization Oracle |
✅ |
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❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Achieving Equalized Odds by Resampling Sensitive Attributes |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Active Invariant Causal Prediction: Experiment Selection through Stability |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Active Structure Learning of Causal DAGs via Directed Clique Trees |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| AdaBelief Optimizer: Adapting Stepsizes by the Belief in Observed Gradients |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| AdaShare: Learning What To Share For Efficient Deep Multi-Task Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| AdaTune: Adaptive Tensor Program Compilation Made Efficient |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Adam with Bandit Sampling for Deep Learning |
✅ |
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✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Adaptation Properties Allow Identification of Optimized Neural Codes |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Adapting Neural Architectures Between Domains |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Adapting to Misspecification in Contextual Bandits |
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❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Adaptive Discretization for Model-Based Reinforcement Learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Adaptive Experimental Design with Temporal Interference: A Maximum Likelihood Approach |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Adaptive Gradient Quantization for Data-Parallel SGD |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Adaptive Graph Convolutional Recurrent Network for Traffic Forecasting |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Adaptive Importance Sampling for Finite-Sum Optimization and Sampling with Decreasing Step-Sizes |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Adaptive Learned Bloom Filter (Ada-BF): Efficient Utilization of the Classifier with Application to Real-Time Information Filtering on the Web |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Adaptive Learning of Rank-One Models for Efficient Pairwise Sequence Alignment |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Adaptive Online Estimation of Piecewise Polynomial Trends |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Adaptive Probing Policies for Shortest Path Routing |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Adaptive Reduced Rank Regression |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Adaptive Sampling for Stochastic Risk-Averse Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Adaptive Shrinkage Estimation for Streaming Graphs |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| AdvFlow: Inconspicuous Black-box Adversarial Attacks using Normalizing Flows |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Advances in Black-Box VI: Normalizing Flows, Importance Weighting, and Optimization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Adversarial Attacks on Deep Graph Matching |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Adversarial Attacks on Linear Contextual Bandits |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Adversarial Bandits with Corruptions: Regret Lower Bound and No-regret Algorithm |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Adversarial Blocking Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Adversarial Counterfactual Learning and Evaluation for Recommender System |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Adversarial Crowdsourcing Through Robust Rank-One Matrix Completion |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Adversarial Distributional Training for Robust Deep Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Adversarial Example Games |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Adversarial Learning for Robust Deep Clustering |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Adversarial Robustness of Supervised Sparse Coding |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Adversarial Self-Supervised Contrastive Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Adversarial Soft Advantage Fitting: Imitation Learning without Policy Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Adversarial Sparse Transformer for Time Series Forecasting |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Adversarial Style Mining for One-Shot Unsupervised Domain Adaptation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
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3 |
| Adversarial Training is a Form of Data-dependent Operator Norm Regularization |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
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2 |
| Adversarial Weight Perturbation Helps Robust Generalization |
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✅ |
✅ |
❌ |
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4 |
| Adversarial robustness via robust low rank representations |
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❌ |
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3 |
| Adversarially Robust Few-Shot Learning: A Meta-Learning Approach |
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✅ |
✅ |
❌ |
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4 |
| Adversarially Robust Streaming Algorithms via Differential Privacy |
✅ |
❌ |
❌ |
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❌ |
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1 |
| Adversarially-learned Inference via an Ensemble of Discrete Undirected Graphical Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Agnostic $Q$-learning with Function Approximation in Deterministic Systems: Near-Optimal Bounds on Approximation Error and Sample Complexity |
✅ |
❌ |
❌ |
❌ |
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❌ |
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1 |
| Agnostic Learning of a Single Neuron with Gradient Descent |
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0 |
| Agnostic Learning with Multiple Objectives |
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✅ |
❌ |
❌ |
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4 |
| Agree to Disagree: Adaptive Ensemble Knowledge Distillation in Gradient Space |
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✅ |
✅ |
❌ |
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4 |
| Algorithmic recourse under imperfect causal knowledge: a probabilistic approach |
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✅ |
✅ |
❌ |
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❌ |
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3 |
| All Word Embeddings from One Embedding |
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✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| All your loss are belong to Bayes |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| All-or-nothing statistical and computational phase transitions in sparse spiked matrix estimation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Almost Optimal Model-Free Reinforcement Learningvia Reference-Advantage Decomposition |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Almost Surely Stable Deep Dynamics |
❌ |
✅ |
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❌ |
❌ |
✅ |
2 |
| An Analysis of SVD for Deep Rotation Estimation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| An Asymptotically Optimal Primal-Dual Incremental Algorithm for Contextual Linear Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| An Efficient Adversarial Attack for Tree Ensembles |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| An Efficient Asynchronous Method for Integrating Evolutionary and Gradient-based Policy Search |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| An Efficient Framework for Clustered Federated Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| An Empirical Process Approach to the Union Bound: Practical Algorithms for Combinatorial and Linear Bandits |
✅ |
❌ |
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❌ |
❌ |
❌ |
✅ |
2 |
| An Equivalence between Loss Functions and Non-Uniform Sampling in Experience Replay |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| An Imitation from Observation Approach to Transfer Learning with Dynamics Mismatch |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| An Improved Analysis of (Variance-Reduced) Policy Gradient and Natural Policy Gradient Methods |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| An Improved Analysis of Stochastic Gradient Descent with Momentum |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| An Optimal Elimination Algorithm for Learning a Best Arm |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| An Unbiased Risk Estimator for Learning with Augmented Classes |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| An Unsupervised Information-Theoretic Perceptual Quality Metric |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| An analytic theory of shallow networks dynamics for hinge loss classification |
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✅ |
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✅ |
2 |
| An efficient nonconvex reformulation of stagewise convex optimization problems |
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✅ |
3 |
| An implicit function learning approach for parametric modal regression |
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❌ |
✅ |
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❌ |
❌ |
✅ |
2 |
| An operator view of policy gradient methods |
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❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Analysis and Design of Thompson Sampling for Stochastic Partial Monitoring |
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❌ |
❌ |
❌ |
✅ |
2 |
| Analytic Characterization of the Hessian in Shallow ReLU Models: A Tale of Symmetry |
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❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Analytical Probability Distributions and Exact Expectation-Maximization for Deep Generative Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Applications of Common Entropy for Causal Inference |
✅ |
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✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Approximate Cross-Validation for Structured Models |
✅ |
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✅ |
✅ |
✅ |
❌ |
❌ |
4 |
| Approximate Cross-Validation with Low-Rank Data in High Dimensions |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Approximate Heavily-Constrained Learning with Lagrange Multiplier Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Approximation Based Variance Reduction for Reparameterization Gradients |
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✅ |
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✅ |
✅ |
✅ |
5 |
| Assessing SATNet's Ability to Solve the Symbol Grounding Problem |
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✅ |
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❌ |
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2 |
| Assisted Learning: A Framework for Multi-Organization Learning |
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✅ |
✅ |
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❌ |
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3 |
| Asymmetric Shapley values: incorporating causal knowledge into model-agnostic explainability |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Asymptotic Guarantees for Generative Modeling Based on the Smooth Wasserstein Distance |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Asymptotic normality and confidence intervals for derivatives of 2-layers neural network in the random features model |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Asymptotically Optimal Exact Minibatch Metropolis-Hastings |
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✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Attack of the Tails: Yes, You Really Can Backdoor Federated Learning |
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✅ |
✅ |
❌ |
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3 |
| AttendLight: Universal Attention-Based Reinforcement Learning Model for Traffic Signal Control |
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❌ |
✅ |
✅ |
❌ |
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3 |
| Attention-Gated Brain Propagation: How the brain can implement reward-based error backpropagation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
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3 |
| Attribute Prototype Network for Zero-Shot Learning |
❌ |
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✅ |
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3 |
| Attribution Preservation in Network Compression for Reliable Network Interpretation |
❌ |
❌ |
✅ |
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❌ |
❌ |
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2 |
| Audeo: Audio Generation for a Silent Performance Video |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Auditing Differentially Private Machine Learning: How Private is Private SGD? |
✅ |
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✅ |
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❌ |
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3 |
| Auto Learning Attention |
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4 |
| Auto-Panoptic: Cooperative Multi-Component Architecture Search for Panoptic Segmentation |
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✅ |
✅ |
✅ |
❌ |
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6 |
| AutoBSS: An Efficient Algorithm for Block Stacking Style Search |
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✅ |
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3 |
| AutoPrivacy: Automated Layer-wise Parameter Selection for Secure Neural Network Inference |
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3 |
| AutoSync: Learning to Synchronize for Data-Parallel Distributed Deep Learning |
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3 |
| Autoencoders that don't overfit towards the Identity |
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3 |
| Autofocused oracles for model-based design |
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4 |
| Automatic Curriculum Learning through Value Disagreement |
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❌ |
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5 |
| Automatic Perturbation Analysis for Scalable Certified Robustness and Beyond |
✅ |
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✅ |
❌ |
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5 |
| Automatically Learning Compact Quality-aware Surrogates for Optimization Problems |
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✅ |
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❌ |
✅ |
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5 |
| Autoregressive Score Matching |
❌ |
❌ |
✅ |
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✅ |
❌ |
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2 |
| Auxiliary Task Reweighting for Minimum-data Learning |
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❌ |
❌ |
❌ |
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3 |
| AvE: Assistance via Empowerment |
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4 |
| Avoiding Side Effects By Considering Future Tasks |
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❌ |
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3 |
| Avoiding Side Effects in Complex Environments |
✅ |
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✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Axioms for Learning from Pairwise Comparisons |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| BAIL: Best-Action Imitation Learning for Batch Deep Reinforcement Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| BERT Loses Patience: Fast and Robust Inference with Early Exit |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| BOSS: Bayesian Optimization over String Spaces |
❌ |
✅ |
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❌ |
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4 |
| BRP-NAS: Prediction-based NAS using GCNs |
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5 |
| Backpropagating Linearly Improves Transferability of Adversarial Examples |
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4 |
| Bad Global Minima Exist and SGD Can Reach Them |
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✅ |
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5 |
| Balanced Meta-Softmax for Long-Tailed Visual Recognition |
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3 |
| Bandit Linear Control |
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❌ |
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1 |
| Bandit Samplers for Training Graph Neural Networks |
✅ |
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✅ |
✅ |
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❌ |
✅ |
6 |
| BanditPAM: Almost Linear Time k-Medoids Clustering via Multi-Armed Bandits |
✅ |
✅ |
✅ |
❌ |
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4 |
| Barking up the right tree: an approach to search over molecule synthesis DAGs |
✅ |
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✅ |
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3 |
| Batch Normalization Biases Residual Blocks Towards the Identity Function in Deep Networks |
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4 |
| Batch normalization provably avoids ranks collapse for randomly initialised deep networks |
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✅ |
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❌ |
❌ |
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2 |
| Batched Coarse Ranking in Multi-Armed Bandits |
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❌ |
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4 |
| Baxter Permutation Process |
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❌ |
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3 |
| BayReL: Bayesian Relational Learning for Multi-omics Data Integration |
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❌ |
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4 |
| Bayes Consistency vs. H-Consistency: The Interplay between Surrogate Loss Functions and the Scoring Function Class |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
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3 |
| Bayesian Attention Modules |
✅ |
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✅ |
✅ |
❌ |
✅ |
6 |
| Bayesian Bits: Unifying Quantization and Pruning |
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3 |
| Bayesian Causal Structural Learning with Zero-Inflated Poisson Bayesian Networks |
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4 |
| Bayesian Deep Ensembles via the Neural Tangent Kernel |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Bayesian Deep Learning and a Probabilistic Perspective of Generalization |
❌ |
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✅ |
✅ |
❌ |
❌ |
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4 |
| Bayesian Meta-Learning for the Few-Shot Setting via Deep Kernels |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
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5 |
| Bayesian Multi-type Mean Field Multi-agent Imitation Learning |
✅ |
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❌ |
❌ |
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1 |
| Bayesian Optimization for Iterative Learning |
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✅ |
5 |
| Bayesian Optimization of Risk Measures |
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❌ |
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2 |
| Bayesian Probabilistic Numerical Integration with Tree-Based Models |
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3 |
| Bayesian Pseudocoresets |
✅ |
✅ |
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❌ |
❌ |
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3 |
| Bayesian Robust Optimization for Imitation Learning |
❌ |
✅ |
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❌ |
❌ |
❌ |
✅ |
2 |
| Bayesian filtering unifies adaptive and non-adaptive neural network optimization methods |
✅ |
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✅ |
❌ |
❌ |
❌ |
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3 |
| Belief Propagation Neural Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
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3 |
| Belief-Dependent Macro-Action Discovery in POMDPs using the Value of Information |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Benchmarking Deep Inverse Models over time, and the Neural-Adjoint method |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Benchmarking Deep Learning Interpretability in Time Series Predictions |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Beta Embeddings for Multi-Hop Logical Reasoning in Knowledge Graphs |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| Beta R-CNN: Looking into Pedestrian Detection from Another Perspective |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Better Full-Matrix Regret via Parameter-Free Online Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Better Set Representations For Relational Reasoning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Beyond Homophily in Graph Neural Networks: Current Limitations and Effective Designs |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Beyond Lazy Training for Over-parameterized Tensor Decomposition |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Beyond Perturbations: Learning Guarantees with Arbitrary Adversarial Test Examples |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Beyond accuracy: quantifying trial-by-trial behaviour of CNNs and humans by measuring error consistency |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Beyond the Mean-Field: Structured Deep Gaussian Processes Improve the Predictive Uncertainties |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Bi-level Score Matching for Learning Energy-based Latent Variable Models |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Bias no more: high-probability data-dependent regret bounds for adversarial bandits and MDPs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Biased Stochastic First-Order Methods for Conditional Stochastic Optimization and Applications in Meta Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Bidirectional Convolutional Poisson Gamma Dynamical Systems |
❌ |
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✅ |
✅ |
✅ |
❌ |
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5 |
| Big Bird: Transformers for Longer Sequences |
❌ |
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✅ |
❌ |
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❌ |
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3 |
| Big Self-Supervised Models are Strong Semi-Supervised Learners |
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❌ |
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5 |
| Biological credit assignment through dynamic inversion of feedforward networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
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2 |
| Biologically Inspired Mechanisms for Adversarial Robustness |
❌ |
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✅ |
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❌ |
❌ |
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2 |
| Black-Box Certification with Randomized Smoothing: A Functional Optimization Based Framework |
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❌ |
✅ |
❌ |
❌ |
❌ |
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1 |
| Black-Box Optimization with Local Generative Surrogates |
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3 |
| Black-Box Ripper: Copying black-box models using generative evolutionary algorithms |
✅ |
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✅ |
❌ |
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4 |
| Blind Video Temporal Consistency via Deep Video Prior |
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5 |
| BlockGAN: Learning 3D Object-aware Scene Representations from Unlabelled Images |
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✅ |
✅ |
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2 |
| BoTorch: A Framework for Efficient Monte-Carlo Bayesian Optimization |
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❌ |
3 |
