| $D^2$: Decentralized Training over Decentralized Data |
✅ |
❌ |
✅ |
❌ |
❌ |
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3 |
| A Boo(n) for Evaluating Architecture Performance |
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✅ |
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2 |
| A Classification-Based Study of Covariate Shift in GAN Distributions |
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✅ |
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2 |
| A Conditional Gradient Framework for Composite Convex Minimization with Applications to Semidefinite Programming |
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3 |
| A Delay-tolerant Proximal-Gradient Algorithm for Distributed Learning |
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4 |
| A Distributed Second-Order Algorithm You Can Trust |
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3 |
| A Fast and Scalable Joint Estimator for Integrating Additional Knowledge in Learning Multiple Related Sparse Gaussian Graphical Models |
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5 |
| A Hierarchical Latent Vector Model for Learning Long-Term Structure in Music |
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3 |
| A Primal-Dual Analysis of Global Optimality in Nonconvex Low-Rank Matrix Recovery |
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2 |
| A Probabilistic Theory of Supervised Similarity Learning for Pointwise ROC Curve Optimization |
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❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| A Progressive Batching L-BFGS Method for Machine Learning |
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4 |
| A Reductions Approach to Fair Classification |
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✅ |
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5 |
| A Robust Approach to Sequential Information Theoretic Planning |
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1 |
| A Semantic Loss Function for Deep Learning with Symbolic Knowledge |
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4 |
| A Simple Stochastic Variance Reduced Algorithm with Fast Convergence Rates |
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3 |
| A Spectral Approach to Gradient Estimation for Implicit Distributions |
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✅ |
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✅ |
3 |
| A Spline Theory of Deep Learning |
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✅ |
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2 |
| A Theoretical Explanation for Perplexing Behaviors of Backpropagation-based Visualizations |
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✅ |
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3 |
| A Two-Step Computation of the Exact GAN Wasserstein Distance |
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❌ |
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3 |
| A Unified Framework for Structured Low-rank Matrix Learning |
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✅ |
✅ |
5 |
| A probabilistic framework for multi-view feature learning with many-to-many associations via neural networks |
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❌ |
✅ |
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❌ |
❌ |
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3 |
| ADMM and Accelerated ADMM as Continuous Dynamical Systems |
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1 |
| Accelerated Spectral Ranking |
✅ |
✅ |
✅ |
❌ |
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✅ |
4 |
| Accelerating Greedy Coordinate Descent Methods |
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❌ |
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❌ |
2 |
| Accelerating Natural Gradient with Higher-Order Invariance |
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❌ |
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❌ |
✅ |
3 |
| Accurate Inference for Adaptive Linear Models |
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✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Accurate Uncertainties for Deep Learning Using Calibrated Regression |
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✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Active Learning with Logged Data |
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✅ |
❌ |
❌ |
✅ |
4 |
| Active Testing: An Efficient and Robust Framework for Estimating Accuracy |
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✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Adafactor: Adaptive Learning Rates with Sublinear Memory Cost |
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✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Adaptive Exploration-Exploitation Tradeoff for Opportunistic Bandits |
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❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Adaptive Sampled Softmax with Kernel Based Sampling |
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❌ |
✅ |
❌ |
❌ |
❌ |
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1 |
| Adaptive Three Operator Splitting |
✅ |
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✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Addressing Function Approximation Error in Actor-Critic Methods |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Adversarial Attack on Graph Structured Data |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Adversarial Distillation of Bayesian Neural Network Posteriors |
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✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Adversarial Learning with Local Coordinate Coding |
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✅ |
❌ |
✅ |
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4 |
| Adversarial Regression with Multiple Learners |
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❌ |
✅ |
❌ |
❌ |
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2 |
| Adversarial Risk and the Dangers of Evaluating Against Weak Attacks |
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❌ |
✅ |
❌ |
❌ |
✅ |
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4 |
| Adversarial Time-to-Event Modeling |
❌ |
✅ |
✅ |
✅ |
❌ |
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3 |
| Adversarially Regularized Autoencoders |
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✅ |
❌ |
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❌ |
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5 |
| Alternating Randomized Block Coordinate Descent |
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❌ |
✅ |
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❌ |
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2 |
| An Algorithmic Framework of Variable Metric Over-Relaxed Hybrid Proximal Extra-Gradient Method |
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✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| An Alternative View: When Does SGD Escape Local Minima? |
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❌ |
✅ |
❌ |
❌ |
❌ |
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2 |
| An Efficient Semismooth Newton based Algorithm for Convex Clustering |
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✅ |
❌ |
✅ |
❌ |
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4 |
| An Efficient, Generalized Bellman Update For Cooperative Inverse Reinforcement Learning |
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❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| An Estimation and Analysis Framework for the Rasch Model |
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❌ |
✅ |
✅ |
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3 |
| An Inference-Based Policy Gradient Method for Learning Options |
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3 |
| An Iterative, Sketching-based Framework for Ridge Regression |
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3 |
| An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
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3 |
| Analysis of Minimax Error Rate for Crowdsourcing and Its Application to Worker Clustering Model |
❌ |
✅ |
✅ |
❌ |
❌ |
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3 |
| Analyzing Uncertainty in Neural Machine Translation |
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✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Analyzing the Robustness of Nearest Neighbors to Adversarial Examples |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Anonymous Walk Embeddings |
✅ |
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✅ |
✅ |
✅ |
❌ |
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6 |
| Approximate Leave-One-Out for Fast Parameter Tuning in High Dimensions |
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✅ |
❌ |
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❌ |
❌ |
❌ |
2 |
| Approximate message passing for amplitude based optimization |
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❌ |
❌ |
❌ |
✅ |
2 |
| Approximation Algorithms for Cascading Prediction Models |
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✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Approximation Guarantees for Adaptive Sampling |
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✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Asynchronous Byzantine Machine Learning (the case of SGD) |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Asynchronous Decentralized Parallel Stochastic Gradient Descent |
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✅ |
❌ |
✅ |
❌ |
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4 |
| Asynchronous Stochastic Quasi-Newton MCMC for Non-Convex Optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
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3 |
| Attention-based Deep Multiple Instance Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
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3 |
| Augment and Reduce: Stochastic Inference for Large Categorical Distributions |
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✅ |
✅ |
❌ |
❌ |
❌ |
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4 |
| Augmented CycleGAN: Learning Many-to-Many Mappings from Unpaired Data |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| AutoPrognosis: Automated Clinical Prognostic Modeling via Bayesian Optimization with Structured Kernel Learning |
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❌ |
✅ |
✅ |
❌ |
❌ |
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3 |
| Automatic Goal Generation for Reinforcement Learning Agents |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
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3 |
| Autoregressive Convolutional Neural Networks for Asynchronous Time Series |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
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5 |
| Autoregressive Quantile Networks for Generative Modeling |
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❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
2 |
| BOCK : Bayesian Optimization with Cylindrical Kernels |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| BOHB: Robust and Efficient Hyperparameter Optimization at Scale |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Bandits with Delayed, Aggregated Anonymous Feedback |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Batch Bayesian Optimization via Multi-objective Acquisition Ensemble for Automated Analog Circuit Design |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Bayesian Coreset Construction via Greedy Iterative Geodesic Ascent |
✅ |
✅ |
❌ |
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❌ |
❌ |
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3 |
| Bayesian Model Selection for Change Point Detection and Clustering |
✅ |
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❌ |
❌ |
❌ |
❌ |
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1 |
| Bayesian Optimization of Combinatorial Structures |
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✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Bayesian Quadrature for Multiple Related Integrals |
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❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Bayesian Uncertainty Estimation for Batch Normalized Deep Networks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
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5 |
| Been There, Done That: Meta-Learning with Episodic Recall |
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❌ |
✅ |
❌ |
❌ |
❌ |
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1 |
| Best Arm Identification in Linear Bandits with Linear Dimension Dependency |
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✅ |
❌ |
❌ |
❌ |
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3 |
| Beyond 1/2-Approximation for Submodular Maximization on Massive Data Streams |
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✅ |
❌ |
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❌ |
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3 |
| Beyond Finite Layer Neural Networks: Bridging Deep Architectures and Numerical Differential Equations |
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❌ |
✅ |
✅ |
❌ |
❌ |
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3 |
| Beyond the One-Step Greedy Approach in Reinforcement Learning |
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❌ |
❌ |
❌ |
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2 |
| Bilevel Programming for Hyperparameter Optimization and Meta-Learning |
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✅ |
✅ |
✅ |
❌ |
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6 |
| Binary Classification with Karmic, Threshold-Quasi-Concave Metrics |
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❌ |
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❌ |
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1 |
| Binary Partitions with Approximate Minimum Impurity |
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✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Black Box FDR |
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❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Black-Box Variational Inference for Stochastic Differential Equations |
✅ |
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✅ |
❌ |
✅ |
❌ |
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5 |
| Black-box Adversarial Attacks with Limited Queries and Information |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
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4 |
| Blind Justice: Fairness with Encrypted Sensitive Attributes |
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✅ |
✅ |
❌ |
❌ |
❌ |
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4 |
| Born Again Neural Networks |
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✅ |
✅ |
❌ |
❌ |
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3 |
| Bounding and Counting Linear Regions of Deep Neural Networks |
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❌ |
✅ |
❌ |
✅ |
✅ |
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4 |
| Bounds on the Approximation Power of Feedforward Neural Networks |
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❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Bucket Renormalization for Approximate Inference |
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3 |
| Budgeted Experiment Design for Causal Structure Learning |
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4 |
| Byzantine-Robust Distributed Learning: Towards Optimal Statistical Rates |
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3 |
| CRAFTML, an Efficient Clustering-based Random Forest for Extreme Multi-label Learning |
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✅ |
✅ |
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4 |
| CRVI: Convex Relaxation for Variational Inference |
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✅ |
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✅ |
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2 |
| Can Deep Reinforcement Learning Solve Erdos-Selfridge-Spencer Games? |
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3 |
| Candidates vs. Noises Estimation for Large Multi-Class Classification Problem |
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✅ |
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❌ |
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5 |
| Canonical Tensor Decomposition for Knowledge Base Completion |
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✅ |
✅ |
✅ |
✅ |
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5 |
| Causal Bandits with Propagating Inference |
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2 |
| Celer: a Fast Solver for the Lasso with Dual Extrapolation |
✅ |
✅ |
✅ |
❌ |
❌ |
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4 |
| Characterizing Implicit Bias in Terms of Optimization Geometry |
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❌ |
❌ |
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1 |
| Characterizing and Learning Equivalence Classes of Causal DAGs under Interventions |
✅ |
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✅ |
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❌ |
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3 |
| Chi-square Generative Adversarial Network |
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✅ |
✅ |
❌ |
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❌ |
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4 |
| Classification from Pairwise Similarity and Unlabeled Data |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
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5 |
| Clipped Action Policy Gradient |
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✅ |
✅ |
❌ |
❌ |
❌ |
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3 |
| Closed-form Marginal Likelihood in Gamma-Poisson Matrix Factorization |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
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3 |
| Clustering Semi-Random Mixtures of Gaussians |
✅ |
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❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| CoVeR: Learning Covariate-Specific Vector Representations with Tensor Decompositions |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Coded Sparse Matrix Multiplication |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Communication-Computation Efficient Gradient Coding |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Comparing Dynamics: Deep Neural Networks versus Glassy Systems |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
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3 |
| Comparison-Based Random Forests |
✅ |
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✅ |
✅ |
❌ |
❌ |
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4 |
| Competitive Caching with Machine Learned Advice |
✅ |
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✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Competitive Multi-agent Inverse Reinforcement Learning with Sub-optimal Demonstrations |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
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2 |
| Compiling Combinatorial Prediction Games |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Composable Planning with Attributes |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Composite Functional Gradient Learning of Generative Adversarial Models |
✅ |
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✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Composite Marginal Likelihood Methods for Random Utility Models |
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❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Compressing Neural Networks using the Variational Information Bottleneck |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Computational Optimal Transport: Complexity by Accelerated Gradient Descent Is Better Than by Sinkhorn’s Algorithm |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Conditional Neural Processes |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Conditional Noise-Contrastive Estimation of Unnormalised Models |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Configurable Markov Decision Processes |
✅ |
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❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Constant-Time Predictive Distributions for Gaussian Processes |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Constrained Interacting Submodular Groupings |
✅ |
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❌ |
✅ |
✅ |
❌ |
✅ |
4 |
| Constraining the Dynamics of Deep Probabilistic Models |
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❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
2 |
| ContextNet: Deep learning for Star Galaxy Classification |
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✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Contextual Graph Markov Model: A Deep and Generative Approach to Graph Processing |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Continual Reinforcement Learning with Complex Synapses |
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❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Continuous and Discrete-time Accelerated Stochastic Mirror Descent for Strongly Convex Functions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Continuous-Time Flows for Efficient Inference and Density Estimation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Convergence guarantees for a class of non-convex and non-smooth optimization problems |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Convergent Tree Backup and Retrace with Function Approximation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Convolutional Imputation of Matrix Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Coordinated Exploration in Concurrent Reinforcement Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Covariate Adjusted Precision Matrix Estimation via Nonconvex Optimization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Crowdsourcing with Arbitrary Adversaries |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Curriculum Learning by Transfer Learning: Theory and Experiments with Deep Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Cut-Pursuit Algorithm for Regularizing Nonsmooth Functionals with Graph Total Variation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| CyCADA: Cycle-Consistent Adversarial Domain Adaptation |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| DCFNet: Deep Neural Network with Decomposed Convolutional Filters |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| DICOD: Distributed Convolutional Coordinate Descent for Convolutional Sparse Coding |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
5 |
| DRACO: Byzantine-resilient Distributed Training via Redundant Gradients |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| DVAE++: Discrete Variational Autoencoders with Overlapping Transformations |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Data Summarization at Scale: A Two-Stage Submodular Approach |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Data-Dependent Stability of Stochastic Gradient Descent |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Decentralized Submodular Maximization: Bridging Discrete and Continuous Settings |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Decomposition of Uncertainty in Bayesian Deep Learning for Efficient and Risk-sensitive Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Decoupled Parallel Backpropagation with Convergence Guarantee |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Decoupling Gradient-Like Learning Rules from Representations |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Deep Asymmetric Multi-task Feature Learning |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Deep Bayesian Nonparametric Tracking |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Deep Density Destructors |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Deep Linear Networks with Arbitrary Loss: All Local Minima Are Global |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Deep Models of Interactions Across Sets |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Deep One-Class Classification |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Deep Predictive Coding Network for Object Recognition |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Deep Reinforcement Learning in Continuous Action Spaces: a Case Study in the Game of Simulated Curling |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Deep Variational Reinforcement Learning for POMDPs |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Deep k-Means: Re-Training and Parameter Sharing with Harder Cluster Assignments for Compressing Deep Convolutions |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Delayed Impact of Fair Machine Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Dependent Relational Gamma Process Models for Longitudinal Networks |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Design of Experiments for Model Discrimination Hybridising Analytical and Data-Driven Approaches |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Detecting and Correcting for Label Shift with Black Box Predictors |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Detecting non-causal artifacts in multivariate linear regression models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| DiCE: The Infinitely Differentiable Monte Carlo Estimator |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Differentiable Abstract Interpretation for Provably Robust Neural Networks |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Differentiable Compositional Kernel Learning for Gaussian Processes |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Differentiable Dynamic Programming for Structured Prediction and Attention |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Differentiable plasticity: training plastic neural networks with backpropagation |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Differentially Private Database Release via Kernel Mean Embeddings |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Differentially Private Identity and Equivalence Testing of Discrete Distributions |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
✅ |
3 |
| Differentially Private Matrix Completion Revisited |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Dimensionality-Driven Learning with Noisy Labels |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Discovering Interpretable Representations for Both Deep Generative and Discriminative Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Discovering and Removing Exogenous State Variables and Rewards for Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Discrete-Continuous Mixtures in Probabilistic Programming: Generalized Semantics and Inference Algorithms |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Disentangled Sequential Autoencoder |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Disentangling by Factorising |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Dissecting Adam: The Sign, Magnitude and Variance of Stochastic Gradients |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Dissipativity Theory for Accelerating Stochastic Variance Reduction: A Unified Analysis of SVRG and Katyusha Using Semidefinite Programs |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Distributed Asynchronous Optimization with Unbounded Delays: How Slow Can You Go? |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Distributed Clustering via LSH Based Data Partitioning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Distributed Nonparametric Regression under Communication Constraints |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Do Outliers Ruin Collaboration? |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Does Distributionally Robust Supervised Learning Give Robust Classifiers? |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Dropout Training, Data-dependent Regularization, and Generalization Bounds |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Dynamic Evaluation of Neural Sequence Models |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Dynamic Regret of Strongly Adaptive Methods |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Dynamical Isometry and a Mean Field Theory of CNNs: How to Train 10,000-Layer Vanilla Convolutional Neural Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Dynamical Isometry and a Mean Field Theory of RNNs: Gating Enables Signal Propagation in Recurrent Neural Networks |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient Bias-Span-Constrained Exploration-Exploitation in Reinforcement Learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Efficient First-Order Algorithms for Adaptive Signal Denoising |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
❌ |
3 |
| Efficient Gradient-Free Variational Inference using Policy Search |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Efficient Model-Based Deep Reinforcement Learning with Variational State Tabulation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Efficient Neural Architecture Search via Parameters Sharing |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Efficient Neural Audio Synthesis |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Efficient and Consistent Adversarial Bipartite Matching |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Efficient end-to-end learning for quantizable representations |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| End-to-End Learning for the Deep Multivariate Probit Model |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| End-to-end Active Object Tracking via Reinforcement Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Entropy-SGD optimizes the prior of a PAC-Bayes bound: Generalization properties of Entropy-SGD and data-dependent priors |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Equivalence of Multicategory SVM and Simplex Cone SVM: Fast Computations and Statistical Theory |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
❌ |
3 |
| Error Compensated Quantized SGD and its Applications to Large-scale Distributed Optimization |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Error Estimation for Randomized Least-Squares Algorithms via the Bootstrap |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Escaping Saddles with Stochastic Gradients |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Essentially No Barriers in Neural Network Energy Landscape |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Estimation of Markov Chain via Rank-Constrained Likelihood |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Explicit Inductive Bias for Transfer Learning with Convolutional Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Exploiting the Potential of Standard Convolutional Autoencoders for Image Restoration by Evolutionary Search |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Exploring Hidden Dimensions in Accelerating Convolutional Neural Networks |
✅ |
❌ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| Extracting Automata from Recurrent Neural Networks Using Queries and Counterexamples |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Extreme Learning to Rank via Low Rank Assumption |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Fair and Diverse DPP-Based Data Summarization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fairness Without Demographics in Repeated Loss Minimization |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Fast Approximate Spectral Clustering for Dynamic Networks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Fast Bellman Updates for Robust MDPs |
✅ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
5 |
| Fast Decoding in Sequence Models Using Discrete Latent Variables |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Fast Gradient-Based Methods with Exponential Rate: A Hybrid Control Framework |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Fast Information-theoretic Bayesian Optimisation |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Fast Maximization of Non-Submodular, Monotonic Functions on the Integer Lattice |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Fast Parametric Learning with Activation Memorization |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Fast Stochastic AUC Maximization with $O(1/n)$-Convergence Rate |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Fast Variance Reduction Method with Stochastic Batch Size |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fast and Sample Efficient Inductive Matrix Completion via Multi-Phase Procrustes Flow |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Fast and Scalable Bayesian Deep Learning by Weight-Perturbation in Adam |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Faster Derivative-Free Stochastic Algorithm for Shared Memory Machines |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Feasible Arm Identification |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Feedback-Based Tree Search for Reinforcement Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Finding Influential Training Samples for Gradient Boosted Decision Trees |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Firing Bandits: Optimizing Crowdfunding |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| First Order Generative Adversarial Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fitting New Speakers Based on a Short Untranscribed Sample |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fixing a Broken ELBO |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Focused Hierarchical RNNs for Conditional Sequence Processing |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Fourier Policy Gradients |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Frank-Wolfe with Subsampling Oracle |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Fully Decentralized Multi-Agent Reinforcement Learning with Networked Agents |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Functional Gradient Boosting based on Residual Network Perception |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| GAIN: Missing Data Imputation using Generative Adversarial Nets |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| GEP-PG: Decoupling Exploration and Exploitation in Deep Reinforcement Learning Algorithms |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Gated Path Planning Networks |
❌ |
❌ |
❌ |
✅ |
❌ |
❌ |
✅ |
2 |
| Generalization without Systematicity: On the Compositional Skills of Sequence-to-Sequence Recurrent Networks |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Generalized Earley Parser: Bridging Symbolic Grammars and Sequence Data for Future Prediction |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Generalized Robust Bayesian Committee Machine for Large-scale Gaussian Process Regression |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Generative Temporal Models with Spatial Memory for Partially Observed Environments |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Geodesic Convolutional Shape Optimization |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
✅ |
3 |
| Geometry Score: A Method For Comparing Generative Adversarial Networks |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Global Convergence of Policy Gradient Methods for the Linear Quadratic Regulator |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Goodness-of-Fit Testing for Discrete Distributions via Stein Discrepancy |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
3 |
| GradNorm: Gradient Normalization for Adaptive Loss Balancing in Deep Multitask Networks |
✅ |
❌ |
✅ |
✅ |
✅ |
✅ |
✅ |
6 |
| Gradient Coding from Cyclic MDS Codes and Expander Graphs |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Gradient Descent Learns One-hidden-layer CNN: Don’t be Afraid of Spurious Local Minima |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Gradient Descent for Sparse Rank-One Matrix Completion for Crowd-Sourced Aggregation of Sparsely Interacting Workers |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Gradient Primal-Dual Algorithm Converges to Second-Order Stationary Solution for Nonconvex Distributed Optimization Over Networks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Gradient descent with identity initialization efficiently learns positive definite linear transformations by deep residual networks |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
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1 |
| Gradient-Based Meta-Learning with Learned Layerwise Metric and Subspace |
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3 |
| Gradually Updated Neural Networks for Large-Scale Image Recognition |
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5 |
| Graph Networks as Learnable Physics Engines for Inference and Control |
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3 |
| GraphRNN: Generating Realistic Graphs with Deep Auto-regressive Models |
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3 |
| Graphical Nonconvex Optimization via an Adaptive Convex Relaxation |
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2 |
| Greed is Still Good: Maximizing Monotone Submodular+Supermodular (BP) Functions |
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2 |
| Hierarchical Clustering with Structural Constraints |
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2 |
| Hierarchical Deep Generative Models for Multi-Rate Multivariate Time Series |
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4 |
| Hierarchical Imitation and Reinforcement Learning |
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3 |
| Hierarchical Long-term Video Prediction without Supervision |
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4 |
| Hierarchical Multi-Label Classification Networks |
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3 |
| Hierarchical Text Generation and Planning for Strategic Dialogue |
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2 |
| High Performance Zero-Memory Overhead Direct Convolutions |
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3 |
| High-Quality Prediction Intervals for Deep Learning: A Distribution-Free, Ensembled Approach |
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4 |
| Hyperbolic Entailment Cones for Learning Hierarchical Embeddings |
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4 |
| IMPALA: Scalable Distributed Deep-RL with Importance Weighted Actor-Learner Architectures |
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3 |
| INSPECTRE: Privately Estimating the Unseen |
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3 |
| Image Transformer |
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5 |
| Implicit Quantile Networks for Distributional Reinforcement Learning |
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2 |
| Implicit Regularization in Nonconvex Statistical Estimation: Gradient Descent Converges Linearly for Phase Retrieval and Matrix Completion |
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2 |
| Importance Weighted Transfer of Samples in Reinforcement Learning |
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3 |
| Improved Regret Bounds for Thompson Sampling in Linear Quadratic Control Problems |
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2 |
| Improved Training of Generative Adversarial Networks Using Representative Features |
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2 |
| Improved large-scale graph learning through ridge spectral sparsification |
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3 |
| Improved nearest neighbor search using auxiliary information and priority functions |
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3 |
| Improving Optimization for Models With Continuous Symmetry Breaking |
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4 |
| Improving Regression Performance with Distributional Losses |
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2 |
| Improving Sign Random Projections With Additional Information |
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3 |
| Improving the Gaussian Mechanism for Differential Privacy: Analytical Calibration and Optimal Denoising |
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4 |
| Improving the Privacy and Accuracy of ADMM-Based Distributed Algorithms |
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3 |
| Inductive Two-Layer Modeling with Parametric Bregman Transfer |
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2 |
| Inference Suboptimality in Variational Autoencoders |
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2 |
| Information Theoretic Guarantees for Empirical Risk Minimization with Applications to Model Selection and Large-Scale Optimization |
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3 |
| Inter and Intra Topic Structure Learning with Word Embeddings |
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4 |
| Interpretability Beyond Feature Attribution: Quantitative Testing with Concept Activation Vectors (TCAV) |
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1 |
| Invariance of Weight Distributions in Rectified MLPs |
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2 |
| Investigating Human Priors for Playing Video Games |
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2 |
| Is Generator Conditioning Causally Related to GAN Performance? |
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3 |
| Iterative Amortized Inference |
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5 |
| JointGAN: Multi-Domain Joint Distribution Learning with Generative Adversarial Nets |
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3 |
| Junction Tree Variational Autoencoder for Molecular Graph Generation |
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3 |
| K-Beam Minimax: Efficient Optimization for Deep Adversarial Learning |
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5 |
| K-means clustering using random matrix sparsification |
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3 |
| Katyusha X: Simple Momentum Method for Stochastic Sum-of-Nonconvex Optimization |
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1 |
| Kernel Recursive ABC: Point Estimation with Intractable Likelihood |
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4 |
| Kernelized Synaptic Weight Matrices |
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4 |
| Knowledge Transfer with Jacobian Matching |
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1 |
| Kronecker Recurrent Units |
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3 |
| LaVAN: Localized and Visible Adversarial Noise |
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3 |
| Large-Scale Cox Process Inference using Variational Fourier Features |
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3 |
| Large-Scale Sparse Inverse Covariance Estimation via Thresholding and Max-Det Matrix Completion |
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6 |
| Latent Space Policies for Hierarchical Reinforcement Learning |
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3 |
| LeapsAndBounds: A Method for Approximately Optimal Algorithm Configuration |
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5 |
| Learn from Your Neighbor: Learning Multi-modal Mappings from Sparse Annotations |
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3 |
| Learning Adversarially Fair and Transferable Representations |
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4 |
| Learning Binary Latent Variable Models: A Tensor Eigenpair Approach |
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3 |
| Learning Compact Neural Networks with Regularization |
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1 |
| Learning Continuous Hierarchies in the Lorentz Model of Hyperbolic Geometry |
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3 |
| Learning Deep ResNet Blocks Sequentially using Boosting Theory |
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5 |
| Learning Diffusion using Hyperparameters |
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2 |
| Learning Dynamics of Linear Denoising Autoencoders |
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3 |
| Learning Equations for Extrapolation and Control |
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3 |
| Learning Hidden Markov Models from Pairwise Co-occurrences with Application to Topic Modeling |
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3 |
| Learning Implicit Generative Models with the Method of Learned Moments |
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3 |
| Learning Independent Causal Mechanisms |
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3 |
| Learning K-way D-dimensional Discrete Codes for Compact Embedding Representations |
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4 |
| Learning Localized Spatio-Temporal Models From Streaming Data |
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4 |
| Learning Long Term Dependencies via Fourier Recurrent Units |
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3 |
| Learning Longer-term Dependencies in RNNs with Auxiliary Losses |
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3 |
| Learning Low-Dimensional Temporal Representations |
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4 |
| Learning Maximum-A-Posteriori Perturbation Models for Structured Prediction in Polynomial Time |
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2 |
| Learning Memory Access Patterns |
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2 |
| Learning One Convolutional Layer with Overlapping Patches |
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2 |
| Learning Policy Representations in Multiagent Systems |
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3 |
| Learning Registered Point Processes from Idiosyncratic Observations |
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2 |
| Learning Representations and Generative Models for 3D Point Clouds |
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4 |
| Learning Semantic Representations for Unsupervised Domain Adaptation |
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4 |
| Learning Steady-States of Iterative Algorithms over Graphs |
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5 |
| Learning a Mixture of Two Multinomial Logits |
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| Learning and Memorization |
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2 |
| Learning by Playing Solving Sparse Reward Tasks from Scratch |
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1 |
| Learning in Integer Latent Variable Models with Nested Automatic Differentiation |
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3 |
| Learning in Reproducing Kernel Kreı̆n Spaces |
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3 |
| Learning the Reward Function for a Misspecified Model |
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2 |
| Learning to Act in Decentralized Partially Observable MDPs |
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4 |
| Learning to Branch |
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5 |
| Learning to Coordinate with Coordination Graphs in Repeated Single-Stage Multi-Agent Decision Problems |
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3 |
| Learning to Explain: An Information-Theoretic Perspective on Model Interpretation |
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4 |
| Learning to Explore via Meta-Policy Gradient |
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4 |
| Learning to Optimize Combinatorial Functions |
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2 |
| Learning to Reweight Examples for Robust Deep Learning |
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4 |
| Learning to Speed Up Structured Output Prediction |
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4 |
| Learning to search with MCTSnets |
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1 |
| Learning unknown ODE models with Gaussian processes |
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4 |
| Learning with Abandonment |
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2 |
| Least-Squares Temporal Difference Learning for the Linear Quadratic Regulator |
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2 |
| Let’s be Honest: An Optimal No-Regret Framework for Zero-Sum Games |
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1 |
| Level-Set Methods for Finite-Sum Constrained Convex Optimization |
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3 |
| Leveraging Well-Conditioned Bases: Streaming and Distributed Summaries in Minkowski $p$-Norms |
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4 |
| Lightweight Stochastic Optimization for Minimizing Finite Sums with Infinite Data |
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3 |
| Limits of Estimating Heterogeneous Treatment Effects: Guidelines for Practical Algorithm Design |
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3 |
| Linear Spectral Estimators and an Application to Phase Retrieval |
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2 |
| Lipschitz Continuity in Model-based Reinforcement Learning |
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3 |
| Local Convergence Properties of SAGA/Prox-SVRG and Acceleration |
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3 |
| Local Density Estimation in High Dimensions |
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3 |
| Local Private Hypothesis Testing: Chi-Square Tests |
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2 |
| Locally Private Hypothesis Testing |
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1 |
| Loss Decomposition for Fast Learning in Large Output Spaces |
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3 |
| Low-Rank Riemannian Optimization on Positive Semidefinite Stochastic Matrices with Applications to Graph Clustering |
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3 |
| Lyapunov Functions for First-Order Methods: Tight Automated Convergence Guarantees |
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2 |
| MAGAN: Aligning Biological Manifolds |
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2 |
| MISSION: Ultra Large-Scale Feature Selection using Count-Sketches |
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5 |
| MSplit LBI: Realizing Feature Selection and Dense Estimation Simultaneously in Few-shot and Zero-shot Learning |
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3 |
| Machine Theory of Mind |
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| Make the Minority Great Again: First-Order Regret Bound for Contextual Bandits |
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1 |
| Markov Modulated Gaussian Cox Processes for Semi-Stationary Intensity Modeling of Events Data |
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3 |
| Massively Parallel Algorithms and Hardness for Single-Linkage Clustering under $\ell_p$ Distances |
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4 |
| Matrix Norms in Data Streams: Faster, Multi-Pass and Row-Order |
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| Max-Mahalanobis Linear Discriminant Analysis Networks |
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4 |
| Mean Field Multi-Agent Reinforcement Learning |
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3 |
| Measuring abstract reasoning in neural networks |
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4 |
| MentorNet: Learning Data-Driven Curriculum for Very Deep Neural Networks on Corrupted Labels |
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4 |
| Message Passing Stein Variational Gradient Descent |
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3 |
| Meta-Learning by Adjusting Priors Based on Extended PAC-Bayes Theory |
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4 |
| Minibatch Gibbs Sampling on Large Graphical Models |
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2 |
| Minimal I-MAP MCMC for Scalable Structure Discovery in Causal DAG Models |
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3 |
| Minimax Concave Penalized Multi-Armed Bandit Model with High-Dimensional Covariates |
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3 |
| Mitigating Bias in Adaptive Data Gathering via Differential Privacy |
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1 |
| Mix & Match Agent Curricula for Reinforcement Learning |
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1 |
| Mixed batches and symmetric discriminators for GAN training |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Model-Level Dual Learning |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Modeling Others using Oneself in Multi-Agent Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
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✅ |
3 |
| Modeling Sparse Deviations for Compressed Sensing using Generative Models |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| More Robust Doubly Robust Off-policy Evaluation |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Multi-Fidelity Black-Box Optimization with Hierarchical Partitions |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Multicalibration: Calibration for the (Computationally-Identifiable) Masses |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Mutual Information Neural Estimation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Near Optimal Frequent Directions for Sketching Dense and Sparse Matrices |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Nearly Optimal Robust Subspace Tracking |
✅ |
✅ |
❌ |
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❌ |
❌ |
✅ |
3 |
| NetGAN: Generating Graphs via Random Walks |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Network Global Testing by Counting Graphlets |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Neural Autoregressive Flows |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Neural Dynamic Programming for Musical Self Similarity |
✅ |
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✅ |
✅ |
❌ |
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✅ |
4 |
| Neural Inverse Rendering for General Reflectance Photometric Stereo |
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✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Neural Networks Should Be Wide Enough to Learn Disconnected Decision Regions |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Neural Program Synthesis from Diverse Demonstration Videos |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
2 |
| Neural Relational Inference for Interacting Systems |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Noise2Noise: Learning Image Restoration without Clean Data |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Noisin: Unbiased Regularization for Recurrent Neural Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Noisy Natural Gradient as Variational Inference |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Non-convex Conditional Gradient Sliding |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Non-linear motor control by local learning in spiking neural networks |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Nonconvex Optimization for Regression with Fairness Constraints |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Nonoverlap-Promoting Variable Selection |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Nonparametric Regression with Comparisons: Escaping the Curse of Dimensionality with Ordinal Information |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Nonparametric variable importance using an augmented neural network with multi-task learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Not All Samples Are Created Equal: Deep Learning with Importance Sampling |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Not to Cry Wolf: Distantly Supervised Multitask Learning in Critical Care |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Obfuscated Gradients Give a False Sense of Security: Circumventing Defenses to Adversarial Examples |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| On Acceleration with Noise-Corrupted Gradients |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| On Learning Sparsely Used Dictionaries from Incomplete Samples |
✅ |
✅ |
❌ |
❌ |
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❌ |
✅ |
3 |
| On Matching Pursuit and Coordinate Descent |
✅ |
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✅ |
❌ |
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❌ |
✅ |
3 |
| On Nesting Monte Carlo Estimators |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Generalization of Equivariance and Convolution in Neural Networks to the Action of Compact Groups |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Implicit Bias of Dropout |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| On the Limitations of First-Order Approximation in GAN Dynamics |
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❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| On the Optimization of Deep Networks: Implicit Acceleration by Overparameterization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| On the Power of Over-parametrization in Neural Networks with Quadratic Activation |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| On the Relationship between Data Efficiency and Error for Uncertainty Sampling |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| On the Spectrum of Random Features Maps of High Dimensional Data |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| One-Shot Segmentation in Clutter |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Online Convolutional Sparse Coding with Sample-Dependent Dictionary |
✅ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Online Learning with Abstention |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Online Linear Quadratic Control |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Open Category Detection with PAC Guarantees |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Optimal Distributed Learning with Multi-pass Stochastic Gradient Methods |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Optimal Rates of Sketched-regularized Algorithms for Least-Squares Regression over Hilbert Spaces |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Optimal Tuning for Divide-and-conquer Kernel Ridge Regression with Massive Data |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Optimization Landscape and Expressivity of Deep CNNs |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Optimization, fast and slow: optimally switching between local and Bayesian optimization |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Optimizing the Latent Space of Generative Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Orthogonal Machine Learning: Power and Limitations |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
✅ |
3 |
| Orthogonal Recurrent Neural Networks with Scaled Cayley Transform |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Orthogonality-Promoting Distance Metric Learning: Convex Relaxation and Theoretical Analysis |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Out-of-sample extension of graph adjacency spectral embedding |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Overcoming Catastrophic Forgetting with Hard Attention to the Task |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| PDE-Net: Learning PDEs from Data |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| PIPPS: Flexible Model-Based Policy Search Robust to the Curse of Chaos |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Parallel Bayesian Network Structure Learning |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
3 |
| Parallel WaveNet: Fast High-Fidelity Speech Synthesis |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Parallel and Streaming Algorithms for K-Core Decomposition |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Parameterized Algorithms for the Matrix Completion Problem |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Partial Optimality and Fast Lower Bounds for Weighted Correlation Clustering |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Path Consistency Learning in Tsallis Entropy Regularized MDPs |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Path-Level Network Transformation for Efficient Architecture Search |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Pathwise Derivatives Beyond the Reparameterization Trick |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
❌ |
3 |
| PixelSNAIL: An Improved Autoregressive Generative Model |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Policy Optimization as Wasserstein Gradient Flows |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Policy Optimization with Demonstrations |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Policy and Value Transfer in Lifelong Reinforcement Learning |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Practical Contextual Bandits with Regression Oracles |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| PredRNN++: Towards A Resolution of the Deep-in-Time Dilemma in Spatiotemporal Predictive Learning |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Predict and Constrain: Modeling Cardinality in Deep Structured Prediction |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Prediction Rule Reshaping |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Probabilistic Boolean Tensor Decomposition |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Probabilistic Recurrent State-Space Models |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Probably Approximately Metric-Fair Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Problem Dependent Reinforcement Learning Bounds Which Can Identify Bandit Structure in MDPs |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Programmatically Interpretable Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Progress & Compress: A scalable framework for continual learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Projection-Free Online Optimization with Stochastic Gradient: From Convexity to Submodularity |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Proportional Allocation: Simple, Distributed, and Diverse Matching with High Entropy |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Provable Defenses against Adversarial Examples via the Convex Outer Adversarial Polytope |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Provable Variable Selection for Streaming Features |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Pseudo-task Augmentation: From Deep Multitask Learning to Intratask Sharing—and Back |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| QMIX: Monotonic Value Function Factorisation for Deep Multi-Agent Reinforcement Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| QuantTree: Histograms for Change Detection in Multivariate Data Streams |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Quasi-Monte Carlo Variational Inference |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Quickshift++: Provably Good Initializations for Sample-Based Mean Shift |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| RLlib: Abstractions for Distributed Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Racing Thompson: an Efficient Algorithm for Thompson Sampling with Non-conjugate Priors |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| RadialGAN: Leveraging multiple datasets to improve target-specific predictive models using Generative Adversarial Networks |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Randomized Block Cubic Newton Method |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Ranking Distributions based on Noisy Sorting |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
3 |
| Rapid Adaptation with Conditionally Shifted Neurons |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
2 |
| Rates of Convergence of Spectral Methods for Graphon Estimation |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Rectify Heterogeneous Models with Semantic Mapping |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Recurrent Predictive State Policy Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Regret Minimization for Partially Observable Deep Reinforcement Learning |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Reinforcement Learning with Function-Valued Action Spaces for Partial Differential Equation Control |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Reinforcing Adversarial Robustness using Model Confidence Induced by Adversarial Training |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Representation Learning on Graphs with Jumping Knowledge Networks |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Representation Tradeoffs for Hyperbolic Embeddings |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Residual Unfairness in Fair Machine Learning from Prejudiced Data |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Revealing Common Statistical Behaviors in Heterogeneous Populations |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Reviving and Improving Recurrent Back-Propagation |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Riemannian Stochastic Recursive Gradient Algorithm |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Robust and Scalable Models of Microbiome Dynamics |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| SADAGRAD: Strongly Adaptive Stochastic Gradient Methods |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| SAFFRON: an Adaptive Algorithm for Online Control of the False Discovery Rate |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| SBEED: Convergent Reinforcement Learning with Nonlinear Function Approximation |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| SGD and Hogwild! Convergence Without the Bounded Gradients Assumption |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| SMAC: Simultaneous Mapping and Clustering Using Spectral Decompositions |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| SQL-Rank: A Listwise Approach to Collaborative Ranking |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Safe Element Screening for Submodular Function Minimization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Scalable Bilinear Pi Learning Using State and Action Features |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Scalable Deletion-Robust Submodular Maximization: Data Summarization with Privacy and Fairness Constraints |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Scalable Gaussian Processes with Grid-Structured Eigenfunctions (GP-GRIEF) |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Scalable approximate Bayesian inference for particle tracking data |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Selecting Representative Examples for Program Synthesis |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Self-Bounded Prediction Suffix Tree via Approximate String Matching |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Self-Consistent Trajectory Autoencoder: Hierarchical Reinforcement Learning with Trajectory Embeddings |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
2 |
| Self-Imitation Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Semi-Amortized Variational Autoencoders |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Semi-Implicit Variational Inference |
✅ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
5 |
| Semi-Supervised Learning on Data Streams via Temporal Label Propagation |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Semi-Supervised Learning via Compact Latent Space Clustering |
✅ |
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✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Semiparametric Contextual Bandits |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| Shampoo: Preconditioned Stochastic Tensor Optimization |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Signal and Noise Statistics Oblivious Orthogonal Matching Pursuit |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Smoothed Action Value Functions for Learning Gaussian Policies |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Soft Actor-Critic: Off-Policy Maximum Entropy Deep Reinforcement Learning with a Stochastic Actor |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Solving Partial Assignment Problems using Random Clique Complexes |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Sound Abstraction and Decomposition of Probabilistic Programs |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
❌ |
2 |
| SparseMAP: Differentiable Sparse Structured Inference |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Spatio-temporal Bayesian On-line Changepoint Detection with Model Selection |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Spectrally Approximating Large Graphs with Smaller Graphs |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Spline Filters For End-to-End Deep Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Spotlight: Optimizing Device Placement for Training Deep Neural Networks |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Spurious Local Minima are Common in Two-Layer ReLU Neural Networks |
❌ |
✅ |
❌ |
❌ |
✅ |
✅ |
✅ |
4 |
| Stability and Generalization of Learning Algorithms that Converge to Global Optima |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Stabilizing Gradients for Deep Neural Networks via Efficient SVD Parameterization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Stagewise Safe Bayesian Optimization with Gaussian Processes |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| State Abstractions for Lifelong Reinforcement Learning |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| State Space Gaussian Processes with Non-Gaussian Likelihood |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
✅ |
7 |
| Stein Points |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Stein Variational Gradient Descent Without Gradient |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Stein Variational Message Passing for Continuous Graphical Models |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Stochastic PCA with $\ell_2$ and $\ell_1$ Regularization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Stochastic Proximal Algorithms for AUC Maximization |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Stochastic Training of Graph Convolutional Networks with Variance Reduction |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Stochastic Variance-Reduced Cubic Regularized Newton Methods |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Stochastic Variance-Reduced Hamilton Monte Carlo Methods |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Stochastic Variance-Reduced Policy Gradient |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Stochastic Video Generation with a Learned Prior |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Stochastic Wasserstein Barycenters |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| StrassenNets: Deep Learning with a Multiplication Budget |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Streaming Principal Component Analysis in Noisy Setting |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Stronger Generalization Bounds for Deep Nets via a Compression Approach |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Structured Control Nets for Deep Reinforcement Learning |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| Structured Evolution with Compact Architectures for Scalable Policy Optimization |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Structured Output Learning with Abstention: Application to Accurate Opinion Prediction |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Structured Variational Learning of Bayesian Neural Networks with Horseshoe Priors |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Structured Variationally Auto-encoded Optimization |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Style Tokens: Unsupervised Style Modeling, Control and Transfer in End-to-End Speech Synthesis |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Submodular Hypergraphs: p-Laplacians, Cheeger Inequalities and Spectral Clustering |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Subspace Embedding and Linear Regression with Orlicz Norm |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Synthesizing Programs for Images using Reinforced Adversarial Learning |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Synthesizing Robust Adversarial Examples |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| TACO: Learning Task Decomposition via Temporal Alignment for Control |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| TAPAS: Tricks to Accelerate (encrypted) Prediction As a Service |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
5 |
| Tempered Adversarial Networks |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Temporal Poisson Square Root Graphical Models |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Testing Sparsity over Known and Unknown Bases |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| The Dynamics of Learning: A Random Matrix Approach |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Edge Density Barrier: Computational-Statistical Tradeoffs in Combinatorial Inference |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| The Generalization Error of Dictionary Learning with Moreau Envelopes |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| The Hidden Vulnerability of Distributed Learning in Byzantium |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| The Hierarchical Adaptive Forgetting Variational Filter |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
✅ |
2 |
| The Limits of Maxing, Ranking, and Preference Learning |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| The Mechanics of n-Player Differentiable Games |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
3 |
| The Mirage of Action-Dependent Baselines in Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| The Multilinear Structure of ReLU Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| The Power of Interpolation: Understanding the Effectiveness of SGD in Modern Over-parametrized Learning |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
3 |
| The Uncertainty Bellman Equation and Exploration |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| The Weighted Kendall and High-order Kernels for Permutations |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| The Well-Tempered Lasso |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Theoretical Analysis of Image-to-Image Translation with Adversarial Learning |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Theoretical Analysis of Sparse Subspace Clustering with Missing Entries |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
1 |
| Thompson Sampling for Combinatorial Semi-Bandits |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Tight Regret Bounds for Bayesian Optimization in One Dimension |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Tighter Variational Bounds are Not Necessarily Better |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Time Limits in Reinforcement Learning |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| To Understand Deep Learning We Need to Understand Kernel Learning |
❌ |
❌ |
✅ |
❌ |
✅ |
❌ |
❌ |
2 |
| Topological mixture estimation |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
❌ |
3 |
| Towards Binary-Valued Gates for Robust LSTM Training |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Towards Black-box Iterative Machine Teaching |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Towards End-to-End Prosody Transfer for Expressive Speech Synthesis with Tacotron |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Towards Fast Computation of Certified Robustness for ReLU Networks |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Towards More Efficient Stochastic Decentralized Learning: Faster Convergence and Sparse Communication |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Trainable Calibration Measures for Neural Networks from Kernel Mean Embeddings |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Training Neural Machines with Trace-Based Supervision |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
✅ |
2 |
| Transfer Learning via Learning to Transfer |
❌ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
3 |
| Transfer in Deep Reinforcement Learning Using Successor Features and Generalised Policy Improvement |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
2 |
| Transformation Autoregressive Networks |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Tree Edit Distance Learning via Adaptive Symbol Embeddings |
❌ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
5 |
| Tropical Geometry of Deep Neural Networks |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Unbiased Objective Estimation in Predictive Optimization |
✅ |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
5 |
| Understanding Generalization and Optimization Performance of Deep CNNs |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Understanding and Simplifying One-Shot Architecture Search |
❌ |
❌ |
✅ |
✅ |
✅ |
❌ |
✅ |
4 |
| Understanding the Loss Surface of Neural Networks for Binary Classification |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Universal Planning Networks: Learning Generalizable Representations for Visuomotor Control |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Using Inherent Structures to design Lean 2-layer RBMs |
❌ |
✅ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Using Reward Machines for High-Level Task Specification and Decomposition in Reinforcement Learning |
✅ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
4 |
| Variable Selection via Penalized Neural Network: a Drop-Out-One Loss Approach |
✅ |
❌ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Variance Regularized Counterfactual Risk Minimization via Variational Divergence Minimization |
✅ |
✅ |
✅ |
✅ |
✅ |
❌ |
✅ |
6 |
| Variational Bayesian dropout: pitfalls and fixes |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
0 |
| Variational Inference and Model Selection with Generalized Evidence Bounds |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Variational Network Inference: Strong and Stable with Concrete Support |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
2 |
| Video Prediction with Appearance and Motion Conditions |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |
| Visualizing and Understanding Atari Agents |
❌ |
✅ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| WHInter: A Working set algorithm for High-dimensional sparse second order Interaction models |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| WSNet: Compact and Efficient Networks Through Weight Sampling |
❌ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
4 |
| Weakly Consistent Optimal Pricing Algorithms in Repeated Posted-Price Auctions with Strategic Buyer |
✅ |
❌ |
❌ |
❌ |
❌ |
❌ |
❌ |
1 |
| Weakly Submodular Maximization Beyond Cardinality Constraints: Does Randomization Help Greedy? |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| Weightless: Lossy weight encoding for deep neural network compression |
✅ |
❌ |
✅ |
❌ |
✅ |
❌ |
✅ |
4 |
| Which Training Methods for GANs do actually Converge? |
❌ |
❌ |
✅ |
❌ |
❌ |
❌ |
❌ |
1 |
| Yes, but Did It Work?: Evaluating Variational Inference |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| oi-VAE: Output Interpretable VAEs for Nonlinear Group Factor Analysis |
✅ |
❌ |
✅ |
❌ |
❌ |
❌ |
✅ |
3 |
| prDeep: Robust Phase Retrieval with a Flexible Deep Network |
❌ |
✅ |
✅ |
❌ |
✅ |
✅ |
✅ |
5 |
| signSGD: Compressed Optimisation for Non-Convex Problems |
✅ |
✅ |
✅ |
✅ |
❌ |
❌ |
✅ |
5 |