| A Baseline for Any Order Gradient Estimation in Stochastic Computation Graphs |
❌ |
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❌ |
❌ |
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❌ |
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1 |
| A Better k-means++ Algorithm via Local Search |
✅ |
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✅ |
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✅ |
3 |
| A Block Coordinate Descent Proximal Method for Simultaneous Filtering and Parameter Estimation |
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2 |
| A Composite Randomized Incremental Gradient Method |
✅ |
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✅ |
2 |
| A Conditional-Gradient-Based Augmented Lagrangian Framework |
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✅ |
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2 |
| A Contrastive Divergence for Combining Variational Inference and MCMC |
✅ |
✅ |
✅ |
❌ |
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4 |
| A Convergence Theory for Deep Learning via Over-Parameterization |
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✅ |
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2 |
| A Deep Reinforcement Learning Perspective on Internet Congestion Control |
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✅ |
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✅ |
2 |
| A Dynamical Systems Perspective on Nesterov Acceleration |
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❌ |
✅ |
1 |
| A Framework for Bayesian Optimization in Embedded Subspaces |
✅ |
✅ |
✅ |
❌ |
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3 |
| A Gradual, Semi-Discrete Approach to Generative Network Training via Explicit Wasserstein Minimization |
✅ |
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✅ |
✅ |
❌ |
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3 |
| A Kernel Perspective for Regularizing Deep Neural Networks |
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✅ |
✅ |
✅ |
❌ |
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4 |
| A Kernel Theory of Modern Data Augmentation |
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✅ |
✅ |
❌ |
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✅ |
3 |
| A Large-Scale Study on Regularization and Normalization in GANs |
❌ |
✅ |
✅ |
❌ |
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✅ |
3 |
| A Multitask Multiple Kernel Learning Algorithm for Survival Analysis with Application to Cancer Biology |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| A Persistent Weisfeiler-Lehman Procedure for Graph Classification |
✅ |
✅ |
✅ |
✅ |
❌ |
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✅ |
5 |
| A Personalized Affective Memory Model for Improving Emotion Recognition |
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❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| A Polynomial Time MCMC Method for Sampling from Continuous Determinantal Point Processes |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| A Quantitative Analysis of the Effect of Batch Normalization on Gradient Descent |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| A Recurrent Neural Cascade-based Model for Continuous-Time Diffusion |
❌ |
✅ |
✅ |
✅ |
❌ |
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✅ |
4 |
| A Statistical Investigation of Long Memory in Language and Music |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| A Tail-Index Analysis of Stochastic Gradient Noise in Deep Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Theoretical Analysis of Contrastive Unsupervised Representation Learning |
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❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| A Theory of Regularized Markov Decision Processes |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| A Tree-Based Method for Fast Repeated Sampling of Determinantal Point Processes |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| A Wrapped Normal Distribution on Hyperbolic Space for Gradient-Based Learning |
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✅ |
✅ |
❌ |
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4 |
| A fully differentiable beam search decoder |
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✅ |
✅ |
❌ |
❌ |
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3 |
| ARSM: Augment-REINFORCE-Swap-Merge Estimator for Gradient Backpropagation Through Categorical Variables |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
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5 |
| AReS and MaRS Adversarial and MMD-Minimizing Regression for SDEs |
✅ |
✅ |
✅ |
❌ |
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4 |
| AUCμ: A Performance Metric for Multi-Class Machine Learning Models |
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❌ |
❌ |
❌ |
❌ |
❌ |
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0 |
| Accelerated Flow for Probability Distributions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Accelerated Linear Convergence of Stochastic Momentum Methods in Wasserstein Distances |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Acceleration of SVRG and Katyusha X by Inexact Preconditioning |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Action Robust Reinforcement Learning and Applications in Continuous Control |
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✅ |
✅ |
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4 |
| Active Embedding Search via Noisy Paired Comparisons |
✅ |
✅ |
✅ |
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4 |
| Active Learning for Decision-Making from Imbalanced Observational Data |
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✅ |
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✅ |
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4 |
| Active Learning for Probabilistic Structured Prediction of Cuts and Matchings |
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✅ |
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2 |
| Active Learning with Disagreement Graphs |
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✅ |
✅ |
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4 |
| Active Manifolds: A non-linear analogue to Active Subspaces |
✅ |
✅ |
❌ |
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3 |
| Actor-Attention-Critic for Multi-Agent Reinforcement Learning |
❌ |
✅ |
✅ |
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✅ |
3 |
| AdaGrad Stepsizes: Sharp Convergence Over Nonconvex Landscapes |
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✅ |
✅ |
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4 |
| Adaptive Antithetic Sampling for Variance Reduction |
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✅ |
❌ |
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1 |
| Adaptive Monte Carlo Multiple Testing via Multi-Armed Bandits |
✅ |
✅ |
✅ |
❌ |
✅ |
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✅ |
5 |
| Adaptive Neural Trees |
✅ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Adaptive Regret of Convex and Smooth Functions |
✅ |
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1 |
| Adaptive Scale-Invariant Online Algorithms for Learning Linear Models |
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3 |
| Adaptive Sensor Placement for Continuous Spaces |
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✅ |
2 |
| Adaptive Stochastic Natural Gradient Method for One-Shot Neural Architecture Search |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Adaptive and Safe Bayesian Optimization in High Dimensions via One-Dimensional Subspaces |
✅ |
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✅ |
❌ |
❌ |
❌ |
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2 |
| Addressing the Loss-Metric Mismatch with Adaptive Loss Alignment |
✅ |
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✅ |
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❌ |
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4 |
| Adjustment Criteria for Generalizing Experimental Findings |
✅ |
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❌ |
❌ |
❌ |
❌ |
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1 |
| Adversarial Attacks on Node Embeddings via Graph Poisoning |
❌ |
✅ |
✅ |
❌ |
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3 |
| Adversarial Examples Are a Natural Consequence of Test Error in Noise |
❌ |
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✅ |
❌ |
❌ |
❌ |
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2 |
| Adversarial Generation of Time-Frequency Features with application in audio synthesis |
❌ |
✅ |
✅ |
❌ |
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4 |
| Adversarial Online Learning with noise |
✅ |
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1 |
| Adversarial camera stickers: A physical camera-based attack on deep learning systems |
✅ |
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✅ |
❌ |
❌ |
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2 |
| Adversarial examples from computational constraints |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Adversarially Learned Representations for Information Obfuscation and Inference |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
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3 |
| Agnostic Federated Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
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4 |
| Almost Unsupervised Text to Speech and Automatic Speech Recognition |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
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4 |
| Almost surely constrained convex optimization |
✅ |
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✅ |
✅ |
❌ |
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✅ |
4 |
| Alternating Minimizations Converge to Second-Order Optimal Solutions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Amortized Monte Carlo Integration |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| An Instability in Variational Inference for Topic Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| An Investigation into Neural Net Optimization via Hessian Eigenvalue Density |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| An Investigation of Model-Free Planning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| An Optimal Private Stochastic-MAB Algorithm based on Optimal Private Stopping Rule |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Analogies Explained: Towards Understanding Word Embeddings |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Analyzing Federated Learning through an Adversarial Lens |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Analyzing and Improving Representations with the Soft Nearest Neighbor Loss |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Anomaly Detection With Multiple-Hypotheses Predictions |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Anytime Online-to-Batch, Optimism and Acceleration |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Approximated Oracle Filter Pruning for Destructive CNN Width Optimization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Approximating Orthogonal Matrices with Effective Givens Factorization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Approximation and non-parametric estimation of ResNet-type convolutional neural networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Are Generative Classifiers More Robust to Adversarial Attacks? |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Area Attention |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Asynchronous Batch Bayesian Optimisation with Improved Local Penalisation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| AutoVC: Zero-Shot Voice Style Transfer with Only Autoencoder Loss |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Automated Model Selection with Bayesian Quadrature |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Automatic Classifiers as Scientific Instruments: One Step Further Away from Ground-Truth |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Automatic Posterior Transformation for Likelihood-Free Inference |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Autoregressive Energy Machines |