| Bongard-LOGO: A New Benchmark for Human-Level Concept Learning and Reasoning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Boosting Adversarial Training with Hypersphere Embedding |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Boosting First-Order Methods by Shifting Objective: New Schemes with Faster Worst-Case Rates |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Bootstrap Your Own Latent - A New Approach to Self-Supervised Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Bootstrapping neural processes |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Boundary thickness and robustness in learning models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| BoxE: A Box Embedding Model for Knowledge Base Completion |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Breaking Reversibility Accelerates Langevin Dynamics for Non-Convex Optimization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Breaking the Communication-Privacy-Accuracy Trilemma |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Breaking the Sample Size Barrier in Model-Based Reinforcement Learning with a Generative Model |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Bridging Imagination and Reality for Model-Based Deep Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Bridging the Gap between Sample-based and One-shot Neural Architecture Search with BONAS |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Building powerful and equivariant graph neural networks with structural message-passing |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Byzantine Resilient Distributed Multi-Task Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| CASTLE: Regularization via Auxiliary Causal Graph Discovery |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| CHIP: A Hawkes Process Model for Continuous-time Networks with Scalable and Consistent Estimation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| CLEARER: Multi-Scale Neural Architecture Search for Image Restoration |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| CO-Optimal Transport |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| COBE: Contextualized Object Embeddings from Narrated Instructional Video |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| COOT: Cooperative Hierarchical Transformer for Video-Text Representation Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| COPT: Coordinated Optimal Transport on Graphs |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| COT-GAN: Generating Sequential Data via Causal Optimal Transport |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| CSER: Communication-efficient SGD with Error Reset |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| CSI: Novelty Detection via Contrastive Learning on Distributionally Shifted Instances |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| CaSPR: Learning Canonical Spatiotemporal Point Cloud Representations |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Calibrated Reliable Regression using Maximum Mean Discrepancy |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Calibrating CNNs for Lifelong Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Calibrating Deep Neural Networks using Focal Loss |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Calibration of Shared Equilibria in General Sum Partially Observable Markov Games |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Can Graph Neural Networks Count Substructures? |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Can I Trust My Fairness Metric? Assessing Fairness with Unlabeled Data and Bayesian Inference |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Can Implicit Bias Explain Generalization? Stochastic Convex Optimization as a Case Study |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Can Q-Learning with Graph Networks Learn a Generalizable Branching Heuristic for a SAT Solver? |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Can Temporal-Difference and Q-Learning Learn Representation? A Mean-Field Theory |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Can the Brain Do Backpropagation? --- Exact Implementation of Backpropagation in Predictive Coding Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Canonical 3D Deformer Maps: Unifying parametric and non-parametric methods for dense weakly-supervised category reconstruction |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Cascaded Text Generation with Markov Transformers |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Causal Discovery from Soft Interventions with Unknown Targets: Characterization and Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Causal Discovery in Physical Systems from Videos |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Causal Estimation with Functional Confounders |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Causal Imitation Learning With Unobserved Confounders |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Causal Intervention for Weakly-Supervised Semantic Segmentation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Causal Shapley Values: Exploiting Causal Knowledge to Explain Individual Predictions of Complex Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Causal analysis of Covid-19 Spread in Germany |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Certifiably Adversarially Robust Detection of Out-of-Distribution Data |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Certified Defense to Image Transformations via Randomized Smoothing |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Certified Monotonic Neural Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Certified Robustness of Graph Convolution Networks for Graph Classification under Topological Attacks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Certifying Confidence via Randomized Smoothing |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Certifying Strategyproof Auction Networks |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Chaos, Extremism and Optimism: Volume Analysis of Learning in Games |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Characterizing Optimal Mixed Policies: Where to Intervene and What to Observe |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Characterizing emergent representations in a space of candidate learning rules for deep networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Choice Bandits |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| CircleGAN: Generative Adversarial Learning across Spherical Circles |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Classification Under Misspecification: Halfspaces, Generalized Linear Models, and Evolvability |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Classification with Valid and Adaptive Coverage |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Closing the Dequantization Gap: PixelCNN as a Single-Layer Flow |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Co-Tuning for Transfer Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Co-exposure Maximization in Online Social Networks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| CoADNet: Collaborative Aggregation-and-Distribution Networks for Co-Salient Object Detection |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| CoMIR: Contrastive Multimodal Image Representation for Registration |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| CoSE: Compositional Stroke Embeddings |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| CodeCMR: Cross-Modal Retrieval For Function-Level Binary Source Code Matching |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Coded Sequential Matrix Multiplication For Straggler Mitigation |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| CogLTX: Applying BERT to Long Texts |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| CogMol: Target-Specific and Selective Drug Design for COVID-19 Using Deep Generative Models |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Coherent Hierarchical Multi-Label Classification Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| CoinDICE: Off-Policy Confidence Interval Estimation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| CoinPress: Practical Private Mean and Covariance Estimation |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| ColdGANs: Taming Language GANs with Cautious Sampling Strategies |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Collapsing Bandits and Their Application to Public Health Intervention |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Collegial Ensembles |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Color Visual Illusions: A Statistics-based Computational Model |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Combining Deep Reinforcement Learning and Search for Imperfect-Information Games |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Community detection in sparse time-evolving graphs with a dynamical Bethe-Hessian |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| Community detection using fast low-cardinality semidefinite programming |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| CompRess: Self-Supervised Learning by Compressing Representations |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Compact task representations as a normative model for higher-order brain activity |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Comparator-Adaptive Convex Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Complex Dynamics in Simple Neural Networks: Understanding Gradient Flow in Phase Retrieval |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Compositional Explanations of Neurons |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Compositional Generalization by Learning Analytical Expressions |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Compositional Generalization via Neural-Symbolic Stack Machines |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Compositional Visual Generation with Energy Based Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Compositional Zero-Shot Learning via Fine-Grained Dense Feature Composition |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Comprehensive Attention Self-Distillation for Weakly-Supervised Object Detection |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Compressing Images by Encoding Their Latent Representations with Relative Entropy Coding |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Computing Valid p-value for Optimal Changepoint by Selective Inference using Dynamic Programming |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Conditioning and Processing: Techniques to Improve Information-Theoretic Generalization Bounds |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Confidence sequences for sampling without replacement |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Conformal Symplectic and Relativistic Optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Confounding-Robust Policy Evaluation in Infinite-Horizon Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
3 |
| Conic Descent and its Application to Memory-efficient Optimization over Positive Semidefinite Matrices |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Consequences of Misaligned AI |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Conservative Q-Learning for Offline Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Consistency Regularization for Certified Robustness of Smoothed Classifiers |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Consistent Estimation of Identifiable Nonparametric Mixture Models from Grouped Observations |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Consistent Plug-in Classifiers for Complex Objectives and Constraints |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Consistent Structural Relation Learning for Zero-Shot Segmentation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Consistent feature selection for analytic deep neural networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Constant-Expansion Suffices for Compressed Sensing with Generative Priors |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Constrained episodic reinforcement learning in concave-convex and knapsack settings |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Constraining Variational Inference with Geometric Jensen-Shannon Divergence |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Content Provider Dynamics and Coordination in Recommendation Ecosystems |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Contextual Games: Multi-Agent Learning with Side Information |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Contextual Reserve Price Optimization in Auctions via Mixed Integer Programming |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Continual Deep Learning by Functional Regularisation of Memorable Past |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Continual Learning in Low-rank Orthogonal Subspaces |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Continual Learning of Control Primitives : Skill Discovery via Reset-Games |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Continual Learning of a Mixed Sequence of Similar and Dissimilar Tasks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Continual Learning with Node-Importance based Adaptive Group Sparse Regularization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Continuous Meta-Learning without Tasks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Continuous Object Representation Networks: Novel View Synthesis without Target View Supervision |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Continuous Regularized Wasserstein Barycenters |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Continuous Submodular Maximization: Beyond DR-Submodularity |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Continuous Surface Embeddings |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| ContraGAN: Contrastive Learning for Conditional Image Generation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Contrastive Learning with Adversarial Examples |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Contrastive learning of global and local features for medical image segmentation with limited annotations |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| ConvBERT: Improving BERT with Span-based Dynamic Convolution |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Convergence and Stability of Graph Convolutional Networks on Large Random Graphs |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Convergence of Meta-Learning with Task-Specific Adaptation over Partial Parameters |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Convex optimization based on global lower second-order models |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| Convolutional Generation of Textured 3D Meshes |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Convolutional Tensor-Train LSTM for Spatio-Temporal Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Cooperative Heterogeneous Deep Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Cooperative Multi-player Bandit Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Coresets for Near-Convex Functions |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Coresets for Regressions with Panel Data |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Coresets for Robust Training of Deep Neural Networks against Noisy Labels |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Coresets via Bilevel Optimization for Continual Learning and Streaming |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Correlation Robust Influence Maximization |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Correspondence learning via linearly-invariant embedding |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Counterexample-Guided Learning of Monotonic Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Counterfactual Contrastive Learning for Weakly-Supervised Vision-Language Grounding |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Counterfactual Data Augmentation using Locally Factored Dynamics |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Counterfactual Prediction for Bundle Treatment |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Counterfactual Predictions under Runtime Confounding |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Counterfactual Vision-and-Language Navigation: Unravelling the Unseen |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Coupling-based Invertible Neural Networks Are Universal Diffeomorphism Approximators |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Cream of the Crop: Distilling Prioritized Paths For One-Shot Neural Architecture Search |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Critic Regularized Regression |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Cross-Scale Internal Graph Neural Network for Image Super-Resolution |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Cross-lingual Retrieval for Iterative Self-Supervised Training |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Cross-validation Confidence Intervals for Test Error |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| CrossTransformers: spatially-aware few-shot transfer |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Crush Optimism with Pessimism: Structured Bandits Beyond Asymptotic Optimality |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| CryptoNAS: Private Inference on a ReLU Budget |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Curriculum By Smoothing |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Curriculum Learning by Dynamic Instance Hardness |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Curriculum learning for multilevel budgeted combinatorial problems |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
5 |
| Curvature Regularization to Prevent Distortion in Graph Embedding |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Cycle-Contrast for Self-Supervised Video Representation Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| DAGs with No Fears: A Closer Look at Continuous Optimization for Learning Bayesian Networks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| DISK: Learning local features with policy gradient |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| DVERGE: Diversifying Vulnerabilities for Enhanced Robust Generation of Ensembles |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Dark Experience for General Continual Learning: a Strong, Simple Baseline |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Data Diversification: A Simple Strategy For Neural Machine Translation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| De-Anonymizing Text by Fingerprinting Language Generation |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Debiased Contrastive Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Debiasing Averaged Stochastic Gradient Descent to handle missing values |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Debiasing Distributed Second Order Optimization with Surrogate Sketching and Scaled Regularization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Debugging Tests for Model Explanations |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Decentralized Accelerated Proximal Gradient Descent |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Decentralized Langevin Dynamics for Bayesian Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Decentralized TD Tracking with Linear Function Approximation and its Finite-Time Analysis |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Decision trees as partitioning machines to characterize their generalization properties |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Decision-Making with Auto-Encoding Variational Bayes |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Decisions, Counterfactual Explanations and Strategic Behavior |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Deep Archimedean Copulas |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Deep Automodulators |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Deep Diffusion-Invariant Wasserstein Distributional Classification |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Deep Direct Likelihood Knockoffs |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Deep Energy-based Modeling of Discrete-Time Physics |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Deep Evidential Regression |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Deep Graph Pose: a semi-supervised deep graphical model for improved animal pose tracking |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Deep Imitation Learning for Bimanual Robotic Manipulation |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Deep Inverse Q-learning with Constraints |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Deep Metric Learning with Spherical Embedding |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Deep Multimodal Fusion by Channel Exchanging |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Deep Rao-Blackwellised Particle Filters for Time Series Forecasting |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Deep Reinforcement Learning with Stacked Hierarchical Attention for Text-based Games |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Deep Reinforcement and InfoMax Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Deep Relational Topic Modeling via Graph Poisson Gamma Belief Network |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Deep Shells: Unsupervised Shape Correspondence with Optimal Transport |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Deep Smoothing of the Implied Volatility Surface |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Deep Statistical Solvers |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Deep Structural Causal Models for Tractable Counterfactual Inference |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Deep Subspace Clustering with Data Augmentation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Deep Transformation-Invariant Clustering |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Deep Transformers with Latent Depth |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Deep Variational Instance Segmentation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Deep Wiener Deconvolution: Wiener Meets Deep Learning for Image Deblurring |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Deep active inference agents using Monte-Carlo methods |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Deep learning versus kernel learning: an empirical study of loss landscape geometry and the time evolution of the Neural Tangent Kernel |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
3 |
| Deep reconstruction of strange attractors from time series |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| DeepI2I: Enabling Deep Hierarchical Image-to-Image Translation by Transferring from GANs |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| DeepSVG: A Hierarchical Generative Network for Vector Graphics Animation |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Deeply Learned Spectral Total Variation Decomposition |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Delay and Cooperation in Nonstochastic Linear Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Delta-STN: Efficient Bilevel Optimization for Neural Networks using Structured Response Jacobians |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Delving into the Cyclic Mechanism in Semi-supervised Video Object Segmentation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Demixed shared component analysis of neural population data from multiple brain areas |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Demystifying Contrastive Self-Supervised Learning: Invariances, Augmentations and Dataset Biases |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Demystifying Orthogonal Monte Carlo and Beyond |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Denoised Smoothing: A Provable Defense for Pretrained Classifiers |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Denoising Diffusion Probabilistic Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Dense Correspondences between Human Bodies via Learning Transformation Synchronization on Graphs |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Depth Uncertainty in Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Design Space for Graph Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Detecting Hands and Recognizing Physical Contact in the Wild |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Detecting Interactions from Neural Networks via Topological Analysis |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Detection as Regression: Certified Object Detection with Median Smoothing |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Deterministic Approximation for Submodular Maximization over a Matroid in Nearly Linear Time |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Dialog without Dialog Data: Learning Visual Dialog Agents from VQA Data |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| DiffGCN: Graph Convolutional Networks via Differential Operators and Algebraic Multigrid Pooling |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Differentiable Augmentation for Data-Efficient GAN Training |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Differentiable Causal Discovery from Interventional Data |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Differentiable Expected Hypervolume Improvement for Parallel Multi-Objective Bayesian Optimization |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Differentiable Meta-Learning of Bandit Policies |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Differentiable Neural Architecture Search in Equivalent Space with Exploration Enhancement |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Differentiable Top-k with Optimal Transport |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Differentially Private Clustering: Tight Approximation Ratios |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Differentially-Private Federated Linear Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Digraph Inception Convolutional Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Direct Feedback Alignment Scales to Modern Deep Learning Tasks and Architectures |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Direct Policy Gradients: Direct Optimization of Policies in Discrete Action Spaces |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Directional Pruning of Deep Neural Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Directional convergence and alignment in deep learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Dirichlet Graph Variational Autoencoder |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| DisARM: An Antithetic Gradient Estimator for Binary Latent Variables |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| DisCor: Corrective Feedback in Reinforcement Learning via Distribution Correction |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Discover, Hallucinate, and Adapt: Open Compound Domain Adaptation for Semantic Segmentation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Discovering Reinforcement Learning Algorithms |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Discovering Symbolic Models from Deep Learning with Inductive Biases |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Discovering conflicting groups in signed networks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| Discriminative Sounding Objects Localization via Self-supervised Audiovisual Matching |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Disentangling Human Error from Ground Truth in Segmentation of Medical Images |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Disentangling by Subspace Diffusion |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Displacement-Invariant Matching Cost Learning for Accurate Optical Flow Estimation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Dissecting Neural ODEs |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
3 |
| Distance Encoding: Design Provably More Powerful Neural Networks for Graph Representation Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Distributed Distillation for On-Device Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Distributed Newton Can Communicate Less and Resist Byzantine Workers |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Distributed Training with Heterogeneous Data: Bridging Median- and Mean-Based Algorithms |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Distribution Aligning Refinery of Pseudo-label for Imbalanced Semi-supervised Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Distribution Matching for Crowd Counting |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Distribution-free binary classification: prediction sets, confidence intervals and calibration |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Distributional Robustness with IPMs and links to Regularization and GANs |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Distributionally Robust Federated Averaging |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Distributionally Robust Local Non-parametric Conditional Estimation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Distributionally Robust Parametric Maximum Likelihood Estimation |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Diverse Image Captioning with Context-Object Split Latent Spaces |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Diversity can be Transferred: Output Diversification for White- and Black-box Attacks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Diversity-Guided Multi-Objective Bayesian Optimization With Batch Evaluations |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Do Adversarially Robust ImageNet Models Transfer Better? |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Does Unsupervised Architecture Representation Learning Help Neural Architecture Search? |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Domain Adaptation as a Problem of Inference on Graphical Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Domain Adaptation with Conditional Distribution Matching and Generalized Label Shift |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Domain Generalization for Medical Imaging Classification with Linear-Dependency Regularization |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Domain Generalization via Entropy Regularization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Doubly Robust Off-Policy Value and Gradient Estimation for Deterministic Policies |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Dual Instrumental Variable Regression |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Dual Manifold Adversarial Robustness: Defense against Lp and non-Lp Adversarial Attacks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Dual T: Reducing Estimation Error for Transition Matrix in Label-noise Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Dual-Free Stochastic Decentralized Optimization with Variance Reduction |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Dual-Resolution Correspondence Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Duality-Induced Regularizer for Tensor Factorization Based Knowledge Graph Completion |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| DynaBERT: Dynamic BERT with Adaptive Width and Depth |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Dynamic Fusion of Eye Movement Data and Verbal Narrations in Knowledge-rich Domains |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
3 |
| Dynamic Regret of Convex and Smooth Functions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Dynamic Regret of Policy Optimization in Non-Stationary Environments |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Dynamic Submodular Maximization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Dynamic allocation of limited memory resources in reinforcement learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Dynamical mean-field theory for stochastic gradient descent in Gaussian mixture classification |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Early-Learning Regularization Prevents Memorization of Noisy Labels |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| EcoLight: Intersection Control in Developing Regions Under Extreme Budget and Network Constraints |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Effective Dimension Adaptive Sketching Methods for Faster Regularized Least-Squares Optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Effective Diversity in Population Based Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Efficient Algorithms for Device Placement of DNN Graph Operators |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Efficient Clustering Based On A Unified View Of $K$-means And Ratio-cut |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Efficient Clustering for Stretched Mixtures: Landscape and Optimality |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient Contextual Bandits with Continuous Actions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient Distance Approximation for Structured High-Dimensional Distributions via Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Efficient Exact Verification of Binarized Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Efficient Exploration of Reward Functions in Inverse Reinforcement Learning via Bayesian Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Efficient Generation of Structured Objects with Constrained Adversarial Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Efficient Learning of Discrete Graphical Models |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Efficient Learning of Generative Models via Finite-Difference Score Matching |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Efficient Low Rank Gaussian Variational Inference for Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient Marginalization of Discrete and Structured Latent Variables via Sparsity |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient Model-Based Reinforcement Learning through Optimistic Policy Search and Planning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient Nonmyopic Bayesian Optimization via One-Shot Multi-Step Trees |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient Online Learning of Optimal Rankings: Dimensionality Reduction via Gradient Descent |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient Planning in Large MDPs with Weak Linear Function Approximation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Efficient Projection-free Algorithms for Saddle Point Problems |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient Variational Inference for Sparse Deep Learning with Theoretical Guarantee |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient active learning of sparse halfspaces with arbitrary bounded noise |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Efficient estimation of neural tuning during naturalistic behavior |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient semidefinite-programming-based inference for binary and multi-class MRFs |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Elastic-InfoGAN: Unsupervised Disentangled Representation Learning in Class-Imbalanced Data |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Election Coding for Distributed Learning: Protecting SignSGD against Byzantine Attacks |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Emergent Reciprocity and Team Formation from Randomized Uncertain Social Preferences |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Empirical Likelihood for Contextual Bandits |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Enabling certification of verification-agnostic networks via memory-efficient semidefinite programming |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| End-to-End Learning and Intervention in Games |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Energy-based Out-of-distribution Detection |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Ensemble Distillation for Robust Model Fusion in Federated Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Ensembling geophysical models with Bayesian Neural Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Ensuring Fairness Beyond the Training Data |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Entropic Causal Inference: Identifiability and Finite Sample Results |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Entropic Optimal Transport between Unbalanced Gaussian Measures has a Closed Form |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Entrywise convergence of iterative methods for eigenproblems |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Equivariant Networks for Hierarchical Structures |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Erdos Goes Neural: an Unsupervised Learning Framework for Combinatorial Optimization on Graphs |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
4 |
| Error Bounds of Imitating Policies and Environments |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Escaping Saddle-Point Faster under Interpolation-like Conditions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Escaping the Gravitational Pull of Softmax |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Estimating Fluctuations in Neural Representations of Uncertain Environments |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Estimating Rank-One Spikes from Heavy-Tailed Noise via Self-Avoiding Walks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Estimating Training Data Influence by Tracing Gradient Descent |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Estimating decision tree learnability with polylogarithmic sample complexity |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Estimating the Effects of Continuous-valued Interventions using Generative Adversarial Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Estimating weighted areas under the ROC curve |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Estimation and Imputation in Probabilistic Principal Component Analysis with Missing Not At Random Data |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Estimation of Skill Distribution from a Tournament |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Evaluating Attribution for Graph Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Evaluating and Rewarding Teamwork Using Cooperative Game Abstractions |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Every View Counts: Cross-View Consistency in 3D Object Detection with Hybrid-Cylindrical-Spherical Voxelization |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Evidential Sparsification of Multimodal Latent Spaces in Conditional Variational Autoencoders |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| EvolveGraph: Multi-Agent Trajectory Prediction with Dynamic Relational Reasoning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Evolving Graphical Planner: Contextual Global Planning for Vision-and-Language Navigation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Evolving Normalization-Activation Layers |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Exact Recovery of Mangled Clusters with Same-Cluster Queries |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Exact expressions for double descent and implicit regularization via surrogate random design |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Exactly Computing the Local Lipschitz Constant of ReLU Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Exchangeable Neural ODE for Set Modeling |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Exemplar Guided Active Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Exemplar VAE: Linking Generative Models, Nearest Neighbor Retrieval, and Data Augmentation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| ExpandNets: Linear Over-parameterization to Train Compact Convolutional Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Experimental design for MRI by greedy policy search |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Explainable Voting |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Explaining Naive Bayes and Other Linear Classifiers with Polynomial Time and Delay |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| Explicit Regularisation in Gaussian Noise Injections |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Exploiting Higher Order Smoothness in Derivative-free Optimization and Continuous Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Exploiting MMD and Sinkhorn Divergences for Fair and Transferable Representation Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Exploiting the Surrogate Gap in Online Multiclass Classification |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Exploiting weakly supervised visual patterns to learn from partial annotations |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Explore Aggressively, Update Conservatively: Stochastic Extragradient Methods with Variable Stepsize Scaling |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Exponential ergodicity of mirror-Langevin diffusions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Extrapolation Towards Imaginary 0-Nearest Neighbour and Its Improved Convergence Rate |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| FLAMBE: Structural Complexity and Representation Learning of Low Rank MDPs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Factor Graph Grammars |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Factor Graph Neural Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Factorizable Graph Convolutional Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Factorized Neural Processes for Neural Processes: K-Shot Prediction of Neural Responses |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
4 |
| Fair Hierarchical Clustering |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Fair Multiple Decision Making Through Soft Interventions |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Fair Performance Metric Elicitation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Fair regression via plug-in estimator and recalibration with statistical guarantees |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Fair regression with Wasserstein barycenters |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Fairness constraints can help exact inference in structured prediction |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Fairness in Streaming Submodular Maximization: Algorithms and Hardness |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Fairness with Overlapping Groups; a Probabilistic Perspective |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Fairness without Demographics through Adversarially Reweighted Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Faithful Embeddings for Knowledge Base Queries |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Falcon: Fast Spectral Inference on Encrypted Data |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Fast Adaptive Non-Monotone Submodular Maximization Subject to a Knapsack Constraint |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fast Adversarial Robustness Certification of Nearest Prototype Classifiers for Arbitrary Seminorms |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fast Convergence of Langevin Dynamics on Manifold: Geodesics meet Log-Sobolev |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Fast Epigraphical Projection-based Incremental Algorithms for Wasserstein Distributionally Robust Support Vector Machine |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
❌ |
4 |
| Fast Fourier Convolution |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Fast Matrix Square Roots with Applications to Gaussian Processes and Bayesian Optimization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Fast Transformers with Clustered Attention |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Fast Unbalanced Optimal Transport on a Tree |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Fast and Accurate $k$-means++ via Rejection Sampling |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Fast and Flexible Temporal Point Processes with Triangular Maps |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Fast geometric learning with symbolic matrices |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Fast, Accurate, and Simple Models for Tabular Data via Augmented Distillation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Faster DBSCAN via subsampled similarity queries |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Faster Differentially Private Samplers via Rényi Divergence Analysis of Discretized Langevin MCMC |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Faster Randomized Infeasible Interior Point Methods for Tall/Wide Linear Programs |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Faster Wasserstein Distance Estimation with the Sinkhorn Divergence |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Feature Importance Ranking for Deep Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Feature Shift Detection: Localizing Which Features Have Shifted via Conditional Distribution Tests |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| FedSplit: an algorithmic framework for fast federated optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Federated Accelerated Stochastic Gradient Descent |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Federated Bayesian Optimization via Thompson Sampling |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Federated Principal Component Analysis |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Few-Cost Salient Object Detection with Adversarial-Paced Learning |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Few-shot Image Generation with Elastic Weight Consolidation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Few-shot Visual Reasoning with Meta-Analogical Contrastive Learning |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Fewer is More: A Deep Graph Metric Learning Perspective Using Fewer Proxies |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Fictitious Play for Mean Field Games: Continuous Time Analysis and Applications |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Field-wise Learning for Multi-field Categorical Data |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Fighting Copycat Agents in Behavioral Cloning from Observation Histories |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Finding All $\epsilon$-Good Arms in Stochastic Bandits |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Finding Second-Order Stationary Points Efficiently in Smooth Nonconvex Linearly Constrained Optimization Problems |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Finding the Homology of Decision Boundaries with Active Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Fine-Grained Dynamic Head for Object Detection |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Finer Metagenomic Reconstruction via Biodiversity Optimization |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Finite Continuum-Armed Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Finite Versus Infinite Neural Networks: an Empirical Study |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Finite-Sample Analysis of Contractive Stochastic Approximation Using Smooth Convex Envelopes |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Finite-Time Analysis for Double Q-learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Finite-Time Analysis of Round-Robin Kullback-Leibler Upper Confidence Bounds for Optimal Adaptive Allocation with Multiple Plays and Markovian Rewards |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Firefly Neural Architecture Descent: a General Approach for Growing Neural Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| First Order Constrained Optimization in Policy Space |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| First-Order Methods for Large-Scale Market Equilibrium Computation |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| FixMatch: Simplifying Semi-Supervised Learning with Consistency and Confidence |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Fixed-Support Wasserstein Barycenters: Computational Hardness and Fast Algorithm |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| FleXOR: Trainable Fractional Quantization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Flexible mean field variational inference using mixtures of non-overlapping exponential families |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Flows for simultaneous manifold learning and density estimation |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Focus of Attention Improves Information Transfer in Visual Features |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Follow the Perturbed Leader: Optimism and Fast Parallel Algorithms for Smooth Minimax Games |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Forethought and Hindsight in Credit Assignment |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Forget About the LiDAR: Self-Supervised Depth Estimators with MED Probability Volumes |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Fourier Features Let Networks Learn High Frequency Functions in Low Dimensional Domains |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Fourier Sparse Leverage Scores and Approximate Kernel Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Fourier Spectrum Discrepancies in Deep Network Generated Images |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Fourier-transform-based attribution priors improve the interpretability and stability of deep learning models for genomics |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| FracTrain: Fractionally Squeezing Bit Savings Both Temporally and Spatially for Efficient DNN Training |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| From Boltzmann Machines to Neural Networks and Back Again |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| From Finite to Countable-Armed Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| From Predictions to Decisions: Using Lookahead Regularization |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| From Trees to Continuous Embeddings and Back: Hyperbolic Hierarchical Clustering |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| FrugalML: How to use ML Prediction APIs more accurately and cheaply |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Fully Convolutional Mesh Autoencoder using Efficient Spatially Varying Kernels |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Fully Dynamic Algorithm for Constrained Submodular Optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Functional Regularization for Representation Learning: A Unified Theoretical Perspective |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Funnel-Transformer: Filtering out Sequential Redundancy for Efficient Language Processing |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Further Analysis of Outlier Detection with Deep Generative Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| GAIT-prop: A biologically plausible learning rule derived from backpropagation of error |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| GAN Memory with No Forgetting |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| GANSpace: Discovering Interpretable GAN Controls |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| GCN meets GPU: Decoupling “When to Sample” from “How to Sample” |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| GCOMB: Learning Budget-constrained Combinatorial Algorithms over Billion-sized Graphs |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| GNNGuard: Defending Graph Neural Networks against Adversarial Attacks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| GOCor: Bringing Globally Optimized Correspondence Volumes into Your Neural Network |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| GPS-Net: Graph-based Photometric Stereo Network |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| GPU-Accelerated Primal Learning for Extremely Fast Large-Scale Classification |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| GRAF: Generative Radiance Fields for 3D-Aware Image Synthesis |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| GS-WGAN: A Gradient-Sanitized Approach for Learning Differentially Private Generators |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Gamma-Models: Generative Temporal Difference Learning for Infinite-Horizon Prediction |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Gaussian Gated Linear Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Gaussian Process Bandit Optimization of the Thermodynamic Variational Objective |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| General Control Functions for Causal Effect Estimation from IVs |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| General Transportability of Soft Interventions: Completeness Results |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Generalised Bayesian Filtering via Sequential Monte Carlo |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Generalization Bound of Gradient Descent for Non-Convex Metric Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Generalization bound of globally optimal non-convex neural network training: Transportation map estimation by infinite dimensional Langevin dynamics |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Generalization error in high-dimensional perceptrons: Approaching Bayes error with convex optimization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Generalized Boosting |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Generalized Focal Loss: Learning Qualified and Distributed Bounding Boxes for Dense Object Detection |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Generalized Hindsight for Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Generalized Independent Noise Condition for Estimating Latent Variable Causal Graphs |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Generalized Leverage Score Sampling for Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Generating Adjacency-Constrained Subgoals in Hierarchical Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Generating Correct Answers for Progressive Matrices Intelligence Tests |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Generative 3D Part Assembly via Dynamic Graph Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Generative Neurosymbolic Machines |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Generative View Synthesis: From Single-view Semantics to Novel-view Images |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
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4 |
| Generative causal explanations of black-box classifiers |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Geo-PIFu: Geometry and Pixel Aligned Implicit Functions for Single-view Human Reconstruction |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Geometric All-way Boolean Tensor Decomposition |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Geometric Dataset Distances via Optimal Transport |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Geometric Exploration for Online Control |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Gibbs Sampling with People |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Glance and Focus: a Dynamic Approach to Reducing Spatial Redundancy in Image Classification |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
4 |
| Global Convergence and Variance Reduction for a Class of Nonconvex-Nonconcave Minimax Problems |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Global Convergence of Deep Networks with One Wide Layer Followed by Pyramidal Topology |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Glow-TTS: A Generative Flow for Text-to-Speech via Monotonic Alignment Search |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Glyph: Fast and Accurately Training Deep Neural Networks on Encrypted Data |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Goal-directed Generation of Discrete Structures with Conditional Generative Models |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| GradAug: A New Regularization Method for Deep Neural Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Gradient Boosted Normalizing Flows |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Gradient Estimation with Stochastic Softmax Tricks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Gradient Regularized V-Learning for Dynamic Treatment Regimes |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Gradient Surgery for Multi-Task Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Gradient-EM Bayesian Meta-Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Graduated Assignment for Joint Multi-Graph Matching and Clustering with Application to Unsupervised Graph Matching Network Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| GramGAN: Deep 3D Texture Synthesis From 2D Exemplars |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Graph Contrastive Learning with Augmentations |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Graph Cross Networks with Vertex Infomax Pooling |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Graph Geometry Interaction Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Graph Information Bottleneck |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Graph Meta Learning via Local Subgraphs |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Graph Policy Network for Transferable Active Learning on Graphs |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Graph Random Neural Networks for Semi-Supervised Learning on Graphs |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Graph Stochastic Neural Networks for Semi-supervised Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Graphon Neural Networks and the Transferability of Graph Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Grasp Proposal Networks: An End-to-End Solution for Visual Learning of Robotic Grasps |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Greedy Optimization Provably Wins the Lottery: Logarithmic Number of Winning Tickets is Enough |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Greedy inference with structure-exploiting lazy maps |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| GreedyFool: Distortion-Aware Sparse Adversarial Attack |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Group Contextual Encoding for 3D Point Clouds |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Group Knowledge Transfer: Federated Learning of Large CNNs at the Edge |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Group-Fair Online Allocation in Continuous Time |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Guided Adversarial Attack for Evaluating and Enhancing Adversarial Defenses |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Guiding Deep Molecular Optimization with Genetic Exploration |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| H-Mem: Harnessing synaptic plasticity with Hebbian Memory Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| HAWQ-V2: Hessian Aware trace-Weighted Quantization of Neural Networks |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| HM-ANN: Efficient Billion-Point Nearest Neighbor Search on Heterogeneous Memory |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| HOI Analysis: Integrating and Decomposing Human-Object Interaction |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| HRN: A Holistic Approach to One Class Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| HYDRA: Pruning Adversarially Robust Neural Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Hamiltonian Monte Carlo using an adjoint-differentiated Laplace approximation: Bayesian inference for latent Gaussian models and beyond |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Handling Missing Data with Graph Representation Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Hard Example Generation by Texture Synthesis for Cross-domain Shape Similarity Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Hard Negative Mixing for Contrastive Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Hard Shape-Constrained Kernel Machines |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Hardness of Learning Neural Networks with Natural Weights |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Hausdorff Dimension, Heavy Tails, and Generalization in Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Heavy-tailed Representations, Text Polarity Classification & Data Augmentation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Hedging in games: Faster convergence of external and swap regrets |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Heuristic Domain Adaptation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| HiFi-GAN: Generative Adversarial Networks for Efficient and High Fidelity Speech Synthesis |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| HiPPO: Recurrent Memory with Optimal Polynomial Projections |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Hierarchical Gaussian Process Priors for Bayesian Neural Network Weights |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Hierarchical Granularity Transfer Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Hierarchical Neural Architecture Search for Deep Stereo Matching |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Hierarchical Patch VAE-GAN: Generating Diverse Videos from a Single Sample |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Hierarchical Poset Decoding for Compositional Generalization in Language |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Hierarchical Quantized Autoencoders |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Hierarchical nucleation in deep neural networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Hierarchically Organized Latent Modules for Exploratory Search in Morphogenetic Systems |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| High-Dimensional Bayesian Optimization via Nested Riemannian Manifolds |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| High-Dimensional Contextual Policy Search with Unknown Context Rewards using Bayesian Optimization |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| High-Dimensional Sparse Linear Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| High-Fidelity Generative Image Compression |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| High-Throughput Synchronous Deep RL |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| High-contrast “gaudy” images improve the training of deep neural network models of visual cortex |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| High-recall causal discovery for autocorrelated time series with latent confounders |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Higher-Order Certification For Randomized Smoothing |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Higher-Order Spectral Clustering of Directed Graphs |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Hitting the High Notes: Subset Selection for Maximizing Expected Order Statistics |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Hold me tight! Influence of discriminative features on deep network boundaries |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| How Can I Explain This to You? An Empirical Study of Deep Neural Network Explanation Methods |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| How Robust are the Estimated Effects of Nonpharmaceutical Interventions against COVID-19? |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| How do fair decisions fare in long-term qualification? |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| How does This Interaction Affect Me? Interpretable Attribution for Feature Interactions |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| How does Weight Correlation Affect Generalisation Ability of Deep Neural Networks? |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| How hard is to distinguish graphs with graph neural networks? |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| How many samples is a good initial point worth in Low-rank Matrix Recovery? |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| How to Characterize The Landscape of Overparameterized Convolutional Neural Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| How to Learn a Useful Critic? Model-based Action-Gradient-Estimator Policy Optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Human Parsing Based Texture Transfer from Single Image to 3D Human via Cross-View Consistency |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| HyNet: Learning Local Descriptor with Hybrid Similarity Measure and Triplet Loss |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Hybrid Models for Learning to Branch |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Hybrid Variance-Reduced SGD Algorithms For Minimax Problems with Nonconvex-Linear Function |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Hyperparameter Ensembles for Robustness and Uncertainty Quantification |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Hypersolvers: Toward Fast Continuous-Depth Models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| ICAM: Interpretable Classification via Disentangled Representations and Feature Attribution Mapping |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| ICE-BeeM: Identifiable Conditional Energy-Based Deep Models Based on Nonlinear ICA |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| ICNet: Intra-saliency Correlation Network for Co-Saliency Detection |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| IDEAL: Inexact DEcentralized Accelerated Augmented Lagrangian Method |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| ISTA-NAS: Efficient and Consistent Neural Architecture Search by Sparse Coding |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Identifying Causal-Effect Inference Failure with Uncertainty-Aware Models |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Identifying Learning Rules From Neural Network Observables |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Identifying Mislabeled Data using the Area Under the Margin Ranking |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Identifying signal and noise structure in neural population activity with Gaussian process factor models |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| ImpatientCapsAndRuns: Approximately Optimal Algorithm Configuration from an Infinite Pool |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Implicit Bias in Deep Linear Classification: Initialization Scale vs Training Accuracy |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Implicit Distributional Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Implicit Graph Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Implicit Neural Representations with Periodic Activation Functions |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Implicit Rank-Minimizing Autoencoder |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Implicit Regularization and Convergence for Weight Normalization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Implicit Regularization in Deep Learning May Not Be Explainable by Norms |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Impossibility Results for Grammar-Compressed Linear Algebra |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Improved Algorithms for Convex-Concave Minimax Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Improved Algorithms for Online Submodular Maximization via First-order Regret Bounds |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Improved Analysis of Clipping Algorithms for Non-convex Optimization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Improved Guarantees for k-means++ and k-means++ Parallel |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Improved Sample Complexity for Incremental Autonomous Exploration in MDPs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Improved Schemes for Episodic Memory-based Lifelong Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Improved Techniques for Training Score-Based Generative Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Improved Variational Bayesian Phylogenetic Inference with Normalizing Flows |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Improved guarantees and a multiple-descent curve for Column Subset Selection and the Nystrom method |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Improving Auto-Augment via Augmentation-Wise Weight Sharing |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Improving GAN Training with Probability Ratio Clipping and Sample Reweighting |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Improving Generalization in Reinforcement Learning with Mixture Regularization |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Improving Inference for Neural Image Compression |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Improving Local Identifiability in Probabilistic Box Embeddings |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Improving Natural Language Processing Tasks with Human Gaze-Guided Neural Attention |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Improving Neural Network Training in Low Dimensional Random Bases |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Improving Online Rent-or-Buy Algorithms with Sequential Decision Making and ML Predictions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Improving Policy-Constrained Kidney Exchange via Pre-Screening |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Improving Sample Complexity Bounds for (Natural) Actor-Critic Algorithms |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Improving Sparse Vector Technique with Renyi Differential Privacy |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Improving model calibration with accuracy versus uncertainty optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Improving robustness against common corruptions by covariate shift adaptation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| In search of robust measures of generalization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Incorporating BERT into Parallel Sequence Decoding with Adapters |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Incorporating Interpretable Output Constraints in Bayesian Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Incorporating Pragmatic Reasoning Communication into Emergent Language |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Independent Policy Gradient Methods for Competitive Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Inductive Quantum Embedding |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Inference Stage Optimization for Cross-scenario 3D Human Pose Estimation |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Inference for Batched Bandits |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Inferring learning rules from animal decision-making |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Influence-Augmented Online Planning for Complex Environments |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Information Maximization for Few-Shot Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Information Theoretic Counterfactual Learning from Missing-Not-At-Random Feedback |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Information Theoretic Regret Bounds for Online Nonlinear Control |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Information theoretic limits of learning a sparse rule |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Information-theoretic Task Selection for Meta-Reinforcement Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Input-Aware Dynamic Backdoor Attack |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Instance Based Approximations to Profile Maximum Likelihood |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Instance Selection for GANs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Instance-based Generalization in Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Instance-optimality in differential privacy via approximate inverse sensitivity mechanisms |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Instance-wise Feature Grouping |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Instead of Rewriting Foreign Code for Machine Learning, Automatically Synthesize Fast Gradients |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Interferobot: aligning an optical interferometer by a reinforcement learning agent |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Interior Point Solving for LP-based prediction+optimisation |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Interpolation Technique to Speed Up Gradients Propagation in Neural ODEs |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Interpretable Sequence Learning for Covid-19 Forecasting |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Interpretable and Personalized Apprenticeship Scheduling: Learning Interpretable Scheduling Policies from Heterogeneous User Demonstrations |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Interpretable multi-timescale models for predicting fMRI responses to continuous natural speech |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Interstellar: Searching Recurrent Architecture for Knowledge Graph Embedding |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| Interventional Few-Shot Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Intra Order-preserving Functions for Calibration of Multi-Class Neural Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Intra-Processing Methods for Debiasing Neural Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Introducing Routing Uncertainty in Capsule Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Inverse Learning of Symmetries |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Inverse Rational Control with Partially Observable Continuous Nonlinear Dynamics |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Inverse Reinforcement Learning from a Gradient-based Learner |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Invertible Gaussian Reparameterization: Revisiting the Gumbel-Softmax |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Inverting Gradients - How easy is it to break privacy in federated learning? |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Investigating Gender Bias in Language Models Using Causal Mediation Analysis |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Is Long Horizon RL More Difficult Than Short Horizon RL? |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Is Plug-in Solver Sample-Efficient for Feature-based Reinforcement Learning? |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Is normalization indispensable for training deep neural network? |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Iterative Deep Graph Learning for Graph Neural Networks: Better and Robust Node Embeddings |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| JAX MD: A Framework for Differentiable Physics |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Joint Contrastive Learning with Infinite Possibilities |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Joint Policy Search for Multi-agent Collaboration with Imperfect Information |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Joints in Random Forests |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Just Pick a Sign: Optimizing Deep Multitask Models with Gradient Sign Dropout |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| KFC: A Scalable Approximation Algorithm for $k$−center Fair Clustering |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Kalman Filtering Attention for User Behavior Modeling in CTR Prediction |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Kernel Alignment Risk Estimator: Risk Prediction from Training Data |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Kernel Based Progressive Distillation for Adder Neural Networks |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Kernel Methods Through the Roof: Handling Billions of Points Efficiently |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Kernelized information bottleneck leads to biologically plausible 3-factor Hebbian learning in deep networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Knowledge Augmented Deep Neural Networks for Joint Facial Expression and Action Unit Recognition |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Knowledge Distillation in Wide Neural Networks: Risk Bound, Data Efficiency and Imperfect Teacher |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Knowledge Transfer in Multi-Task Deep Reinforcement Learning for Continuous Control |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| LAPAR: Linearly-Assembled Pixel-Adaptive Regression Network for Single Image Super-resolution and Beyond |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Label-Aware Neural Tangent Kernel: Toward Better Generalization and Local Elasticity |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Labelling unlabelled videos from scratch with multi-modal self-supervision |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Lamina-specific neuronal properties promote robust, stable signal propagation in feedforward networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Language Models are Few-Shot Learners |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Language Through a Prism: A Spectral Approach for Multiscale Language Representations |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Language and Visual Entity Relationship Graph for Agent Navigation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Language as a Cognitive Tool to Imagine Goals in Curiosity Driven Exploration |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Language-Conditioned Imitation Learning for Robot Manipulation Tasks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Large-Scale Adversarial Training for Vision-and-Language Representation Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Large-Scale Methods for Distributionally Robust Optimization |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Latent Bandits Revisited |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Latent Dynamic Factor Analysis of High-Dimensional Neural Recordings |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Latent Template Induction with Gumbel-CRFs |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Latent World Models For Intrinsically Motivated Exploration |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Leap-Of-Thought: Teaching Pre-Trained Models to Systematically Reason Over Implicit Knowledge |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Learnability with Indirect Supervision Signals |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Learning About Objects by Learning to Interact with Them |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Affordance Landscapes for Interaction Exploration in 3D Environments |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning Agent Representations for Ice Hockey |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
2 |
| Learning Augmented Energy Minimization via Speed Scaling |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning Black-Box Attackers with Transferable Priors and Query Feedback |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| Learning Bounds for Risk-sensitive Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Causal Effects via Weighted Empirical Risk Minimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning Certified Individually Fair Representations |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning Composable Energy Surrogates for PDE Order Reduction |
❌ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
3 |
| Learning Compositional Rules via Neural Program Synthesis |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Continuous System Dynamics from Irregularly-Sampled Partial Observations |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Learning Deep Attribution Priors Based On Prior Knowledge |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning Deformable Tetrahedral Meshes for 3D Reconstruction |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Learning Differentiable Programs with Admissible Neural Heuristics |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning Differential Equations that are Easy to Solve |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Discrete Energy-based Models via Auxiliary-variable Local Exploration |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning Disentangled Representations and Group Structure of Dynamical Environments |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Learning Disentangled Representations of Videos with Missing Data |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Diverse and Discriminative Representations via the Principle of Maximal Coding Rate Reduction |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Dynamic Belief Graphs to Generalize on Text-Based Games |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Learning Feature Sparse Principal Subspace |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Learning Global Transparent Models consistent with Local Contrastive Explanations |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning Graph Structure With A Finite-State Automaton Layer |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Guidance Rewards with Trajectory-space Smoothing |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning Implicit Credit Assignment for Cooperative Multi-Agent Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Learning Implicit Functions for Topology-Varying Dense 3D Shape Correspondence |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning Individually Inferred Communication for Multi-Agent Cooperation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Learning Invariances in Neural Networks from Training Data |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning Invariants through Soft Unification |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning Kernel Tests Without Data Splitting |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning Latent Space Energy-Based Prior Model |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Learning Linear Programs from Optimal Decisions |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Learning Loss for Test-Time Augmentation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Learning Manifold Implicitly via Explicit Heat-Kernel Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Multi-Agent Communication through Structured Attentive Reasoning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Multi-Agent Coordination for Enhancing Target Coverage in Directional Sensor Networks |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Mutational Semantics |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning Object-Centric Representations of Multi-Object Scenes from Multiple Views |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning Optimal Representations with the Decodable Information Bottleneck |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Parities with Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Physical Constraints with Neural Projections |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
2 |
| Learning Physical Graph Representations from Visual Scenes |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
4 |
| Learning Representations from Audio-Visual Spatial Alignment |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Restricted Boltzmann Machines with Sparse Latent Variables |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning Retrospective Knowledge with Reverse Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Rich Rankings |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning Robust Decision Policies from Observational Data |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning Search Space Partition for Black-box Optimization using Monte Carlo Tree Search |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Semantic-aware Normalization for Generative Adversarial Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning Some Popular Gaussian Graphical Models without Condition Number Bounds |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Sparse Prototypes for Text Generation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning Strategic Network Emergence Games |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Learning Strategy-Aware Linear Classifiers |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Structured Distributions From Untrusted Batches: Faster and Simpler |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Learning Utilities and Equilibria in Non-Truthful Auctions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Learning abstract structure for drawing by efficient motor program induction |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Learning by Minimizing the Sum of Ranked Range |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning compositional functions via multiplicative weight updates |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning discrete distributions with infinite support |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Learning discrete distributions: user vs item-level privacy |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning efficient task-dependent representations with synaptic plasticity |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning from Aggregate Observations |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning from Failure: De-biasing Classifier from Biased Classifier |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Learning from Label Proportions: A Mutual Contamination Framework |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning from Mixtures of Private and Public Populations |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning from Positive and Unlabeled Data with Arbitrary Positive Shift |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning identifiable and interpretable latent models of high-dimensional neural activity using pi-VAE |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning of Discrete Graphical Models with Neural Networks |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning outside the Black-Box: The pursuit of interpretable models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Learning sparse codes from compressed representations with biologically plausible local wiring constraints |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning the Geometry of Wave-Based Imaging |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning the Linear Quadratic Regulator from Nonlinear Observations |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning to Adapt to Evolving Domains |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning to Approximate a Bregman Divergence |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning to Decode: Reinforcement Learning for Decoding of Sparse Graph-Based Channel Codes |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning to Detect Objects with a 1 Megapixel Event Camera |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning to Dispatch for Job Shop Scheduling via Deep Reinforcement Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning to Execute Programs with Instruction Pointer Attention Graph Neural Networks |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Learning to Extrapolate Knowledge: Transductive Few-shot Out-of-Graph Link Prediction |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning to Incentivize Other Learning Agents |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning to Learn Variational Semantic Memory |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning to Learn with Feedback and Local Plasticity |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning to Mutate with Hypergradient Guided Population |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning to Orient Surfaces by Self-supervised Spherical CNNs |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning to Play No-Press Diplomacy with Best Response Policy Iteration |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning to Play Sequential Games versus Unknown Opponents |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning to Prove Theorems by Learning to Generate Theorems |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning to Select Best Forecast Tasks for Clinical Outcome Prediction |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning to Utilize Shaping Rewards: A New Approach of Reward Shaping |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning to search efficiently for causally near-optimal treatments |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning to solve TV regularised problems with unrolled algorithms |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning to summarize with human feedback |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning under Model Misspecification: Applications to Variational and Ensemble methods |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Learning with Differentiable Pertubed Optimizers |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning with Operator-valued Kernels in Reproducing Kernel Krein Spaces |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
6 |
| Learning with Optimized Random Features: Exponential Speedup by Quantum Machine Learning without Sparsity and Low-Rank Assumptions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Least Squares Regression with Markovian Data: Fundamental Limits and Algorithms |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Leverage the Average: an Analysis of KL Regularization in Reinforcement Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Leveraging Predictions in Smoothed Online Convex Optimization via Gradient-based Algorithms |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Liberty or Depth: Deep Bayesian Neural Nets Do Not Need Complex Weight Posterior Approximations |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Lifelong Policy Gradient Learning of Factored Policies for Faster Training Without Forgetting |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Lightweight Generative Adversarial Networks for Text-Guided Image Manipulation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Likelihood Regret: An Out-of-Distribution Detection Score For Variational Auto-encoder |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Limits on Testing Structural Changes in Ising Models |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
2 |
| Limits to Depth Efficiencies of Self-Attention |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
2 |
| Linear Disentangled Representations and Unsupervised Action Estimation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Linear Dynamical Systems as a Core Computational Primitive |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Linear Time Sinkhorn Divergences using Positive Features |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Linear-Sample Learning of Low-Rank Distributions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Linearly Converging Error Compensated SGD |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Lipschitz Bounds and Provably Robust Training by Laplacian Smoothing |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Lipschitz-Certifiable Training with a Tight Outer Bound |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| List-Decodable Mean Estimation via Iterative Multi-Filtering |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Listening to Sounds of Silence for Speech Denoising |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| LoCo: Local Contrastive Representation Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Locally Differentially Private (Contextual) Bandits Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Locally private non-asymptotic testing of discrete distributions is faster using interactive mechanisms |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Locally-Adaptive Nonparametric Online Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Log-Likelihood Ratio Minimizing Flows: Towards Robust and Quantifiable Neural Distribution Alignment |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Logarithmic Pruning is All You Need |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Logarithmic Regret Bound in Partially Observable Linear Dynamical Systems |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Long-Horizon Visual Planning with Goal-Conditioned Hierarchical Predictors |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| Long-Tailed Classification by Keeping the Good and Removing the Bad Momentum Causal Effect |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Look-ahead Meta Learning for Continual Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| LoopReg: Self-supervised Learning of Implicit Surface Correspondences, Pose and Shape for 3D Human Mesh Registration |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Low Distortion Block-Resampling with Spatially Stochastic Networks |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Lower Bounds and Optimal Algorithms for Personalized Federated Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| MATE: Plugging in Model Awareness to Task Embedding for Meta Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| MCUNet: Tiny Deep Learning on IoT Devices |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| MDP Homomorphic Networks: Group Symmetries in Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| MESA: Boost Ensemble Imbalanced Learning with MEta-SAmpler |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| MMA Regularization: Decorrelating Weights of Neural Networks by Maximizing the Minimal Angles |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| MOPO: Model-based Offline Policy Optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| MOReL: Model-Based Offline Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| MPNet: Masked and Permuted Pre-training for Language Understanding |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| MRI Banding Removal via Adversarial Training |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Make One-Shot Video Object Segmentation Efficient Again |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Making Non-Stochastic Control (Almost) as Easy as Stochastic |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Manifold GPLVMs for discovering non-Euclidean latent structure in neural data |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Manifold structure in graph embeddings |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Marginal Utility for Planning in Continuous or Large Discrete Action Spaces |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Margins are Insufficient for Explaining Gradient Boosting |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Markovian Score Climbing: Variational Inference with KL(p||q) |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Matrix Completion with Hierarchical Graph Side Information |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Matrix Completion with Quantified Uncertainty through Low Rank Gaussian Copula |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Matrix Inference and Estimation in Multi-Layer Models |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Matérn Gaussian Processes on Riemannian Manifolds |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Maximum-Entropy Adversarial Data Augmentation for Improved Generalization and Robustness |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Measuring Robustness to Natural Distribution Shifts in Image Classification |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Measuring Systematic Generalization in Neural Proof Generation with Transformers |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Memory Based Trajectory-conditioned Policies for Learning from Sparse Rewards |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Memory-Efficient Learning of Stable Linear Dynamical Systems for Prediction and Control |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| MeshSDF: Differentiable Iso-Surface Extraction |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Meta-Consolidation for Continual Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Meta-Gradient Reinforcement Learning with an Objective Discovered Online |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Meta-Learning Requires Meta-Augmentation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Meta-Learning Stationary Stochastic Process Prediction with Convolutional Neural Processes |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Meta-Learning through Hebbian Plasticity in Random Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Meta-Learning with Adaptive Hyperparameters |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Meta-Neighborhoods |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Meta-learning from Tasks with Heterogeneous Attribute Spaces |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Meta-trained agents implement Bayes-optimal agents |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| MetaPerturb: Transferable Regularizer for Heterogeneous Tasks and Architectures |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| MetaPoison: Practical General-purpose Clean-label Data Poisoning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| MetaSDF: Meta-Learning Signed Distance Functions |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Metric-Free Individual Fairness in Online Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| MinMax Methods for Optimal Transport and Beyond: Regularization, Approximation and Numerics |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| MiniLM: Deep Self-Attention Distillation for Task-Agnostic Compression of Pre-Trained Transformers |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Minibatch Stochastic Approximate Proximal Point Methods |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Minibatch vs Local SGD for Heterogeneous Distributed Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Minimax Bounds for Generalized Linear Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Minimax Classification with 0-1 Loss and Performance Guarantees |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Minimax Dynamics of Optimally Balanced Spiking Networks of Excitatory and Inhibitory Neurons |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
3 |
| Minimax Estimation of Conditional Moment Models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Minimax Lower Bounds for Transfer Learning with Linear and One-hidden Layer Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Minimax Optimal Nonparametric Estimation of Heterogeneous Treatment Effects |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Minimax Regret of Switching-Constrained Online Convex Optimization: No Phase Transition |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Minimax Value Interval for Off-Policy Evaluation and Policy Optimization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Mitigating Forgetting in Online Continual Learning via Instance-Aware Parameterization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Mitigating Manipulation in Peer Review via Randomized Reviewer Assignments |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Mix and Match: An Optimistic Tree-Search Approach for Learning Models from Mixture Distributions |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Mixed Hamiltonian Monte Carlo for Mixed Discrete and Continuous Variables |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Model Agnostic Multilevel Explanations |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Model Class Reliance for Random Forests |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Model Fusion via Optimal Transport |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Model Interpretability through the lens of Computational Complexity |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Model Inversion Networks for Model-Based Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Model Rubik’s Cube: Twisting Resolution, Depth and Width for TinyNets |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Model Selection for Production System via Automated Online Experiments |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Model Selection in Contextual Stochastic Bandit Problems |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Model-based Adversarial Meta-Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Model-based Policy Optimization with Unsupervised Model Adaptation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Model-based Reinforcement Learning for Semi-Markov Decision Processes with Neural ODEs |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Modeling Continuous Stochastic Processes with Dynamic Normalizing Flows |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Modeling Noisy Annotations for Crowd Counting |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Modeling Shared responses in Neuroimaging Studies through MultiView ICA |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Modeling Task Effects on Meaning Representation in the Brain via Zero-Shot MEG Prediction |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Modeling and Optimization Trade-off in Meta-learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Modern Hopfield Networks and Attention for Immune Repertoire Classification |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Modular Meta-Learning with Shrinkage |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| MomentumRNN: Integrating Momentum into Recurrent Neural Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Monotone operator equilibrium networks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Most ReLU Networks Suffer from $\ell^2$ Adversarial Perturbations |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Movement Pruning: Adaptive Sparsity by Fine-Tuning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| MuSCLE: Multi Sweep Compression of LiDAR using Deep Entropy Models |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Multi-Fidelity Bayesian Optimization via Deep Neural Networks |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Multi-Plane Program Induction with 3D Box Priors |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Multi-Robot Collision Avoidance under Uncertainty with Probabilistic Safety Barrier Certificates |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Multi-Stage Influence Function |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Multi-Task Reinforcement Learning with Soft Modularization |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multi-Task Temporal Shift Attention Networks for On-Device Contactless Vitals Measurement |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Multi-agent Trajectory Prediction with Fuzzy Query Attention |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multi-agent active perception with prediction rewards |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multi-label Contrastive Predictive Coding |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Multi-label classification: do Hamming loss and subset accuracy really conflict with each other? |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Multi-task Additive Models for Robust Estimation and Automatic Structure Discovery |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Multi-task Batch Reinforcement Learning with Metric Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Multi-task Causal Learning with Gaussian Processes |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| MultiON: Benchmarking Semantic Map Memory using Multi-Object Navigation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multifaceted Uncertainty Estimation for Label-Efficient Deep Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Multilabel Classification by Hierarchical Partitioning and Data-dependent Grouping |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Multimodal Generative Learning Utilizing Jensen-Shannon-Divergence |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Multimodal Graph Networks for Compositional Generalization in Visual Question Answering |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Multiparameter Persistence Image for Topological Machine Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Multipole Graph Neural Operator for Parametric Partial Differential Equations |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Multiscale Deep Equilibrium Models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Multiview Neural Surface Reconstruction by Disentangling Geometry and Appearance |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Munchausen Reinforcement Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Mutual exclusivity as a challenge for deep neural networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Myersonian Regression |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| NVAE: A Deep Hierarchical Variational Autoencoder |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| NanoFlow: Scalable Normalizing Flows with Sublinear Parameter Complexity |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Natural Graph Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Natural Policy Gradient Primal-Dual Method for Constrained Markov Decision Processes |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Near-Optimal Comparison Based Clustering |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Near-Optimal Reinforcement Learning with Self-Play |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Near-Optimal SQ Lower Bounds for Agnostically Learning Halfspaces and ReLUs under Gaussian Marginals |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Network Diffusions via Neural Mean-Field Dynamics |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Network size and size of the weights in memorization with two-layers neural networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Network-to-Network Translation with Conditional Invertible Neural Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| NeuMiss networks: differentiable programming for supervised learning with missing values. |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Neural Anisotropy Directions |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Neural Architecture Generator Optimization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Neural Bridge Sampling for Evaluating Safety-Critical Autonomous Systems |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Neural Complexity Measures |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Neural Controlled Differential Equations for Irregular Time Series |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Neural Dynamic Policies for End-to-End Sensorimotor Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Neural Execution Engines: Learning to Execute Subroutines |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Neural FFTs for Universal Texture Image Synthesis |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Neural Manifold Ordinary Differential Equations |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Neural Mesh Flow: 3D Manifold Mesh Generation via Diffeomorphic Flows |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Neural Message Passing for Multi-Relational Ordered and Recursive Hypergraphs |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Neural Methods for Point-wise Dependency Estimation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Neural Networks Fail to Learn Periodic Functions and How to Fix It |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Neural Networks Learning and Memorization with (almost) no Over-Parameterization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Neural Networks with Recurrent Generative Feedback |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Neural Networks with Small Weights and Depth-Separation Barriers |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Neural Non-Rigid Tracking |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Neural Path Features and Neural Path Kernel : Understanding the role of gates in deep learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Neural Power Units |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Neural Sparse Representation for Image Restoration |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Neural Sparse Voxel Fields |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Neural Star Domain as Primitive Representation |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Neural Topographic Factor Analysis for fMRI Data |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Neural Unsigned Distance Fields for Implicit Function Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Neural encoding with visual attention |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Neuron Merging: Compensating for Pruned Neurons |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Neuron Shapley: Discovering the Responsible Neurons |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Neuron-level Structured Pruning using Polarization Regularizer |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Neuronal Gaussian Process Regression |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Neurosymbolic Reinforcement Learning with Formally Verified Exploration |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Neurosymbolic Transformers for Multi-Agent Communication |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Neutralizing Self-Selection Bias in Sampling for Sortition |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Nimble: Lightweight and Parallel GPU Task Scheduling for Deep Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| No Subclass Left Behind: Fine-Grained Robustness in Coarse-Grained Classification Problems |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| No-Regret Learning Dynamics for Extensive-Form Correlated Equilibrium |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| No-Regret Learning and Mixed Nash Equilibria: They Do Not Mix |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| No-regret Learning in Price Competitions under Consumer Reference Effects |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Node Classification on Graphs with Few-Shot Novel Labels via Meta Transformed Network Embedding |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Node Embeddings and Exact Low-Rank Representations of Complex Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Noise-Contrastive Estimation for Multivariate Point Processes |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Noise2Same: Optimizing A Self-Supervised Bound for Image Denoising |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Non-Convex SGD Learns Halfspaces with Adversarial Label Noise |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Non-Crossing Quantile Regression for Distributional Reinforcement Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Non-Euclidean Universal Approximation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Non-Stochastic Control with Bandit Feedback |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Non-parametric Models for Non-negative Functions |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Non-reversible Gaussian processes for identifying latent dynamical structure in neural data |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Nonasymptotic Guarantees for Spiked Matrix Recovery with Generative Priors |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Nonconvex Sparse Graph Learning under Laplacian Constrained Graphical Model |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Normalizing Kalman Filters for Multivariate Time Series Analysis |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Not All Unlabeled Data are Equal: Learning to Weight Data in Semi-supervised Learning |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Novelty Search in Representational Space for Sample Efficient Exploration |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Numerically Solving Parametric Families of High-Dimensional Kolmogorov Partial Differential Equations via Deep Learning |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| O(n) Connections are Expressive Enough: Universal Approximability of Sparse Transformers |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| OOD-MAML: Meta-Learning for Few-Shot Out-of-Distribution Detection and Classification |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| OTLDA: A Geometry-aware Optimal Transport Approach for Topic Modeling |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Object Goal Navigation using Goal-Oriented Semantic Exploration |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Object-Centric Learning with Slot Attention |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Ode to an ODE |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Off-Policy Evaluation and Learning for External Validity under a Covariate Shift |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Off-Policy Evaluation via the Regularized Lagrangian |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Off-Policy Imitation Learning from Observations |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Off-Policy Interval Estimation with Lipschitz Value Iteration |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Off-policy Policy Evaluation For Sequential Decisions Under Unobserved Confounding |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Offline Imitation Learning with a Misspecified Simulator |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On 1/n neural representation and robustness |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On Adaptive Attacks to Adversarial Example Defenses |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On Adaptive Distance Estimation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| On Completeness-aware Concept-Based Explanations in Deep Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| On Convergence and Generalization of Dropout Training |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On Convergence of Nearest Neighbor Classifiers over Feature Transformations |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On Correctness of Automatic Differentiation for Non-Differentiable Functions |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On Efficiency in Hierarchical Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On Infinite-Width Hypernetworks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On Learning Ising Models under Huber's Contamination Model |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| On Numerosity of Deep Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| On Power Laws in Deep Ensembles |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| On Regret with Multiple Best Arms |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On Reward-Free Reinforcement Learning with Linear Function Approximation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On Second Order Behaviour in Augmented Neural ODEs |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On Testing of Samplers |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| On Uniform Convergence and Low-Norm Interpolation Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On Warm-Starting Neural Network Training |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| On ranking via sorting by estimated expected utility |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| On the Almost Sure Convergence of Stochastic Gradient Descent in Non-Convex Problems |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On the Convergence of Smooth Regularized Approximate Value Iteration Schemes |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Equivalence between Online and Private Learnability beyond Binary Classification |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| On the Ergodicity, Bias and Asymptotic Normality of Randomized Midpoint Sampling Method |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Error Resistance of Hinge-Loss Minimization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Expressiveness of Approximate Inference in Bayesian Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Loss Landscape of Adversarial Training: Identifying Challenges and How to Overcome Them |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Modularity of Hypernetworks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On the Optimal Weighted $\ell_2$ Regularization in Overparameterized Linear Regression |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| On the Power of Louvain in the Stochastic Block Model |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| On the Role of Sparsity and DAG Constraints for Learning Linear DAGs |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| On the Similarity between the Laplace and Neural Tangent Kernels |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| On the Stability and Convergence of Robust Adversarial Reinforcement Learning: A Case Study on Linear Quadratic Systems |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| On the Theory of Transfer Learning: The Importance of Task Diversity |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Tightness of Semidefinite Relaxations for Certifying Robustness to Adversarial Examples |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| On the Trade-off between Adversarial and Backdoor Robustness |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| On the Value of Out-of-Distribution Testing: An Example of Goodhart's Law |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| On the distance between two neural networks and the stability of learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| On the equivalence of molecular graph convolution and molecular wave function with poor basis set |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
4 |
| On the linearity of large non-linear models: when and why the tangent kernel is constant |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the training dynamics of deep networks with $L_2$ regularization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On the universality of deep learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Once-for-All Adversarial Training: In-Situ Tradeoff between Robustness and Accuracy for Free |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| One Ring to Rule Them All: Certifiably Robust Geometric Perception with Outliers |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| One Solution is Not All You Need: Few-Shot Extrapolation via Structured MaxEnt RL |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| One-bit Supervision for Image Classification |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| One-sample Guided Object Representation Disassembling |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Online Adaptation for Consistent Mesh Reconstruction in the Wild |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Online Agnostic Boosting via Regret Minimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Online Algorithm for Unsupervised Sequential Selection with Contextual Information |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Online Algorithms for Multi-shop Ski Rental with Machine Learned Advice |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Online Bayesian Goal Inference for Boundedly Rational Planning Agents |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Online Bayesian Persuasion |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Online Convex Optimization Over Erdos-Renyi Random Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Online Decision Based Visual Tracking via Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Online Fast Adaptation and Knowledge Accumulation (OSAKA): a New Approach to Continual Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Online Influence Maximization under Linear Threshold Model |
✅ |
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1 |
| Online Learning in Contextual Bandits using Gated Linear Networks |
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4 |
| Online Learning with Primary and Secondary Losses |
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1 |
| Online Linear Optimization with Many Hints |
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1 |
| Online MAP Inference of Determinantal Point Processes |
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3 |
| Online Matrix Completion with Side Information |
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2 |
| Online Meta-Critic Learning for Off-Policy Actor-Critic Methods |
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4 |
| Online Multitask Learning with Long-Term Memory |
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1 |
| Online Neural Connectivity Estimation with Noisy Group Testing |
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4 |
| Online Non-Convex Optimization with Imperfect Feedback |
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1 |
| Online Optimization with Memory and Competitive Control |
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1 |
| Online Planning with Lookahead Policies |
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1 |
| Online Robust Regression via SGD on the l1 loss |
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1 |
| Online Sinkhorn: Optimal Transport distances from sample streams |
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3 |
| Online Structured Meta-learning |
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3 |
| Online learning with dynamics: A minimax perspective |
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0 |
| Open Graph Benchmark: Datasets for Machine Learning on Graphs |
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5 |
| Optimal Adaptive Electrode Selection to Maximize Simultaneously Recorded Neuron Yield |
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4 |
| Optimal Algorithms for Stochastic Multi-Armed Bandits with Heavy Tailed Rewards |
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2 |
| Optimal Approximation - Smoothness Tradeoffs for Soft-Max Functions |
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0 |
| Optimal Best-arm Identification in Linear Bandits |
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2 |
| Optimal Epoch Stochastic Gradient Descent Ascent Methods for Min-Max Optimization |
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1 |
| Optimal Iterative Sketching Methods with the Subsampled Randomized Hadamard Transform |
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2 |
| Optimal Learning from Verified Training Data |
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5 |
| Optimal Lottery Tickets via Subset Sum: Logarithmic Over-Parameterization is Sufficient |
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3 |
| Optimal Prediction of the Number of Unseen Species with Multiplicity |
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1 |
| Optimal Private Median Estimation under Minimal Distributional Assumptions |
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3 |
| Optimal Query Complexity of Secure Stochastic Convex Optimization |
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1 |
| Optimal Robustness-Consistency Trade-offs for Learning-Augmented Online Algorithms |
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1 |
| Optimal Variance Control of the Score-Function Gradient Estimator for Importance-Weighted Bounds |
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4 |
| Optimal and Practical Algorithms for Smooth and Strongly Convex Decentralized Optimization |
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3 |
| Optimal visual search based on a model of target detectability in natural images |
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5 |
| Optimally Deceiving a Learning Leader in Stackelberg Games |
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0 |
| Optimistic Dual Extrapolation for Coherent Non-monotone Variational Inequalities |
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1 |
| Optimization and Generalization Analysis of Transduction through Gradient Boosting and Application to Multi-scale Graph Neural Networks |
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3 |
| Optimization and Generalization of Shallow Neural Networks with Quadratic Activation Functions |
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1 |
| Optimizing Mode Connectivity via Neuron Alignment |
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6 |
| Optimizing Neural Networks via Koopman Operator Theory |
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3 |
| OrganITE: Optimal transplant donor organ offering using an individual treatment effect |
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2 |
| Organizing recurrent network dynamics by task-computation to enable continual learning |
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2 |
| Outlier Robust Mean Estimation with Subgaussian Rates via Stability |
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0 |
| Over-parameterized Adversarial Training: An Analysis Overcoming the Curse of Dimensionality |
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2 |
| Overfitting Can Be Harmless for Basis Pursuit, But Only to a Degree |
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1 |
| PAC-Bayes Analysis Beyond the Usual Bounds |
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0 |
| PAC-Bayes Learning Bounds for Sample-Dependent Priors |
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0 |
| PAC-Bayesian Bound for the Conditional Value at Risk |
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0 |
| PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning |
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2 |
| PEP: Parameter Ensembling by Perturbation |
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3 |
| PGM-Explainer: Probabilistic Graphical Model Explanations for Graph Neural Networks |
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5 |
| PIE-NET: Parametric Inference of Point Cloud Edges |
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3 |
| PLANS: Neuro-Symbolic Program Learning from Videos |
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4 |
| PLLay: Efficient Topological Layer based on Persistent Landscapes |
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5 |
| POLY-HOOT: Monte-Carlo Planning in Continuous Space MDPs with Non-Asymptotic Analysis |
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3 |
| POMDPs in Continuous Time and Discrete Spaces |
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1 |
| POMO: Policy Optimization with Multiple Optima for Reinforcement Learning |
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6 |
| PRANK: motion Prediction based on RANKing |
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4 |
| Parabolic Approximation Line Search for DNNs |
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5 |
| Parameterized Explainer for Graph Neural Network |
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3 |
| Parametric Instance Classification for Unsupervised Visual Feature learning |
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5 |
| Part-dependent Label Noise: Towards Instance-dependent Label Noise |
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6 |
| Partial Optimal Tranport with applications on Positive-Unlabeled Learning |
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4 |
| Partially View-aligned Clustering |
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5 |
| Passport-aware Normalization for Deep Model Protection |
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2 |
| Patch2Self: Denoising Diffusion MRI with Self-Supervised Learning |
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4 |
| Path Integral Based Convolution and Pooling for Graph Neural Networks |
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5 |
| Path Sample-Analytic Gradient Estimators for Stochastic Binary Networks |
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4 |
| Penalized Langevin dynamics with vanishing penalty for smooth and log-concave targets |
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0 |
| Permute-and-Flip: A new mechanism for differentially private selection |
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2 |
| Personalized Federated Learning with Moreau Envelopes |
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6 |
| Personalized Federated Learning with Theoretical Guarantees: A Model-Agnostic Meta-Learning Approach |
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3 |
| Perturbing Across the Feature Hierarchy to Improve Standard and Strict Blackbox Attack Transferability |
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3 |
| Phase retrieval in high dimensions: Statistical and computational phase transitions |
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0 |
| Pipeline PSRO: A Scalable Approach for Finding Approximate Nash Equilibria in Large Games |
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3 |
| Pixel-Level Cycle Association: A New Perspective for Domain Adaptive Semantic Segmentation |
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4 |
| PlanGAN: Model-based Planning With Sparse Rewards and Multiple Goals |
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3 |
| Planning in Markov Decision Processes with Gap-Dependent Sample Complexity |
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3 |
| Planning with General Objective Functions: Going Beyond Total Rewards |
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1 |
| Point process models for sequence detection in high-dimensional neural spike trains |
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5 |
| Pointer Graph Networks |
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4 |
| Policy Improvement via Imitation of Multiple Oracles |
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4 |
| Polynomial-Time Computation of Optimal Correlated Equilibria in Two-Player Extensive-Form Games with Public Chance Moves and Beyond |
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5 |
| Pontryagin Differentiable Programming: An End-to-End Learning and Control Framework |
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3 |
| Position-based Scaled Gradient for Model Quantization and Pruning |
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3 |
| Post-training Iterative Hierarchical Data Augmentation for Deep Networks |
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4 |
| Posterior Network: Uncertainty Estimation without OOD Samples via Density-Based Pseudo-Counts |
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4 |
| Posterior Re-calibration for Imbalanced Datasets |
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4 |
| Practical Low-Rank Communication Compression in Decentralized Deep Learning |
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4 |
| Practical No-box Adversarial Attacks against DNNs |
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4 |
| Practical Quasi-Newton Methods for Training Deep Neural Networks |
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5 |
| Pre-training via Paraphrasing |
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2 |
| Precise expressions for random projections: Low-rank approximation and randomized Newton |
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1 |
| Predicting Training Time Without Training |
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2 |
| Prediction with Corrupted Expert Advice |
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1 |
| Predictive Information Accelerates Learning in RL |
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4 |
| Predictive coding in balanced neural networks with noise, chaos and delays |
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1 |
| Predictive inference is free with the jackknife+-after-bootstrap |
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4 |
| Preference learning along multiple criteria: A game-theoretic perspective |
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1 |
| Preference-based Reinforcement Learning with Finite-Time Guarantees |
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2 |
| Primal Dual Interpretation of the Proximal Stochastic Gradient Langevin Algorithm |
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0 |
| Primal-Dual Mesh Convolutional Neural Networks |
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3 |
| Principal Neighbourhood Aggregation for Graph Nets |
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4 |
| Privacy Amplification via Random Check-Ins |
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1 |
| Private Identity Testing for High-Dimensional Distributions |
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1 |
| Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity |
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1 |
| Probabilistic Active Meta-Learning |
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2 |
| Probabilistic Circuits for Variational Inference in Discrete Graphical Models |
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3 |
| Probabilistic Fair Clustering |
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5 |
| Probabilistic Inference with Algebraic Constraints: Theoretical Limits and Practical Approximations |
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2 |
| Probabilistic Linear Solvers for Machine Learning |
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4 |
| Probabilistic Orientation Estimation with Matrix Fisher Distributions |
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4 |
| Probabilistic Time Series Forecasting with Shape and Temporal Diversity |
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4 |
| Probably Approximately Correct Constrained Learning |
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3 |
| Profile Entropy: A Fundamental Measure for the Learnability and Compressibility of Distributions |
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1 |
| Program Synthesis with Pragmatic Communication |
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2 |
| Projected Stein Variational Gradient Descent |
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5 |
| Projection Efficient Subgradient Method and Optimal Nonsmooth Frank-Wolfe Method |
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3 |
| Projection Robust Wasserstein Distance and Riemannian Optimization |
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3 |
| Promoting Coordination through Policy Regularization in Multi-Agent Deep Reinforcement Learning |
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3 |
| Promoting Stochasticity for Expressive Policies via a Simple and Efficient Regularization Method |
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4 |
| Prophet Attention: Predicting Attention with Future Attention |
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4 |
| Provable Online CP/PARAFAC Decomposition of a Structured Tensor via Dictionary Learning |
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3 |
| Provable Overlapping Community Detection in Weighted Graphs |
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3 |
| Provably Consistent Partial-Label Learning |
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4 |
| Provably Efficient Exploration for Reinforcement Learning Using Unsupervised Learning |
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2 |
| Provably Efficient Neural Estimation of Structural Equation Models: An Adversarial Approach |
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1 |
| Provably Efficient Neural GTD for Off-Policy Learning |
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2 |
| Provably Efficient Online Hyperparameter Optimization with Population-Based Bandits |
✅ |
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4 |
| Provably Efficient Reinforcement Learning with Kernel and Neural Function Approximations |
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1 |
| Provably Efficient Reward-Agnostic Navigation with Linear Value Iteration |
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❌ |
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1 |
| Provably Good Batch Off-Policy Reinforcement Learning Without Great Exploration |
✅ |
❌ |
✅ |
❌ |
❌ |
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✅ |
3 |
| Provably Robust Metric Learning |
✅ |
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✅ |
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✅ |
❌ |
❌ |
3 |
| Provably adaptive reinforcement learning in metric spaces |
✅ |
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1 |
| Proximal Mapping for Deep Regularization |
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4 |
| Proximity Operator of the Matrix Perspective Function and its Applications |
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❌ |
✅ |
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❌ |
3 |
| Pruning Filter in Filter |
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✅ |
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3 |
| Pruning neural networks without any data by iteratively conserving synaptic flow |
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✅ |
✅ |
❌ |
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3 |
| Pushing the Limits of Narrow Precision Inferencing at Cloud Scale with Microsoft Floating Point |
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✅ |
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✅ |
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3 |
| PyGlove: Symbolic Programming for Automated Machine Learning |
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✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Quantifying Learnability and Describability of Visual Concepts Emerging in Representation Learning |
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❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Quantifying the Empirical Wasserstein Distance to a Set of Measures: Beating the Curse of Dimensionality |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Quantile Propagation for Wasserstein-Approximate Gaussian Processes |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Quantitative Propagation of Chaos for SGD in Wide Neural Networks |
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❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Quantized Variational Inference |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| R-learning in actor-critic model offers a biologically relevant mechanism for sequential decision-making |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| RANet: Region Attention Network for Semantic Segmentation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| RATT: Recurrent Attention to Transient Tasks for Continual Image Captioning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| RD$^2$: Reward Decomposition with Representation Decomposition |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| RELATE: Physically Plausible Multi-Object Scene Synthesis Using Structured Latent Spaces |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| RL Unplugged: A Suite of Benchmarks for Offline Reinforcement Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| RNNPool: Efficient Non-linear Pooling for RAM Constrained Inference |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
5 |
| RSKDD-Net: Random Sample-based Keypoint Detector and Descriptor |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| RandAugment: Practical Automated Data Augmentation with a Reduced Search Space |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Random Reshuffling is Not Always Better |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Random Reshuffling: Simple Analysis with Vast Improvements |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Random Walk Graph Neural Networks |
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✅ |
✅ |
❌ |
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✅ |
3 |
| Randomized tests for high-dimensional regression: A more efficient and powerful solution |
✅ |
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❌ |
❌ |
✅ |
2 |
| Rankmax: An Adaptive Projection Alternative to the Softmax Function |
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✅ |
✅ |
❌ |
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✅ |
4 |
| Ratio Trace Formulation of Wasserstein Discriminant Analysis |
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✅ |
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3 |
| Rational neural networks |
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❌ |
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3 |
| Re-Examining Linear Embeddings for High-Dimensional Bayesian Optimization |
✅ |
✅ |
✅ |
❌ |
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3 |
| Real World Games Look Like Spinning Tops |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Reasoning about Uncertainties in Discrete-Time Dynamical Systems using Polynomial Forms. |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Reciprocal Adversarial Learning via Characteristic Functions |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Reconciling Modern Deep Learning with Traditional Optimization Analyses: The Intrinsic Learning Rate |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Reconsidering Generative Objectives For Counterfactual Reasoning |
✅ |
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✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Reconstructing Perceptive Images from Brain Activity by Shape-Semantic GAN |
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✅ |
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❌ |
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✅ |
3 |
| Recovery of sparse linear classifiers from mixture of responses |
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✅ |
❌ |
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2 |
| Recurrent Quantum Neural Networks |
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✅ |
✅ |
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3 |
| Recurrent Switching Dynamical Systems Models for Multiple Interacting Neural Populations |
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✅ |
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✅ |
❌ |
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✅ |
3 |
| Recursive Inference for Variational Autoencoders |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Reducing Adversarially Robust Learning to Non-Robust PAC Learning |
✅ |
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❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Refactoring Policy for Compositional Generalizability using Self-Supervised Object Proposals |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Regression with reject option and application to kNN |
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✅ |
✅ |
✅ |
❌ |
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✅ |
4 |
| Regret Bounds without Lipschitz Continuity: Online Learning with Relative-Lipschitz Losses |
✅ |
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❌ |
❌ |
❌ |
❌ |
1 |
| Regret in Online Recommendation Systems |
✅ |
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❌ |
1 |
| Regularized linear autoencoders recover the principal components, eventually |
✅ |
✅ |
✅ |
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3 |
| Regularizing Black-box Models for Improved Interpretability |
✅ |
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✅ |
❌ |
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5 |
| Regularizing Towards Permutation Invariance In Recurrent Models |
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✅ |
❌ |
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1 |
| Reinforced Molecular Optimization with Neighborhood-Controlled Grammars |
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✅ |
✅ |
❌ |
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3 |
| Reinforcement Learning for Control with Multiple Frequencies |
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✅ |
3 |
| Reinforcement Learning in Factored MDPs: Oracle-Efficient Algorithms and Tighter Regret Bounds for the Non-Episodic Setting |
✅ |
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3 |
| Reinforcement Learning with Augmented Data |
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3 |
| Reinforcement Learning with Combinatorial Actions: An Application to Vehicle Routing |
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✅ |
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✅ |
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3 |
| Reinforcement Learning with Feedback Graphs |
✅ |
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1 |
| Reinforcement Learning with General Value Function Approximation: Provably Efficient Approach via Bounded Eluder Dimension |
✅ |
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1 |
| Rel3D: A Minimally Contrastive Benchmark for Grounding Spatial Relations in 3D |
❌ |
✅ |
✅ |
✅ |
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4 |
| RelationNet++: Bridging Visual Representations for Object Detection via Transformer Decoder |
❌ |
✅ |
✅ |
✅ |
✅ |
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6 |
| Relative gradient optimization of the Jacobian term in unsupervised deep learning |
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5 |
| Reliable Graph Neural Networks via Robust Aggregation |
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✅ |
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5 |
| Removing Bias in Multi-modal Classifiers: Regularization by Maximizing Functional Entropies |
❌ |
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✅ |
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3 |
| RepPoints v2: Verification Meets Regression for Object Detection |
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5 |
| Reparameterizing Mirror Descent as Gradient Descent |
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❌ |
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0 |
| Replica-Exchange Nos\'e-Hoover Dynamics for Bayesian Learning on Large Datasets |
✅ |
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✅ |
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4 |
| Representation Learning for Integrating Multi-domain Outcomes to Optimize Individualized Treatment |
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❌ |
✅ |
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4 |
| Rescuing neural spike train models from bad MLE |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Reservoir Computing meets Recurrent Kernels and Structured Transforms |
✅ |
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✅ |
❌ |
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4 |
| Residual Distillation: Towards Portable Deep Neural Networks without Shortcuts |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Residual Force Control for Agile Human Behavior Imitation and Extended Motion Synthesis |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Restless-UCB, an Efficient and Low-complexity Algorithm for Online Restless Bandits |
✅ |
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✅ |
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✅ |
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4 |
| Restoring Negative Information in Few-Shot Object Detection |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Rethinking Importance Weighting for Deep Learning under Distribution Shift |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Rethinking Learnable Tree Filter for Generic Feature Transform |
✅ |
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✅ |
✅ |
❌ |
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✅ |
5 |
| Rethinking Pre-training and Self-training |
❌ |
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✅ |
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❌ |
✅ |
4 |
| Rethinking pooling in graph neural networks |
❌ |
✅ |
✅ |
✅ |
❌ |
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✅ |
4 |
| Rethinking the Value of Labels for Improving Class-Imbalanced Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| RetroXpert: Decompose Retrosynthesis Prediction Like A Chemist |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Reverse-engineering recurrent neural network solutions to a hierarchical inference task for mice |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
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5 |
| Revisiting Frank-Wolfe for Polytopes: Strict Complementarity and Sparsity |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Revisiting Parameter Sharing for Automatic Neural Channel Number Search |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Revisiting the Sample Complexity of Sparse Spectrum Approximation of Gaussian Processes |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Reward Propagation Using Graph Convolutional Networks |
✅ |
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✅ |
❌ |
✅ |
❌ |
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4 |
| Reward-rational (implicit) choice: A unifying formalism for reward learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
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0 |
| Rewriting History with Inverse RL: Hindsight Inference for Policy Improvement |
✅ |
✅ |
✅ |
❌ |
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3 |
| Ridge Rider: Finding Diverse Solutions by Following Eigenvectors of the Hessian |
✅ |
✅ |
✅ |
❌ |
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❌ |
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4 |
| Riemannian Continuous Normalizing Flows |
❌ |
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✅ |
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❌ |
✅ |
2 |
| Risk-Sensitive Reinforcement Learning: Near-Optimal Risk-Sample Tradeoff in Regret |
✅ |
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❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Robust Correction of Sampling Bias using Cumulative Distribution Functions |
✅ |
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✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Robust Deep Reinforcement Learning against Adversarial Perturbations on State Observations |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Robust Density Estimation under Besov IPM Losses |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Robust Disentanglement of a Few Factors at a Time using rPU-VAE |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Robust Federated Learning: The Case of Affine Distribution Shifts |
✅ |
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✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Robust Gaussian Covariance Estimation in Nearly-Matrix Multiplication Time |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Robust Meta-learning for Mixed Linear Regression with Small Batches |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Robust Multi-Agent Reinforcement Learning with Model Uncertainty |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Robust Multi-Object Matching via Iterative Reweighting of the Graph Connection Laplacian |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Robust Optimal Transport with Applications in Generative Modeling and Domain Adaptation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Robust Optimization for Fairness with Noisy Protected Groups |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Robust Persistence Diagrams using Reproducing Kernels |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Robust Pre-Training by Adversarial Contrastive Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Robust Quantization: One Model to Rule Them All |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Robust Recovery via Implicit Bias of Discrepant Learning Rates for Double Over-parameterization |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Robust Reinforcement Learning via Adversarial training with Langevin Dynamics |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Robust Sequence Submodular Maximization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Robust Sub-Gaussian Principal Component Analysis and Width-Independent Schatten Packing |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Robust and Heavy-Tailed Mean Estimation Made Simple, via Regret Minimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Robust compressed sensing using generative models |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Robust large-margin learning in hyperbolic space |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Robust, Accurate Stochastic Optimization for Variational Inference |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Robust-Adaptive Control of Linear Systems: beyond Quadratic Costs |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Robustness Analysis of Non-Convex Stochastic Gradient Descent using Biased Expectations |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Robustness of Bayesian Neural Networks to Gradient-Based Attacks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Robustness of Community Detection to Random Geometric Perturbations |
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❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Rotated Binary Neural Network |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Rotation-Invariant Local-to-Global Representation Learning for 3D Point Cloud |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| SAC: Accelerating and Structuring Self-Attention via Sparse Adaptive Connection |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| SCOP: Scientific Control for Reliable Neural Network Pruning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| SDF-SRN: Learning Signed Distance 3D Object Reconstruction from Static Images |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| SE(3)-Transformers: 3D Roto-Translation Equivariant Attention Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| SEVIR : A Storm Event Imagery Dataset for Deep Learning Applications in Radar and Satellite Meteorology |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| SGD with shuffling: optimal rates without component convexity and large epoch requirements |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| SIRI: Spatial Relation Induced Network For Spatial Description Resolution |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| SLIP: Learning to predict in unknown dynamical systems with long-term memory |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| SMYRF - Efficient Attention using Asymmetric Clustering |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| SOLOv2: Dynamic and Fast Instance Segmentation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| STEER : Simple Temporal Regularization For Neural ODE |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| STLnet: Signal Temporal Logic Enforced Multivariate Recurrent Neural Networks |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| SURF: A Simple, Universal, Robust, Fast Distribution Learning Algorithm |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| SVGD as a kernelized Wasserstein gradient flow of the chi-squared divergence |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Safe Reinforcement Learning via Curriculum Induction |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Sample Complexity of Asynchronous Q-Learning: Sharper Analysis and Variance Reduction |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Sample Complexity of Uniform Convergence for Multicalibration |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Sample Efficient Reinforcement Learning via Low-Rank Matrix Estimation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Sample complexity and effective dimension for regression on manifolds |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Sample-Efficient Optimization in the Latent Space of Deep Generative Models via Weighted Retraining |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Sample-Efficient Reinforcement Learning of Undercomplete POMDPs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Sampling from a k-DPP without looking at all items |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Sampling-Decomposable Generative Adversarial Recommender |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Sanity-Checking Pruning Methods: Random Tickets can Win the Jackpot |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Scalable Belief Propagation via Relaxed Scheduling |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Scalable Graph Neural Networks via Bidirectional Propagation |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Scalable Multi-Agent Reinforcement Learning for Networked Systems with Average Reward |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Scattering GCN: Overcoming Oversmoothness in Graph Convolutional Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Searching for Low-Bit Weights in Quantized Neural Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Second Order Optimality in Decentralized Non-Convex Optimization via Perturbed Gradient Tracking |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Second Order PAC-Bayesian Bounds for the Weighted Majority Vote |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Secretary and Online Matching Problems with Machine Learned Advice |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Security Analysis of Safe and Seldonian Reinforcement Learning Algorithms |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| See, Hear, Explore: Curiosity via Audio-Visual Association |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Self-Adaptive Training: beyond Empirical Risk Minimization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Self-Adaptively Learning to Demoiré from Focused and Defocused Image Pairs |
❌ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
4 |
| Self-Distillation Amplifies Regularization in Hilbert Space |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Self-Distillation as Instance-Specific Label Smoothing |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Self-Imitation Learning via Generalized Lower Bound Q-learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Self-Learning Transformations for Improving Gaze and Head Redirection |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Self-Paced Deep Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Self-Supervised Few-Shot Learning on Point Clouds |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Self-Supervised Generative Adversarial Compression |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Self-Supervised Graph Transformer on Large-Scale Molecular Data |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Self-Supervised Learning by Cross-Modal Audio-Video Clustering |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Self-Supervised MultiModal Versatile Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Self-Supervised Relational Reasoning for Representation Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Self-Supervised Relationship Probing |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Self-Supervised Visual Representation Learning from Hierarchical Grouping |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Self-paced Contrastive Learning with Hybrid Memory for Domain Adaptive Object Re-ID |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Self-supervised Auxiliary Learning with Meta-paths for Heterogeneous Graphs |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Self-supervised Co-Training for Video Representation Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Self-supervised learning through the eyes of a child |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Self-training Avoids Using Spurious Features Under Domain Shift |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Semantic Visual Navigation by Watching YouTube Videos |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Semi-Supervised Neural Architecture Search |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Semi-Supervised Partial Label Learning via Confidence-Rated Margin Maximization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Semialgebraic Optimization for Lipschitz Constants of ReLU Networks |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Sense and Sensitivity Analysis: Simple Post-Hoc Analysis of Bias Due to Unobserved Confounding |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Sequence to Multi-Sequence Learning via Conditional Chain Mapping for Mixture Signals |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Sequential Bayesian Experimental Design with Variable Cost Structure |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Set2Graph: Learning Graphs From Sets |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| ShapeFlow: Learnable Deformation Flows Among 3D Shapes |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Shared Experience Actor-Critic for Multi-Agent Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Shared Space Transfer Learning for analyzing multi-site fMRI data |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Sharp Representation Theorems for ReLU Networks with Precise Dependence on Depth |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Sharp uniform convergence bounds through empirical centralization |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Sharpened Generalization Bounds based on Conditional Mutual Information and an Application to Noisy, Iterative Algorithms |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Sharper Generalization Bounds for Pairwise Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| ShiftAddNet: A Hardware-Inspired Deep Network |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Simple and Fast Algorithm for Binary Integer and Online Linear Programming |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Simple and Principled Uncertainty Estimation with Deterministic Deep Learning via Distance Awareness |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Simple and Scalable Sparse k-means Clustering via Feature Ranking |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Simplify and Robustify Negative Sampling for Implicit Collaborative Filtering |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Simplifying Hamiltonian and Lagrangian Neural Networks via Explicit Constraints |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Simulating a Primary Visual Cortex at the Front of CNNs Improves Robustness to Image Perturbations |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Simultaneous Preference and Metric Learning from Paired Comparisons |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
✅ |
3 |
| Simultaneously Learning Stochastic and Adversarial Episodic MDPs with Known Transition |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Sinkhorn Barycenter via Functional Gradient Descent |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Sinkhorn Natural Gradient for Generative Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Skeleton-bridged Point Completion: From Global Inference to Local Adjustment |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Sliding Window Algorithms for k-Clustering Problems |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Small Nash Equilibrium Certificates in Very Large Games |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
❌ |
3 |
| Smooth And Consistent Probabilistic Regression Trees |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Smoothed Analysis of Online and Differentially Private Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Smoothed Geometry for Robust Attribution |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Smoothly Bounding User Contributions in Differential Privacy |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| SnapBoost: A Heterogeneous Boosting Machine |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Soft Contrastive Learning for Visual Localization |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| SoftFlow: Probabilistic Framework for Normalizing Flow on Manifolds |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Softmax Deep Double Deterministic Policy Gradients |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Solver-in-the-Loop: Learning from Differentiable Physics to Interact with Iterative PDE-Solvers |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Space-Time Correspondence as a Contrastive Random Walk |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Sparse Graphical Memory for Robust Planning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Sparse Learning with CART |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Sparse Spectrum Warped Input Measures for Nonstationary Kernel Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Sparse Symplectically Integrated Neural Networks |
❌ |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Sparse Weight Activation Training |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Sparse and Continuous Attention Mechanisms |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Spectra of the Conjugate Kernel and Neural Tangent Kernel for linear-width neural networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Spectral Temporal Graph Neural Network for Multivariate Time-series Forecasting |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Spike and slab variational Bayes for high dimensional logistic regression |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Spin-Weighted Spherical CNNs |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Stability of Stochastic Gradient Descent on Nonsmooth Convex Losses |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Stable and expressive recurrent vision models |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Stage-wise Conservative Linear Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Stateful Posted Pricing with Vanishing Regret via Dynamic Deterministic Markov Decision Processes |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Stationary Activations for Uncertainty Calibration in Deep Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Statistical Efficiency of Thompson Sampling for Combinatorial Semi-Bandits |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Statistical Guarantees of Distributed Nearest Neighbor Classification |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Statistical Optimal Transport posed as Learning Kernel Embedding |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Statistical and Topological Properties of Sliced Probability Divergences |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Statistical control for spatio-temporal MEG/EEG source imaging with desparsified mutli-task Lasso |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Statistical-Query Lower Bounds via Functional Gradients |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Steady State Analysis of Episodic Reinforcement Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Steering Distortions to Preserve Classes and Neighbors in Supervised Dimensionality Reduction |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Stein Self-Repulsive Dynamics: Benefits From Past Samples |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Stochastic Deep Gaussian Processes over Graphs |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Stochastic Gradient Descent in Correlated Settings: A Study on Gaussian Processes |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Stochastic Latent Actor-Critic: Deep Reinforcement Learning with a Latent Variable Model |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Stochastic Normalization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Stochastic Normalizing Flows |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Stochastic Optimization for Performative Prediction |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Stochastic Optimization with Heavy-Tailed Noise via Accelerated Gradient Clipping |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Stochastic Optimization with Laggard Data Pipelines |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Stochastic Recursive Gradient Descent Ascent for Stochastic Nonconvex-Strongly-Concave Minimax Problems |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Stochastic Segmentation Networks: Modelling Spatially Correlated Aleatoric Uncertainty |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Stochastic Stein Discrepancies |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Stochasticity of Deterministic Gradient Descent: Large Learning Rate for Multiscale Objective Function |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Storage Efficient and Dynamic Flexible Runtime Channel Pruning via Deep Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| StratLearner: Learning a Strategy for Misinformation Prevention in Social Networks |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Strictly Batch Imitation Learning by Energy-based Distribution Matching |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Strongly Incremental Constituency Parsing with Graph Neural Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Strongly local p-norm-cut algorithms for semi-supervised learning and local graph clustering |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Structured Convolutions for Efficient Neural Network Design |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Structured Prediction for Conditional Meta-Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Sub-linear Regret Bounds for Bayesian Optimisation in Unknown Search Spaces |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Sub-sampling for Efficient Non-Parametric Bandit Exploration |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Subgraph Neural Networks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Subgroup-based Rank-1 Lattice Quasi-Monte Carlo |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Submodular Maximization Through Barrier Functions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Submodular Meta-Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Succinct and Robust Multi-Agent Communication With Temporal Message Control |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Sufficient dimension reduction for classification using principal optimal transport direction |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| SuperLoss: A Generic Loss for Robust Curriculum Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Supermasks in Superposition |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Supervised Contrastive Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| SurVAE Flows: Surjections to Bridge the Gap between VAEs and Flows |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| Swapping Autoencoder for Deep Image Manipulation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Synbols: Probing Learning Algorithms with Synthetic Datasets |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Synthesize, Execute and Debug: Learning to Repair for Neural Program Synthesis |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Synthesizing Tasks for Block-based Programming |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Synthetic Data Generators -- Sequential and Private |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| System Identification with Biophysical Constraints: A Circuit Model of the Inner Retina |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| TSPNet: Hierarchical Feature Learning via Temporal Semantic Pyramid for Sign Language Translation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Tackling the Objective Inconsistency Problem in Heterogeneous Federated Optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Taming Discrete Integration via the Boon of Dimensionality |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Targeted Adversarial Perturbations for Monocular Depth Prediction |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Task-Agnostic Amortized Inference of Gaussian Process Hyperparameters |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Task-Agnostic Online Reinforcement Learning with an Infinite Mixture of Gaussian Processes |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Task-Oriented Feature Distillation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Task-Robust Model-Agnostic Meta-Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Task-agnostic Exploration in Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| TaylorGAN: Neighbor-Augmented Policy Update Towards Sample-Efficient Natural Language Generation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Teaching a GAN What Not to Learn |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
3 |
| Telescoping Density-Ratio Estimation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Temporal Positive-unlabeled Learning for Biomedical Hypothesis Generation via Risk Estimation |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Temporal Spike Sequence Learning via Backpropagation for Deep Spiking Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Temporal Variability in Implicit Online Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Tensor Completion Made Practical |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Testing Determinantal Point Processes |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Texture Interpolation for Probing Visual Perception |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| The Adaptive Complexity of Maximizing a Gross Substitutes Valuation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Advantage of Conditional Meta-Learning for Biased Regularization and Fine Tuning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| The All-or-Nothing Phenomenon in Sparse Tensor PCA |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| The Autoencoding Variational Autoencoder |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| The Complete Lasso Tradeoff Diagram |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Complexity of Adversarially Robust Proper Learning of Halfspaces with Agnostic Noise |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| The Cone of Silence: Speech Separation by Localization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| The Convex Relaxation Barrier, Revisited: Tightened Single-Neuron Relaxations for Neural Network Verification |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| The Convolution Exponential and Generalized Sylvester Flows |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| The Devil is in the Detail: A Framework for Macroscopic Prediction via Microscopic Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| The Dilemma of TriHard Loss and an Element-Weighted TriHard Loss for Person Re-Identification |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| The Discrete Gaussian for Differential Privacy |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| The Diversified Ensemble Neural Network |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| The Flajolet-Martin Sketch Itself Preserves Differential Privacy: Private Counting with Minimal Space |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Generalization-Stability Tradeoff In Neural Network Pruning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| The Generalized Lasso with Nonlinear Observations and Generative Priors |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| The Hateful Memes Challenge: Detecting Hate Speech in Multimodal Memes |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| The Implications of Local Correlation on Learning Some Deep Functions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| The LoCA Regret: A Consistent Metric to Evaluate Model-Based Behavior in Reinforcement Learning |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Lottery Ticket Hypothesis for Pre-trained BERT Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| The MAGICAL Benchmark for Robust Imitation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| The Mean-Squared Error of Double Q-Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| The NetHack Learning Environment |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| The Origins and Prevalence of Texture Bias in Convolutional Neural Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| The Pitfalls of Simplicity Bias in Neural Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| The Potts-Ising model for discrete multivariate data |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| The Power of Comparisons for Actively Learning Linear Classifiers |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Power of Predictions in Online Control |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Primal-Dual method for Learning Augmented Algorithms |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| The Smoothed Possibility of Social Choice |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| The Statistical Complexity of Early-Stopped Mirror Descent |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| The Statistical Cost of Robust Kernel Hyperparameter Turning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| The Strong Screening Rule for SLOPE |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| The Surprising Simplicity of the Early-Time Learning Dynamics of Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| The Value Equivalence Principle for Model-Based Reinforcement Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| The Wasserstein Proximal Gradient Algorithm |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| The interplay between randomness and structure during learning in RNNs |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| The phase diagram of approximation rates for deep neural networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| The route to chaos in routing games: When is price of anarchy too optimistic? |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Theoretical Insights Into Multiclass Classification: A High-dimensional Asymptotic View |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Theory-Inspired Path-Regularized Differential Network Architecture Search |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Throughput-Optimal Topology Design for Cross-Silo Federated Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Thunder: a Fast Coordinate Selection Solver for Sparse Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Tight First- and Second-Order Regret Bounds for Adversarial Linear Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Tight Nonparametric Convergence Rates for Stochastic Gradient Descent under the Noiseless Linear Model |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Tight last-iterate convergence rates for no-regret learning in multi-player games |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Time-Reversal Symmetric ODE Network |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Timeseries Anomaly Detection using Temporal Hierarchical One-Class Network |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| TinyTL: Reduce Memory, Not Parameters for Efficient On-Device Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Top-KAST: Top-K Always Sparse Training |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Top-k Training of GANs: Improving GAN Performance by Throwing Away Bad Samples |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| TorsionNet: A Reinforcement Learning Approach to Sequential Conformer Search |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Toward the Fundamental Limits of Imitation Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Towards Better Generalization of Adaptive Gradient Methods |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Towards Convergence Rate Analysis of Random Forests for Classification |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Towards Crowdsourced Training of Large Neural Networks using Decentralized Mixture-of-Experts |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Towards Deeper Graph Neural Networks with Differentiable Group Normalization |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Towards Interpretable Natural Language Understanding with Explanations as Latent Variables |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Towards Learning Convolutions from Scratch |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Towards Maximizing the Representation Gap between In-Domain & Out-of-Distribution Examples |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Towards Minimax Optimal Reinforcement Learning in Factored Markov Decision Processes |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Towards More Practical Adversarial Attacks on Graph Neural Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Towards Neural Programming Interfaces |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Towards Playing Full MOBA Games with Deep Reinforcement Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Towards Problem-dependent Optimal Learning Rates |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Towards Safe Policy Improvement for Non-Stationary MDPs |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Towards Scalable Bayesian Learning of Causal DAGs |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Towards Scale-Invariant Graph-related Problem Solving by Iterative Homogeneous GNNs |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Towards Theoretically Understanding Why Sgd Generalizes Better Than Adam in Deep Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Towards Understanding Hierarchical Learning: Benefits of Neural Representations |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Towards a Better Global Loss Landscape of GANs |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Towards a Combinatorial Characterization of Bounded-Memory Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Towards practical differentially private causal graph discovery |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Trade-offs and Guarantees of Adversarial Representation Learning for Information Obfuscation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Trading Personalization for Accuracy: Data Debugging in Collaborative Filtering |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Train-by-Reconnect: Decoupling Locations of Weights from Their Values |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Training Generative Adversarial Networks by Solving Ordinary Differential Equations |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Training Generative Adversarial Networks with Limited Data |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Training Linear Finite-State Machines |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Training Normalizing Flows with the Information Bottleneck for Competitive Generative Classification |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Training Stronger Baselines for Learning to Optimize |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Trajectory-wise Multiple Choice Learning for Dynamics Generalization in Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Transfer Learning via $\ell_1$ Regularization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Transferable Calibration with Lower Bias and Variance in Domain Adaptation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Transferable Graph Optimizers for ML Compilers |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
3 |
| Tree! I am no Tree! I am a low dimensional Hyperbolic Embedding |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Triple descent and the two kinds of overfitting: where & why do they appear? |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Truncated Linear Regression in High Dimensions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Trust the Model When It Is Confident: Masked Model-based Actor-Critic |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Truthful Data Acquisition via Peer Prediction |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| UCLID-Net: Single View Reconstruction in Object Space |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| UCSG-NET- Unsupervised Discovering of Constructive Solid Geometry Tree |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| UDH: Universal Deep Hiding for Steganography, Watermarking, and Light Field Messaging |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| UWSOD: Toward Fully-Supervised-Level Capacity Weakly Supervised Object Detection |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Ultra-Low Precision 4-bit Training of Deep Neural Networks |
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2 |
| Ultrahyperbolic Representation Learning |
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3 |
| UnModNet: Learning to Unwrap a Modulo Image for High Dynamic Range Imaging |
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2 |
| Unbalanced Sobolev Descent |
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✅ |
❌ |
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4 |
| Uncertainty Aware Semi-Supervised Learning on Graph Data |
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✅ |
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2 |
| Uncertainty Quantification for Inferring Hawkes Networks |
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2 |
| Uncertainty-Aware Learning for Zero-Shot Semantic Segmentation |
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✅ |
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4 |
| Uncertainty-aware Self-training for Few-shot Text Classification |
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6 |
| Uncovering the Topology of Time-Varying fMRI Data using Cubical Persistence |
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4 |
| Understanding Anomaly Detection with Deep Invertible Networks through Hierarchies of Distributions and Features |
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2 |
| Understanding Approximate Fisher Information for Fast Convergence of Natural Gradient Descent in Wide Neural Networks |
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3 |
| Understanding Deep Architecture with Reasoning Layer |
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3 |
| Understanding Double Descent Requires A Fine-Grained Bias-Variance Decomposition |
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❌ |
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❌ |
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1 |
| Understanding Global Feature Contributions With Additive Importance Measures |
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✅ |
✅ |
✅ |
❌ |
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4 |
| Understanding Gradient Clipping in Private SGD: A Geometric Perspective |
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❌ |
✅ |
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❌ |
❌ |
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2 |
| Understanding and Exploring the Network with Stochastic Architectures |
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3 |
| Understanding and Improving Fast Adversarial Training |
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4 |
| Understanding spiking networks through convex optimization |
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2 |
| Understanding the Role of Training Regimes in Continual Learning |
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❌ |
❌ |
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4 |
| Unfolding recurrence by Green’s functions for optimized reservoir computing |
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2 |
| Unfolding the Alternating Optimization for Blind Super Resolution |
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❌ |
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4 |
| Unifying Activation- and Timing-based Learning Rules for Spiking Neural Networks |
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✅ |
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❌ |
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4 |
| Universal Domain Adaptation through Self Supervision |
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4 |
| Universal Function Approximation on Graphs |
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5 |
| Universal guarantees for decision tree induction via a higher-order splitting criterion |
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1 |
| Universally Quantized Neural Compression |
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3 |
| Unreasonable Effectiveness of Greedy Algorithms in Multi-Armed Bandit with Many Arms |
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4 |
| Unsupervised Data Augmentation for Consistency Training |
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3 |
| Unsupervised Joint k-node Graph Representations with Compositional Energy-Based Models |
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3 |
| Unsupervised Learning of Dense Visual Representations |
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3 |
| Unsupervised Learning of Lagrangian Dynamics from Images for Prediction and Control |
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3 |
| Unsupervised Learning of Object Landmarks via Self-Training Correspondence |
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4 |
| Unsupervised Learning of Visual Features by Contrasting Cluster Assignments |
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5 |
| Unsupervised Representation Learning by Invariance Propagation |
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3 |
| Unsupervised Semantic Aggregation and Deformable Template Matching for Semi-Supervised Learning |
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5 |
| Unsupervised Sound Separation Using Mixture Invariant Training |
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4 |
| Unsupervised Text Generation by Learning from Search |
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❌ |
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6 |
| Unsupervised Translation of Programming Languages |
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5 |
| Unsupervised object-centric video generation and decomposition in 3D |
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❌ |
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3 |
| Untangling tradeoffs between recurrence and self-attention in artificial neural networks |
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❌ |
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4 |
| Upper Confidence Primal-Dual Reinforcement Learning for CMDP with Adversarial Loss |
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❌ |
❌ |
❌ |
❌ |
❌ |
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1 |
| User-Dependent Neural Sequence Models for Continuous-Time Event Data |
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✅ |
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4 |
| Using noise to probe recurrent neural network structure and prune synapses |
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❌ |
❌ |
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1 |
| VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data |
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2 |
| VIME: Extending the Success of Self- and Semi-supervised Learning to Tabular Domain |
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❌ |
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3 |
| Value-driven Hindsight Modelling |
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✅ |
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2 |
| VarGrad: A Low-Variance Gradient Estimator for Variational Inference |
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✅ |
✅ |
✅ |
❌ |
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✅ |
5 |
| Variance Reduction via Accelerated Dual Averaging for Finite-Sum Optimization |
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3 |
| Variance reduction for Random Coordinate Descent-Langevin Monte Carlo |
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2 |
| Variance-Reduced Off-Policy TDC Learning: Non-Asymptotic Convergence Analysis |
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3 |
| Variational Amodal Object Completion |
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2 |
| Variational Bayesian Monte Carlo with Noisy Likelihoods |
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3 |
| Variational Bayesian Unlearning |
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3 |
| Variational Inference for Graph Convolutional Networks in the Absence of Graph Data and Adversarial Settings |
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4 |
| Variational Interaction Information Maximization for Cross-domain Disentanglement |
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2 |
| Variational Policy Gradient Method for Reinforcement Learning with General Utilities |
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3 |
| Video Frame Interpolation without Temporal Priors |
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4 |
| Video Object Segmentation with Adaptive Feature Bank and Uncertain-Region Refinement |
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5 |
| WOR and $p$'s: Sketches for $\ell_p$-Sampling Without Replacement |
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✅ |
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3 |
| Walking in the Shadow: A New Perspective on Descent Directions for Constrained Minimization |
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3 |
| Walsh-Hadamard Variational Inference for Bayesian Deep Learning |
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✅ |
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❌ |
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5 |
| Wasserstein Distances for Stereo Disparity Estimation |
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4 |
| Watch out! Motion is Blurring the Vision of Your Deep Neural Networks |
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✅ |
✅ |
❌ |
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4 |
| Wavelet Flow: Fast Training of High Resolution Normalizing Flows |
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✅ |
✅ |
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5 |
| Weak Form Generalized Hamiltonian Learning |
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3 |
| Weakly Supervised Deep Functional Maps for Shape Matching |
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4 |
| Weakly-Supervised Reinforcement Learning for Controllable Behavior |
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2 |
| Weighted QMIX: Expanding Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning |
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4 |
| Weisfeiler and Leman go sparse: Towards scalable higher-order graph embeddings |
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2 |
| Weston-Watkins Hinge Loss and Ordered Partitions |
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❌ |
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0 |
| What Did You Think Would Happen? Explaining Agent Behaviour through Intended Outcomes |
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3 |
| What Do Neural Networks Learn When Trained With Random Labels? |
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2 |
| What Makes for Good Views for Contrastive Learning? |
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2 |
| What Neural Networks Memorize and Why: Discovering the Long Tail via Influence Estimation |
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4 |
| What if Neural Networks had SVDs? |
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4 |
| What is being transferred in transfer learning? |
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2 |
| What shapes feature representations? Exploring datasets, architectures, and training |
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3 |
| What went wrong and when? Instance-wise feature importance for time-series black-box models |
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3 |
| When Counterpoint Meets Chinese Folk Melodies |
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2 |
| When Do Neural Networks Outperform Kernel Methods? |
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3 |
| When and How to Lift the Lockdown? Global COVID-19 Scenario Analysis and Policy Assessment using Compartmental Gaussian Processes |
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4 |
| Why Do Deep Residual Networks Generalize Better than Deep Feedforward Networks? --- A Neural Tangent Kernel Perspective |
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2 |
| Why Normalizing Flows Fail to Detect Out-of-Distribution Data |
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4 |
| Why are Adaptive Methods Good for Attention Models? |
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4 |
| Winning the Lottery with Continuous Sparsification |
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4 |
| Wisdom of the Ensemble: Improving Consistency of Deep Learning Models |
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5 |
| WoodFisher: Efficient Second-Order Approximation for Neural Network Compression |
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4 |
| Woodbury Transformations for Deep Generative Flows |
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3 |
| Worst-Case Analysis for Randomly Collected Data |
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4 |
| X-CAL: Explicit Calibration for Survival Analysis |
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3 |
| Your Classifier can Secretly Suffice Multi-Source Domain Adaptation |
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5 |
| Your GAN is Secretly an Energy-based Model and You Should Use Discriminator Driven Latent Sampling |
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✅ |
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❌ |
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1 |
| Zap Q-Learning With Nonlinear Function Approximation |
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2 |
| Zero-Resource Knowledge-Grounded Dialogue Generation |
✅ |
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✅ |
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5 |
| f-Divergence Variational Inference |
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✅ |
✅ |
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3 |
| f-GAIL: Learning f-Divergence for Generative Adversarial Imitation Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| wav2vec 2.0: A Framework for Self-Supervised Learning of Speech Representations |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |