| Direction Matters: On the Implicit Bias of Stochastic Gradient Descent with Moderate Learning Rate | Nov 4, 2020 | Learning Theory | —Unverified | 0 |
| Geometry Perspective Of Estimating Learning Capability Of Neural Networks | Nov 3, 2020 | Learning Theory | —Unverified | 0 |
| A Learning Theoretic Perspective on Local Explainability | Nov 2, 2020 | BIG-bench Machine LearningInterpretable Machine Learning | —Unverified | 0 |
| Toward Better Generalization Bounds with Locally Elastic Stability | Oct 27, 2020 | Generalization BoundsLearning Theory | —Unverified | 0 |
| Enforcing Interpretability and its Statistical Impacts: Trade-offs between Accuracy and Interpretability | Oct 26, 2020 | Binary ClassificationLearning Theory | —Unverified | 0 |
| Kernel Smoothing, Mean Shift, and Their Learning Theory with Directional Data | Oct 23, 2020 | AstronomyClustering | CodeCode Available | 0 |
| Deep Learning is Singular, and That's Good | Oct 22, 2020 | Deep LearningLearning Theory | CodeCode Available | 0 |
| Regularised Least-Squares Regression with Infinite-Dimensional Output Space | Oct 21, 2020 | Learning Theoryregression | —Unverified | 0 |
| Failures of model-dependent generalization bounds for least-norm interpolation | Oct 16, 2020 | Generalization BoundsLearning Theory | —Unverified | 0 |
| Depth-Width Trade-offs for Neural Networks via Topological Entropy | Oct 15, 2020 | Learning Theory | —Unverified | 0 |
| Learning Theory for Inferring Interaction Kernels in Second-Order Interacting Agent Systems | Oct 8, 2020 | Learning Theory | —Unverified | 0 |
| A Note on High-Probability versus In-Expectation Guarantees of Generalization Bounds in Machine Learning | Oct 6, 2020 | BIG-bench Machine LearningGeneralization Bounds | —Unverified | 0 |
| Improving Few-Shot Learning through Multi-task Representation Learning Theory | Oct 5, 2020 | Continual LearningFew-Shot Learning | CodeCode Available | 0 |
| Benign overfitting in ridge regression | Sep 29, 2020 | Generalization BoundsLearning Theory | —Unverified | 0 |
| A Framework of Learning Through Empirical Gain Maximization | Sep 29, 2020 | Learning Theoryregression | —Unverified | 0 |
| Do Deeper Convolutional Networks Perform Better? | Sep 28, 2020 | Learning Theory | —Unverified | 0 |
| Putting Theory to Work: From Learning Bounds to Meta-Learning Algorithms | Sep 28, 2020 | Few-Shot LearningLearning Theory | —Unverified | 0 |
| Generalized Leverage Score Sampling for Neural Networks | Sep 21, 2020 | Learning Theoryregression | —Unverified | 0 |
| A Principle of Least Action for the Training of Neural Networks | Sep 17, 2020 | Learning Theory | CodeCode Available | 0 |
| Too Much Information Kills Information: A Clustering Perspective | Sep 16, 2020 | ClusteringLearning Theory | —Unverified | 0 |
| Understanding Boolean Function Learnability on Deep Neural Networks: PAC Learning Meets Neurosymbolic Models | Sep 13, 2020 | Combinatorial OptimizationLearning Theory | CodeCode Available | 0 |
| Gradient-based Competitive Learning: Theory | Sep 6, 2020 | ClusteringDeep Clustering | —Unverified | 0 |
| Exploiting Heterogeneity in Operational Neural Networks by Synaptic Plasticity | Aug 21, 2020 | Learning Theory | —Unverified | 0 |
| Optimal Approximations Made Easy | Aug 20, 2020 | Learning Theory | —Unverified | 0 |
| How Powerful are Shallow Neural Networks with Bandlimited Random Weights? | Aug 19, 2020 | Learning Theory | —Unverified | 0 |