❌ |
✅ |
✅ |
✅ |
❌ |
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✅ |
4 |
| BERT and PALs: Projected Attention Layers for Efficient Adaptation in Multi-Task Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Band-limited Training and Inference for Convolutional Neural Networks |
❌ |
❌ |
✅ |
❌ |
✅ |
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3 |
| Bandit Multiclass Linear Classification: Efficient Algorithms for the Separable Case |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Batch Policy Learning under Constraints |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| BayesNAS: A Bayesian Approach for Neural Architecture Search |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Bayesian Action Decoder for Deep Multi-Agent Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Bayesian Counterfactual Risk Minimization |
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❌ |
✅ |
✅ |
❌ |
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3 |
| Bayesian Deconditional Kernel Mean Embeddings |
✅ |
❌ |
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❌ |
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❌ |
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1 |
| Bayesian Generative Active Deep Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
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4 |
| Bayesian Joint Spike-and-Slab Graphical Lasso |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Bayesian Nonparametric Federated Learning of Neural Networks |
✅ |
✅ |
✅ |
❌ |
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❌ |
✅ |
4 |
| Bayesian Optimization Meets Bayesian Optimal Stopping |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Bayesian Optimization of Composite Functions |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Bayesian leave-one-out cross-validation for large data |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Benefits and Pitfalls of the Exponential Mechanism with Applications to Hilbert Spaces and Functional PCA |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
3 |
| Better generalization with less data using robust gradient descent |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Beyond Adaptive Submodularity: Approximation Guarantees of Greedy Policy with Adaptive Submodularity Ratio |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Beyond Backprop: Online Alternating Minimization with Auxiliary Variables |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Beyond the Chinese Restaurant and Pitman-Yor processes: Statistical Models with double power-law behavior |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
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3 |
| Bias Also Matters: Bias Attribution for Deep Neural Network Explanation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Bilinear Bandits with Low-rank Structure |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Bit-Swap: Recursive Bits-Back Coding for Lossless Compression with Hierarchical Latent Variables |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Blended Conditonal Gradients |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Boosted Density Estimation Remastered |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Bounding User Contributions: A Bias-Variance Trade-off in Differential Privacy |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Breaking Inter-Layer Co-Adaptation by Classifier Anonymization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Breaking the Softmax Bottleneck via Learnable Monotonic Pointwise Non-linearities |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Breaking the gridlock in Mixture-of-Experts: Consistent and Efficient Algorithms |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Bridging Theory and Algorithm for Domain Adaptation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| CAB: Continuous Adaptive Blending for Policy Evaluation and Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| CHiVE: Varying Prosody in Speech Synthesis with a Linguistically Driven Dynamic Hierarchical Conditional Variational Network |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| COMIC: Multi-view Clustering Without Parameter Selection |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| CURIOUS: Intrinsically Motivated Modular Multi-Goal Reinforcement Learning |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Calibrated Approximate Bayesian Inference |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Calibrated Model-Based Deep Reinforcement Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| CapsAndRuns: An Improved Method for Approximately Optimal Algorithm Configuration |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Categorical Feature Compression via Submodular Optimization |
✅ |
❌ |
✅ |
✅ |
❌ |
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❌ |
3 |
| Causal Discovery and Forecasting in Nonstationary Environments with State-Space Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Causal Identification under Markov Equivalence: Completeness Results |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Cautious Regret Minimization: Online Optimization with Long-Term Budget Constraints |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Certified Adversarial Robustness via Randomized Smoothing |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Challenging Common Assumptions in the Unsupervised Learning of Disentangled Representations |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Characterization of Convex Objective Functions and Optimal Expected Convergence Rates for SGD |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Characterizing Well-Behaved vs. Pathological Deep Neural Networks |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Cheap Orthogonal Constraints in Neural Networks: A Simple Parametrization of the Orthogonal and Unitary Group |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Circuit-GNN: Graph Neural Networks for Distributed Circuit Design |
❌ |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
3 |
| Classification from Positive, Unlabeled and Biased Negative Data |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Classifying Treatment Responders Under Causal Effect Monotonicity |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Co-Representation Network for Generalized Zero-Shot Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Co-manifold learning with missing data |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| CoT: Cooperative Training for Generative Modeling of Discrete Data |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Cognitive model priors for predicting human decisions |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Collaborative Channel Pruning for Deep Networks |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Collaborative Evolutionary Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Collective Model Fusion for Multiple Black-Box Experts |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Combating Label Noise in Deep Learning using Abstention |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Combining parametric and nonparametric models for off-policy evaluation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Communication Complexity in Locally Private Distribution Estimation and Heavy Hitters |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Communication-Constrained Inference and the Role of Shared Randomness |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| CompILE: Compositional Imitation Learning and Execution |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Competing Against Nash Equilibria in Adversarially Changing Zero-Sum Games |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Complementary-Label Learning for Arbitrary Losses and Models |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Complexity of Linear Regions in Deep Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Composable Core-sets for Determinant Maximization: A Simple Near-Optimal Algorithm |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Composing Entropic Policies using Divergence Correction |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Composing Value Functions in Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Compositional Fairness Constraints for Graph Embeddings |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Compressed Factorization: Fast and Accurate Low-Rank Factorization of Compressively-Sensed Data |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Compressing Gradient Optimizers via Count-Sketches |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Concentration Inequalities for Conditional Value at Risk |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Concrete Autoencoders: Differentiable Feature Selection and Reconstruction |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Conditional Gradient Methods via Stochastic Path-Integrated Differential Estimator |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Conditional Independence in Testing Bayesian Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Conditioning by adaptive sampling for robust design |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Connectivity-Optimized Representation Learning via Persistent Homology |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Context-Aware Zero-Shot Learning for Object Recognition |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Contextual Memory Trees |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Contextual Multi-armed Bandit Algorithm for Semiparametric Reward Model |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Control Regularization for Reduced Variance Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Convolutional Poisson Gamma Belief Network |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Coresets for Ordered Weighted Clustering |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Correlated Variational Auto-Encoders |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Correlated bandits or: How to minimize mean-squared error online |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Counterfactual Off-Policy Evaluation with Gumbel-Max Structural Causal Models |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Counterfactual Visual Explanations |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Cross-Domain 3D Equivariant Image Embeddings |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Curiosity-Bottleneck: Exploration By Distilling Task-Specific Novelty |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Curvature-Exploiting Acceleration of Elastic Net Computations |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| DAG-GNN: DAG Structure Learning with Graph Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| DBSCAN++: Towards fast and scalable density clustering |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| DL2: Training and Querying Neural Networks with Logic |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
5 |
| DP-GP-LVM: A Bayesian Non-Parametric Model for Learning Multivariate Dependency Structures |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Data Poisoning Attacks on Stochastic Bandits |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Data Shapley: Equitable Valuation of Data for Machine Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Dead-ends and Secure Exploration in Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Decentralized Exploration in Multi-Armed Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Decentralized Stochastic Optimization and Gossip Algorithms with Compressed Communication |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Decomposing feature-level variation with Covariate Gaussian Process Latent Variable Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Deep Compressed Sensing |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Deep Counterfactual Regret Minimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Deep Factors for Forecasting |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Deep Gaussian Processes with Importance-Weighted Variational Inference |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Deep Generative Learning via Variational Gradient Flow |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Deep Residual Output Layers for Neural Language Generation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| DeepMDP: Learning Continuous Latent Space Models for Representation Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| DeepNose: Using artificial neural networks to represent the space of odorants |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Defending Against Saddle Point Attack in Byzantine-Robust Distributed Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Demystifying Dropout |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Detecting Overlapping and Correlated Communities without Pure Nodes: Identifiability and Algorithm |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Diagnosing Bottlenecks in Deep Q-learning Algorithms |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Differentiable Dynamic Normalization for Learning Deep Representation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Differentiable Linearized ADMM |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Differential Inclusions for Modeling Nonsmooth ADMM Variants: A Continuous Limit Theory |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Differentially Private Empirical Risk Minimization with Non-convex Loss Functions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Differentially Private Fair Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Differentially Private Learning of Geometric Concepts |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Dimension-Wise Importance Sampling Weight Clipping for Sample-Efficient Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Dimensionality Reduction for Tukey Regression |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Direct Uncertainty Prediction for Medical Second Opinions |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Dirichlet Simplex Nest and Geometric Inference |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Discovering Conditionally Salient Features with Statistical Guarantees |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Discovering Context Effects from Raw Choice Data |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Discovering Latent Covariance Structures for Multiple Time Series |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Discovering Options for Exploration by Minimizing Cover Time |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Discriminative Regularization for Latent Variable Models with Applications to Electrocardiography |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Disentangled Graph Convolutional Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Disentangling Disentanglement in Variational Autoencoders |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Distributed Learning over Unreliable Networks |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Distributed Learning with Sublinear Communication |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Distributed Weighted Matching via Randomized Composable Coresets |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Distributed, Egocentric Representations of Graphs for Detecting Critical Structures |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Distribution calibration for regression |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Distributional Multivariate Policy Evaluation and Exploration with the Bellman GAN |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Distributional Reinforcement Learning for Efficient Exploration |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Do ImageNet Classifiers Generalize to ImageNet? |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Does Data Augmentation Lead to Positive Margin? |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Domain Adaptation with Asymmetrically-Relaxed Distribution Alignment |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Domain Agnostic Learning with Disentangled Representations |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| DoubleSqueeze: Parallel Stochastic Gradient Descent with Double-pass Error-Compensated Compression |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Doubly Robust Joint Learning for Recommendation on Data Missing Not at Random |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Doubly-Competitive Distribution Estimation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Dropout as a Structured Shrinkage Prior |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Dual Entangled Polynomial Code: Three-Dimensional Coding for Distributed Matrix Multiplication |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| Dynamic Learning with Frequent New Product Launches: A Sequential Multinomial Logit Bandit Problem |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Dynamic Measurement Scheduling for Event Forecasting using Deep RL |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Dynamic Weights in Multi-Objective Deep Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| EDDI: Efficient Dynamic Discovery of High-Value Information with Partial VAE |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| ELF OpenGo: an analysis and open reimplementation of AlphaZero |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| EMI: Exploration with Mutual Information |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Efficient Amortised Bayesian Inference for Hierarchical and Nonlinear Dynamical Systems |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Efficient Dictionary Learning with Gradient Descent |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Efficient Full-Matrix Adaptive Regularization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Efficient Nonconvex Regularized Tensor Completion with Structure-aware Proximal Iterations |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Efficient Off-Policy Meta-Reinforcement Learning via Probabilistic Context Variables |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Efficient On-Device Models using Neural Projections |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Efficient Training of BERT by Progressively Stacking |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Efficient learning of smooth probability functions from Bernoulli tests with guarantees |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Efficient optimization of loops and limits with randomized telescoping sums |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| EigenDamage: Structured Pruning in the Kronecker-Factored Eigenbasis |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Emerging Convolutions for Generative Normalizing Flows |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Empirical Analysis of Beam Search Performance Degradation in Neural Sequence Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| End-to-End Probabilistic Inference for Nonstationary Audio Analysis |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Entropic GANs meet VAEs: A Statistical Approach to Compute Sample Likelihoods in GANs |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Equivariant Transformer Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Error Feedback Fixes SignSGD and other Gradient Compression Schemes |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Escaping Saddle Points with Adaptive Gradient Methods |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Estimate Sequences for Variance-Reduced Stochastic Composite Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Estimating Information Flow in Deep Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Explaining Deep Neural Networks with a Polynomial Time Algorithm for Shapley Value Approximation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Exploiting Worker Correlation for Label Aggregation in Crowdsourcing |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Exploiting structure of uncertainty for efficient matroid semi-bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Exploration Conscious Reinforcement Learning Revisited |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Exploring interpretable LSTM neural networks over multi-variable data |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Exploring the Landscape of Spatial Robustness |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Extrapolating Beyond Suboptimal Demonstrations via Inverse Reinforcement Learning from Observations |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Fair Regression: Quantitative Definitions and Reduction-Based Algorithms |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fair k-Center Clustering for Data Summarization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Fairness risk measures |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Fairness without Harm: Decoupled Classifiers with Preference Guarantees |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Fairness-Aware Learning for Continuous Attributes and Treatments |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Fairwashing: the risk of rationalization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Fast Algorithm for Generalized Multinomial Models with Ranking Data |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fast Context Adaptation via Meta-Learning |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Fast Direct Search in an Optimally Compressed Continuous Target Space for Efficient Multi-Label Active Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Fast Incremental von Neumann Graph Entropy Computation: Theory, Algorithm, and Applications |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Fast Rates for a kNN Classifier Robust to Unknown Asymmetric Label Noise |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Fast and Flexible Inference of Joint Distributions from their Marginals |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fast and Stable Maximum Likelihood Estimation for Incomplete Multinomial Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Faster Algorithms for Binary Matrix Factorization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Faster Attend-Infer-Repeat with Tractable Probabilistic Models |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Faster Stochastic Alternating Direction Method of Multipliers for Nonconvex Optimization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Fault Tolerance in Iterative-Convergent Machine Learning |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Feature Grouping as a Stochastic Regularizer for High-Dimensional Structured Data |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Feature-Critic Networks for Heterogeneous Domain Generalization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Finding Mixed Nash Equilibria of Generative Adversarial Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Finding Options that Minimize Planning Time |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Fine-Grained Analysis of Optimization and Generalization for Overparameterized Two-Layer Neural Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Fingerprint Policy Optimisation for Robust Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
4 |
| Finite-Time Analysis of Distributed TD(0) with Linear Function Approximation on Multi-Agent Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| First-Order Adversarial Vulnerability of Neural Networks and Input Dimension |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| First-Order Algorithms Converge Faster than $O(1/k)$ on Convex Problems |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Flat Metric Minimization with Applications in Generative Modeling |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Flexibly Fair Representation Learning by Disentanglement |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| FloWaveNet : A Generative Flow for Raw Audio |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Flow++: Improving Flow-Based Generative Models with Variational Dequantization and Architecture Design |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Formal Privacy for Functional Data with Gaussian Perturbations |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
4 |
| Functional Transparency for Structured Data: a Game-Theoretic Approach |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| GDPP: Learning Diverse Generations using Determinantal Point Processes |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| GEOMetrics: Exploiting Geometric Structure for Graph-Encoded Objects |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| GMNN: Graph Markov Neural Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| GOODE: A Gaussian Off-The-Shelf Ordinary Differential Equation Solver |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Gaining Free or Low-Cost Interpretability with Interpretable Partial Substitute |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Game Theoretic Optimization via Gradient-based Nikaido-Isoda Function |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Garbage In, Reward Out: Bootstrapping Exploration in Multi-Armed Bandits |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Gauge Equivariant Convolutional Networks and the Icosahedral CNN |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Generalized Approximate Survey Propagation for High-Dimensional Estimation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Generalized Linear Rule Models |
❌ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
4 |
| Generalized Majorization-Minimization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Generalized No Free Lunch Theorem for Adversarial Robustness |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Generative Adversarial User Model for Reinforcement Learning Based Recommendation System |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Generative Modeling of Infinite Occluded Objects for Compositional Scene Representation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Geometric Losses for Distributional Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Geometric Scattering for Graph Data Analysis |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Geometry Aware Convolutional Filters for Omnidirectional Images Representation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Geometry and Symmetry in Short-and-Sparse Deconvolution |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Global Convergence of Block Coordinate Descent in Deep Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Good Initializations of Variational Bayes for Deep Models |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Gradient Descent Finds Global Minima of Deep Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Graph Convolutional Gaussian Processes |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Graph Element Networks: adaptive, structured computation and memory |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Graph Matching Networks for Learning the Similarity of Graph Structured Objects |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Graph Neural Network for Music Score Data and Modeling Expressive Piano Performance |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Graph Resistance and Learning from Pairwise Comparisons |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Graph U-Nets |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Graphical-model based estimation and inference for differential privacy |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Graphite: Iterative Generative Modeling of Graphs |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Greedy Layerwise Learning Can Scale To ImageNet |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Greedy Orthogonal Pivoting Algorithm for Non-Negative Matrix Factorization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Grid-Wise Control for Multi-Agent Reinforcement Learning in Video Game AI |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Gromov-Wasserstein Learning for Graph Matching and Node Embedding |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Guarantees for Spectral Clustering with Fairness Constraints |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Guided evolutionary strategies: augmenting random search with surrogate gradients |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| HOList: An Environment for Machine Learning of Higher Order Logic Theorem Proving |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Hessian Aided Policy Gradient |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Heterogeneous Model Reuse via Optimizing Multiparty Multiclass Margin |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| HexaGAN: Generative Adversarial Nets for Real World Classification |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Hierarchical Decompositional Mixtures of Variational Autoencoders |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Hierarchical Importance Weighted Autoencoders |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Hierarchically Structured Meta-learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| High-Fidelity Image Generation With Fewer Labels |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Hiring Under Uncertainty |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Homomorphic Sensing |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| How does Disagreement Help Generalization against Label Corruption? |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Humor in Word Embeddings: Cockamamie Gobbledegook for Nincompoops |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Hybrid Models with Deep and Invertible Features |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| HyperGAN: A Generative Model for Diverse, Performant Neural Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Hyperbolic Disk Embeddings for Directed Acyclic Graphs |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| IMEXnet A Forward Stable Deep Neural Network |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Imitating Latent Policies from Observation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Imitation Learning from Imperfect Demonstration |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Imperceptible, Robust, and Targeted Adversarial Examples for Automatic Speech Recognition |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Importance Sampling Policy Evaluation with an Estimated Behavior Policy |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Improved Convergence for $\ell_1$ and $\ell_∞$ Regression via Iteratively Reweighted Least Squares |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Improved Dynamic Graph Learning through Fault-Tolerant Sparsification |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Improved Parallel Algorithms for Density-Based Network Clustering |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Improved Zeroth-Order Variance Reduced Algorithms and Analysis for Nonconvex Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Improving Adversarial Robustness via Promoting Ensemble Diversity |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Improving Model Selection by Employing the Test Data |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
2 |
| Improving Neural Language Modeling via Adversarial Training |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Improving Neural Network Quantization without Retraining using Outlier Channel Splitting |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Imputing Missing Events in Continuous-Time Event Streams |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
5 |
| Incorporating Grouping Information into Bayesian Decision Tree Ensembles |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Incremental Randomized Sketching for Online Kernel Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Inference and Sampling of $K_33$-free Ising Models |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Inferring Heterogeneous Causal Effects in Presence of Spatial Confounding |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Infinite Mixture Prototypes for Few-shot Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Information-Theoretic Considerations in Batch Reinforcement Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Insertion Transformer: Flexible Sequence Generation via Insertion Operations |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Interpreting Adversarially Trained Convolutional Neural Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Invariant-Equivariant Representation Learning for Multi-Class Data |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Invertible Residual Networks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Iterative Linearized Control: Stable Algorithms and Complexity Guarantees |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Ithemal: Accurate, Portable and Fast Basic Block Throughput Estimation using Deep Neural Networks |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
❌ |
3 |
| Jumpout : Improved Dropout for Deep Neural Networks with ReLUs |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Katalyst: Boosting Convex Katayusha for Non-Convex Problems with a Large Condition Number |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Kernel Mean Matching for Content Addressability of GANs |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Kernel Normalized Cut: a Theoretical Revisit |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Kernel-Based Reinforcement Learning in Robust Markov Decision Processes |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| LGM-Net: Learning to Generate Matching Networks for Few-Shot Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| LIT: Learned Intermediate Representation Training for Model Compression |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| LR-GLM: High-Dimensional Bayesian Inference Using Low-Rank Data Approximations |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Ladder Capsule Network |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Large-Scale Sparse Kernel Canonical Correlation Analysis |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Latent Normalizing Flows for Discrete Sequences |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| LatentGNN: Learning Efficient Non-local Relations for Visual Recognition |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learn to Grow: A Continual Structure Learning Framework for Overcoming Catastrophic Forgetting |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning Action Representations for Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Classifiers for Target Domain with Limited or No Labels |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Learning Context-dependent Label Permutations for Multi-label Classification |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Learning Dependency Structures for Weak Supervision Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Learning Discrete Structures for Graph Neural Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning Discrete and Continuous Factors of Data via Alternating Disentanglement |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning Distance for Sequences by Learning a Ground Metric |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning Fast Algorithms for Linear Transforms Using Butterfly Factorizations |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Generative Models across Incomparable Spaces |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning Hawkes Processes Under Synchronization Noise |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning Latent Dynamics for Planning from Pixels |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Learning Linear-Quadratic Regulators Efficiently with only $\sqrtT$ Regret |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning Models from Data with Measurement Error: Tackling Underreporting |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Learning Neurosymbolic Generative Models via Program Synthesis |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Learning Novel Policies For Tasks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning Optimal Fair Policies |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning Optimal Linear Regularizers |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning Structured Decision Problems with Unawareness |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning What and Where to Transfer |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning a Compressed Sensing Measurement Matrix via Gradient Unrolling |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning a Prior over Intent via Meta-Inverse Reinforcement Learning |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Learning and Data Selection in Big Datasets |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
4 |
| Learning deep kernels for exponential family densities |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Learning from Delayed Outcomes via Proxies with Applications to Recommender Systems |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning from a Learner |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning interpretable continuous-time models of latent stochastic dynamical systems |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Learning to Clear the Market |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Learning to Collaborate in Markov Decision Processes |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Learning to Convolve: A Generalized Weight-Tying Approach |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning to Exploit Long-term Relational Dependencies in Knowledge Graphs |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Learning to Generalize from Sparse and Underspecified Rewards |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Learning to Groove with Inverse Sequence Transformations |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Learning to Infer Program Sketches |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Learning to Optimize Multigrid PDE Solvers |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Learning to Prove Theorems via Interacting with Proof Assistants |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Learning to Route in Similarity Graphs |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Learning to bid in revenue-maximizing auctions |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning to select for a predefined ranking |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
4 |
| Learning with Bad Training Data via Iterative Trimmed Loss Minimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Learning-to-Learn Stochastic Gradient Descent with Biased Regularization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| LegoNet: Efficient Convolutional Neural Networks with Lego Filters |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Leveraging Low-Rank Relations Between Surrogate Tasks in Structured Prediction |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Lexicographic and Depth-Sensitive Margins in Homogeneous and Non-Homogeneous Deep Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Linear-Complexity Data-Parallel Earth Mover’s Distance Approximations |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Lipschitz Generative Adversarial Nets |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Locally Private Bayesian Inference for Count Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Look Ma, No Latent Variables: Accurate Cutset Networks via Compilation |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Lorentzian Distance Learning for Hyperbolic Representations |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
3 |
| Loss Landscapes of Regularized Linear Autoencoders |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Lossless or Quantized Boosting with Integer Arithmetic |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Low Latency Privacy Preserving Inference |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Lower Bounds for Smooth Nonconvex Finite-Sum Optimization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| MASS: Masked Sequence to Sequence Pre-training for Language Generation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| ME-Net: Towards Effective Adversarial Robustness with Matrix Estimation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| MIWAE: Deep Generative Modelling and Imputation of Incomplete Data Sets |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| MONK Outlier-Robust Mean Embedding Estimation by Median-of-Means |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Making Convolutional Networks Shift-Invariant Again |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Making Decisions that Reduce Discriminatory Impacts |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Making Deep Q-learning methods robust to time discretization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Mallows ranking models: maximum likelihood estimate and regeneration |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Manifold Mixup: Better Representations by Interpolating Hidden States |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Matrix-Free Preconditioning in Online Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Maximum Entropy-Regularized Multi-Goal Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Maximum Likelihood Estimation for Learning Populations of Parameters |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| MeanSum: A Neural Model for Unsupervised Multi-Document Abstractive Summarization |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Measurements of Three-Level Hierarchical Structure in the Outliers in the Spectrum of Deepnet Hessians |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Memory-Optimal Direct Convolutions for Maximizing Classification Accuracy in Embedded Applications |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Meta-Learning Neural Bloom Filters |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Metric-Optimized Example Weights |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| MetricGAN: Generative Adversarial Networks based Black-box Metric Scores Optimization for Speech Enhancement |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Metropolis-Hastings Generative Adversarial Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Minimal Achievable Sufficient Statistic Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| MixHop: Higher-Order Graph Convolutional Architectures via Sparsified Neighborhood Mixing |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Mixture Models for Diverse Machine Translation: Tricks of the Trade |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Model Comparison for Semantic Grouping |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Model Function Based Conditional Gradient Method with Armijo-like Line Search |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Model-Based Active Exploration |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Molecular Hypergraph Grammar with Its Application to Molecular Optimization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Moment-Based Variational Inference for Markov Jump Processes |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Monge blunts Bayes: Hardness Results for Adversarial Training |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| More Efficient Off-Policy Evaluation through Regularized Targeted Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Multi-Agent Adversarial Inverse Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Multi-Frequency Phase Synchronization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Multi-Frequency Vector Diffusion Maps |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Multi-Object Representation Learning with Iterative Variational Inference |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Multi-objective training of Generative Adversarial Networks with multiple discriminators |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Multiplicative Weights Updates as a distributed constrained optimization algorithm: Convergence to second-order stationary points almost always |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Multivariate Submodular Optimization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Multivariate-Information Adversarial Ensemble for Scalable Joint Distribution Matching |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Myopic Posterior Sampling for Adaptive Goal Oriented Design of Experiments |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| NAS-Bench-101: Towards Reproducible Neural Architecture Search |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| NATTACK: Learning the Distributions of Adversarial Examples for an Improved Black-Box Attack on Deep Neural Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Natural Analysts in Adaptive Data Analysis |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Near optimal finite time identification of arbitrary linear dynamical systems |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Nearest Neighbor and Kernel Survival Analysis: Nonasymptotic Error Bounds and Strong Consistency Rates |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Neural Collaborative Subspace Clustering |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Neural Inverse Knitting: From Images to Manufacturing Instructions |
❌ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
5 |
| Neural Joint Source-Channel Coding |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Neural Logic Reinforcement Learning |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Neural Network Attributions: A Causal Perspective |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Neural Separation of Observed and Unobserved Distributions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Neurally-Guided Structure Inference |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Neuron birth-death dynamics accelerates gradient descent and converges asymptotically |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| New results on information theoretic clustering |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Noise2Self: Blind Denoising by Self-Supervision |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Noisy Dual Principal Component Pursuit |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Non-Asymptotic Analysis of Fractional Langevin Monte Carlo for Non-Convex Optimization |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Non-Monotonic Sequential Text Generation |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Non-Parametric Priors For Generative Adversarial Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Non-monotone Submodular Maximization with Nearly Optimal Adaptivity and Query Complexity |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Nonconvex Variance Reduced Optimization with Arbitrary Sampling |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Nonlinear Distributional Gradient Temporal-Difference Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Nonlinear Stein Variational Gradient Descent for Learning Diversified Mixture Models |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Nonparametric Bayesian Deep Networks with Local Competition |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Obtaining Fairness using Optimal Transport Theory |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Off-Policy Deep Reinforcement Learning without Exploration |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| On Certifying Non-Uniform Bounds against Adversarial Attacks |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| On Connected Sublevel Sets in Deep Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On Dropout and Nuclear Norm Regularization |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| On Efficient Optimal Transport: An Analysis of Greedy and Accelerated Mirror Descent Algorithms |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On Learning Invariant Representations for Domain Adaptation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On Medians of (Randomized) Pairwise Means |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| On Scalable and Efficient Computation of Large Scale Optimal Transport |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| On Sparse Linear Regression in the Local Differential Privacy Model |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| On Symmetric Losses for Learning from Corrupted Labels |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| On The Power of Curriculum Learning in Training Deep Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| On Variational Bounds of Mutual Information |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On discriminative learning of prediction uncertainty |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| On the Complexity of Approximating Wasserstein Barycenters |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| On the Computation and Communication Complexity of Parallel SGD with Dynamic Batch Sizes for Stochastic Non-Convex Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Connection Between Adversarial Robustness and Saliency Map Interpretability |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| On the Convergence and Robustness of Adversarial Training |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Design of Estimators for Bandit Off-Policy Evaluation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On the Feasibility of Learning, Rather than Assuming, Human Biases for Reward Inference |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Generalization Gap in Reparameterizable Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| On the Impact of the Activation function on Deep Neural Networks Training |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Limitations of Representing Functions on Sets |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| On the Linear Speedup Analysis of Communication Efficient Momentum SGD for Distributed Non-Convex Optimization |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| On the Long-term Impact of Algorithmic Decision Policies: Effort Unfairness and Feature Segregation through Social Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| On the Spectral Bias of Neural Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On the Universality of Invariant Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the statistical rate of nonlinear recovery in generative models with heavy-tailed data |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Online Adaptive Principal Component Analysis and Its extensions |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Online Algorithms for Rent-Or-Buy with Expert Advice |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Online Control with Adversarial Disturbances |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Online Convex Optimization in Adversarial Markov Decision Processes |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Online Learning to Rank with Features |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Online Learning with Sleeping Experts and Feedback Graphs |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Online Meta-Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Online Variance Reduction with Mixtures |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Online learning with kernel losses |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Open Vocabulary Learning on Source Code with a Graph-Structured Cache |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Open-ended learning in symmetric zero-sum games |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Optimal Algorithms for Lipschitz Bandits with Heavy-tailed Rewards |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Optimal Auctions through Deep Learning |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
4 |
| Optimal Continuous DR-Submodular Maximization and Applications to Provable Mean Field Inference |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Optimal Kronecker-Sum Approximation of Real Time Recurrent Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Optimal Mini-Batch and Step Sizes for SAGA |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Optimal Minimal Margin Maximization with Boosting |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Optimal Transport for structured data with application on graphs |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Optimality Implies Kernel Sum Classifiers are Statistically Efficient |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Optimistic Policy Optimization via Multiple Importance Sampling |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Orthogonal Random Forest for Causal Inference |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Overcoming Mean-Field Approximations in Recurrent Gaussian Process Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Overcoming Multi-model Forgetting |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Overparameterized Nonlinear Learning: Gradient Descent Takes the Shortest Path? |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| PA-GD: On the Convergence of Perturbed Alternating Gradient Descent to Second-Order Stationary Points for Structured Nonconvex Optimization |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| PAC Identification of Many Good Arms in Stochastic Multi-Armed Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| PAC Learnability of Node Functions in Networked Dynamical Systems |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| POLITEX: Regret Bounds for Policy Iteration using Expert Prediction |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| POPQORN: Quantifying Robustness of Recurrent Neural Networks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| PROVEN: Verifying Robustness of Neural Networks with a Probabilistic Approach |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Parameter efficient training of deep convolutional neural networks by dynamic sparse reparameterization |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Parameter-Efficient Transfer Learning for NLP |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Pareto Optimal Streaming Unsupervised Classification |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Parsimonious Black-Box Adversarial Attacks via Efficient Combinatorial Optimization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Partially Exchangeable Networks and Architectures for Learning Summary Statistics in Approximate Bayesian Computation |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
2 |
| Partially Linear Additive Gaussian Graphical Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Particle Flow Bayes’ Rule |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Passed & Spurious: Descent Algorithms and Local Minima in Spiked Matrix-Tensor Models |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Per-Decision Option Discounting |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Phase transition in PCA with missing data: Reduced signal-to-noise ratio, not sample size! |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Phaseless PCA: Low-Rank Matrix Recovery from Column-wise Phaseless Measurements |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Plug-and-Play Methods Provably Converge with Properly Trained Denoisers |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Poission Subsampled Rényi Differential Privacy |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Policy Certificates: Towards Accountable Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Policy Consolidation for Continual Reinforcement Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Population Based Augmentation: Efficient Learning of Augmentation Policy Schedules |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Position-aware Graph Neural Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Power k-Means Clustering |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Predicate Exchange: Inference with Declarative Knowledge |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Predictor-Corrector Policy Optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Probabilistic Neural Symbolic Models for Interpretable Visual Question Answering |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Probability Functional Descent: A Unifying Perspective on GANs, Variational Inference, and Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Processing Megapixel Images with Deep Attention-Sampling Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Projection onto Minkowski Sums with Application to Constrained Learning |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Projections for Approximate Policy Iteration Algorithms |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Proportionally Fair Clustering |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Provable Guarantees for Gradient-Based Meta-Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Provably Efficient Imitation Learning from Observation Alone |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Provably Efficient Maximum Entropy Exploration |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Provably efficient RL with Rich Observations via Latent State Decoding |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| QTRAN: Learning to Factorize with Transformation for Cooperative Multi-Agent Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Quantifying Generalization in Reinforcement Learning |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Quantile Stein Variational Gradient Descent for Batch Bayesian Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| RaFM: Rank-Aware Factorization Machines |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Rademacher Complexity for Adversarially Robust Generalization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Random Expert Distillation: Imitation Learning via Expert Policy Support Estimation |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Random Function Priors for Correlation Modeling |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Random Matrix Improved Covariance Estimation for a Large Class of Metrics |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
4 |
| Random Shuffling Beats SGD after Finite Epochs |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Random Walks on Hypergraphs with Edge-Dependent Vertex Weights |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Rao-Blackwellized Stochastic Gradients for Discrete Distributions |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Rate Distortion For Model Compression:From Theory To Practice |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Rates of Convergence for Sparse Variational Gaussian Process Regression |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Recurrent Kalman Networks: Factorized Inference in High-Dimensional Deep Feature Spaces |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Recursive Sketches for Modular Deep Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Refined Complexity of PCA with Outliers |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Regret Circuits: Composability of Regret Minimizers |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Regularization in directable environments with application to Tetris |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Rehashing Kernel Evaluation in High Dimensions |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Reinforcement Learning in Configurable Continuous Environments |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Relational Pooling for Graph Representations |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Remember and Forget for Experience Replay |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Repairing without Retraining: Avoiding Disparate Impact with Counterfactual Distributions |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Replica Conditional Sequential Monte Carlo |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| Rethinking Lossy Compression: The Rate-Distortion-Perception Tradeoff |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Revisiting precision recall definition for generative modeling |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Revisiting the Softmax Bellman Operator: New Benefits and New Perspective |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Riemannian adaptive stochastic gradient algorithms on matrix manifolds |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Robust Decision Trees Against Adversarial Examples |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Robust Estimation of Tree Structured Gaussian Graphical Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Robust Inference via Generative Classifiers for Handling Noisy Labels |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Robust Influence Maximization for Hyperparametric Models |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Robust Learning from Untrusted Sources |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Robustly Disentangled Causal Mechanisms: Validating Deep Representations for Interventional Robustness |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Rotation Invariant Householder Parameterization for Bayesian PCA |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| SAGA with Arbitrary Sampling |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| SATNet: Bridging deep learning and logical reasoning using a differentiable satisfiability solver |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
✅ |
4 |
| SELFIE: Refurbishing Unclean Samples for Robust Deep Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| SGD without Replacement: Sharper Rates for General Smooth Convex Functions |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| SGD: General Analysis and Improved Rates |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| SOLAR: Deep Structured Representations for Model-Based Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| SWALP : Stochastic Weight Averaging in Low Precision Training |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
4 |
| Safe Grid Search with Optimal Complexity |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Safe Policy Improvement with Baseline Bootstrapping |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Same, Same But Different: Recovering Neural Network Quantization Error Through Weight Factorization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Sample-Optimal Parametric Q-Learning Using Linearly Additive Features |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Scalable Fair Clustering |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Scalable Learning in Reproducing Kernel Krein Spaces |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Scalable Metropolis-Hastings for Exact Bayesian Inference with Large Datasets |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Scalable Nonparametric Sampling from Multimodal Posteriors with the Posterior Bootstrap |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Scalable Training of Inference Networks for Gaussian-Process Models |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Scale-free adaptive planning for deterministic dynamics & discounted rewards |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Scaling Up Ordinal Embedding: A Landmark Approach |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Screening rules for Lasso with non-convex Sparse Regularizers |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| SelectiveNet: A Deep Neural Network with an Integrated Reject Option |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Self-Attention Generative Adversarial Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Self-Attention Graph Pooling |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Self-Supervised Exploration via Disagreement |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Self-similar Epochs: Value in arrangement |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Semi-Cyclic Stochastic Gradient Descent |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Sensitivity Analysis of Linear Structural Causal Models |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
2 |
| Separating value functions across time-scales |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Sequential Facility Location: Approximate Submodularity and Greedy Algorithm |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Set Transformer: A Framework for Attention-based Permutation-Invariant Neural Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Sever: A Robust Meta-Algorithm for Stochastic Optimization |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
5 |
| Shallow-Deep Networks: Understanding and Mitigating Network Overthinking |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Shape Constraints for Set Functions |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Similarity of Neural Network Representations Revisited |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Simple Black-box Adversarial Attacks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Simple Stochastic Gradient Methods for Non-Smooth Non-Convex Regularized Optimization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Simplifying Graph Convolutional Networks |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Sliced-Wasserstein Flows: Nonparametric Generative Modeling via Optimal Transport and Diffusions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Sorting Out Lipschitz Function Approximation |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Sparse Extreme Multi-label Learning with Oracle Property |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Sparse Multi-Channel Variational Autoencoder for the Joint Analysis of Heterogeneous Data |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Spectral Approximate Inference |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Spectral Clustering of Signed Graphs via Matrix Power Means |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Stable and Fair Classification |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Stable-Predictive Optimistic Counterfactual Regret Minimization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| State-Regularized Recurrent Neural Networks |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| State-Reification Networks: Improving Generalization by Modeling the Distribution of Hidden Representations |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Static Automatic Batching In TensorFlow |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Statistical Foundations of Virtual Democracy |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Statistics and Samples in Distributional Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Stay With Me: Lifetime Maximization Through Heteroscedastic Linear Bandits With Reneging |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Stein Point Markov Chain Monte Carlo |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Stochastic Beams and Where To Find Them: The Gumbel-Top-k Trick for Sampling Sequences Without Replacement |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Stochastic Blockmodels meet Graph Neural Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Stochastic Deep Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Stochastic Gradient Push for Distributed Deep Learning |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Stochastic Iterative Hard Thresholding for Graph-structured Sparsity Optimization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Stochastic Optimization for DC Functions and Non-smooth Non-convex Regularizers with Non-asymptotic Convergence |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Structured agents for physical construction |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Sublinear Space Private Algorithms Under the Sliding Window Model |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Sublinear Time Nearest Neighbor Search over Generalized Weighted Space |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Sublinear quantum algorithms for training linear and kernel-based classifiers |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Submodular Cost Submodular Cover with an Approximate Oracle |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Submodular Maximization beyond Non-negativity: Guarantees, Fast Algorithms, and Applications |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Submodular Observation Selection and Information Gathering for Quadratic Models |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Submodular Streaming in All Its Glory: Tight Approximation, Minimum Memory and Low Adaptive Complexity |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Subspace Robust Wasserstein Distances |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Sum-of-Squares Polynomial Flow |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Supervised Hierarchical Clustering with Exponential Linkage |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Surrogate Losses for Online Learning of Stepsizes in Stochastic Non-Convex Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Switching Linear Dynamics for Variational Bayes Filtering |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Taming MAML: Efficient unbiased meta-reinforcement learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| TapNet: Neural Network Augmented with Task-Adaptive Projection for Few-Shot Learning |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| TarMAC: Targeted Multi-Agent Communication |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Target Tracking for Contextual Bandits: Application to Demand Side Management |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Target-Based Temporal-Difference Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Task-Agnostic Dynamics Priors for Deep Reinforcement Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Teaching a black-box learner |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Temporal Gaussian Mixture Layer for Videos |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Tensor Variable Elimination for Plated Factor Graphs |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
❌ |
4 |
| TensorFuzz: Debugging Neural Networks with Coverage-Guided Fuzzing |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| The Anisotropic Noise in Stochastic Gradient Descent: Its Behavior of Escaping from Sharp Minima and Regularization Effects |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Effect of Network Width on Stochastic Gradient Descent and Generalization: an Empirical Study |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Evolved Transformer |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| The Implicit Fairness Criterion of Unconstrained Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| The Kernel Interaction Trick: Fast Bayesian Discovery of Pairwise Interactions in High Dimensions |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| The Natural Language of Actions |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| The Odds are Odd: A Statistical Test for Detecting Adversarial Examples |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| The Value Function Polytope in Reinforcement Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| The Variational Predictive Natural Gradient |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| The Wasserstein Transform |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| The advantages of multiple classes for reducing overfitting from test set reuse |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| The information-theoretic value of unlabeled data in semi-supervised learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Theoretically Principled Trade-off between Robustness and Accuracy |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| TibGM: A Transferable and Information-Based Graphical Model Approach for Reinforcement Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Tight Kernel Query Complexity of Kernel Ridge Regression and Kernel $k$-means Clustering |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Tighter Problem-Dependent Regret Bounds in Reinforcement Learning without Domain Knowledge using Value Function Bounds |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Topological Data Analysis of Decision Boundaries with Application to Model Selection |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Toward Controlling Discrimination in Online Ad Auctions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Toward Understanding the Importance of Noise in Training Neural Networks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Towards Accurate Model Selection in Deep Unsupervised Domain Adaptation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Towards Understanding Knowledge Distillation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Towards a Deep and Unified Understanding of Deep Neural Models in NLP |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Towards a Unified Analysis of Random Fourier Features |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Trading Redundancy for Communication: Speeding up Distributed SGD for Non-convex Optimization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Traditional and Heavy Tailed Self Regularization in Neural Network Models |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
3 |
| Trainable Decoding of Sets of Sequences for Neural Sequence Models |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Training CNNs with Selective Allocation of Channels |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Training Neural Networks with Local Error Signals |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Training Well-Generalizing Classifiers for Fairness Metrics and Other Data-Dependent Constraints |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Trajectory-Based Off-Policy Deep Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Transfer Learning for Related Reinforcement Learning Tasks via Image-to-Image Translation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Transfer of Samples in Policy Search via Multiple Importance Sampling |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain Adaptation |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Transferable Adversarial Training: A General Approach to Adapting Deep Classifiers |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Transferable Clean-Label Poisoning Attacks on Deep Neural Nets |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Trimming the $\ell_1$ Regularizer: Statistical Analysis, Optimization, and Applications to Deep Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Understanding Geometry of Encoder-Decoder CNNs |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Understanding Impacts of High-Order Loss Approximations and Features in Deep Learning Interpretation |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Understanding MCMC Dynamics as Flows on the Wasserstein Space |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Understanding Priors in Bayesian Neural Networks at the Unit Level |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Understanding and Accelerating Particle-Based Variational Inference |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Understanding and Controlling Memory in Recurrent Neural Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Understanding and Utilizing Deep Neural Networks Trained with Noisy Labels |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Understanding and correcting pathologies in the training of learned optimizers |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Understanding the Impact of Entropy on Policy Optimization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Understanding the Origins of Bias in Word Embeddings |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Uniform Convergence Rate of the Kernel Density Estimator Adaptive to Intrinsic Volume Dimension |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Unifying Orthogonal Monte Carlo Methods |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Universal Multi-Party Poisoning Attacks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Unreproducible Research is Reproducible |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Unsupervised Deep Learning by Neighbourhood Discovery |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Unsupervised Label Noise Modeling and Loss Correction |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Using Pre-Training Can Improve Model Robustness and Uncertainty |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Validating Causal Inference Models via Influence Functions |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Variational Annealing of GANs: A Langevin Perspective |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Variational Implicit Processes |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Variational Inference for sparse network reconstruction from count data |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Variational Laplace Autoencoders |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Variational Russian Roulette for Deep Bayesian Nonparametrics |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Voronoi Boundary Classification: A High-Dimensional Geometric Approach via Weighted Monte Carlo Integration |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
6 |
| Warm-starting Contextual Bandits: Robustly Combining Supervised and Bandit Feedback |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Wasserstein Adversarial Examples via Projected Sinkhorn Iterations |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Wasserstein of Wasserstein Loss for Learning Generative Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Weak Detection of Signal in the Spiked Wigner Model |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Weakly-Supervised Temporal Localization via Occurrence Count Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| What is the Effect of Importance Weighting in Deep Learning? |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| When Samples Are Strategically Selected |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| White-box vs Black-box: Bayes Optimal Strategies for Membership Inference |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Why do Larger Models Generalize Better? A Theoretical Perspective via the XOR Problem |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Width Provably Matters in Optimization for Deep Linear Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Zeno: Distributed Stochastic Gradient Descent with Suspicion-based Fault-tolerance |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Zero-Shot Knowledge Distillation in Deep Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| kernelPSI: a Post-Selection Inference Framework for Nonlinear Variable Selection |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |