learning theory

Superpolynomial Lower Bounds for Learning One-Layer Neural Networks using Gradient Descent ICML 2020 Teaching with Limited Information on the Learner’s Behaviour ICML 2020 Theory of Curriculum Learning, with Convex Loss Functions JMLR 2020 Towards Convergence Rate Analysis of Random Forests for Classification NeurIPS 2020 Understanding the Impact of Model Incoherence on Convergence of Incremental SGD with Random Reshuffle ICML 2020 Understanding Why Neural Networks Generalize Well Through GSNR of Parameters ICLR 2020 Universal Equivariant Multilayer Perceptrons ICML 2020 What shapes feature representations? Exploring datasets, architectures, and training NeurIPS 2020 A Generalization Bound for Online Variational Inference ACML 2019 An Exponential Tail Bound for the Deleted Estimate AAAI 2019 An Improved Analysis of Training Over-parameterized Deep Neural Networks NeurIPS 2019 An Information-Theoretic Approach to Minimax Regret in Partial Monitoring COLT 2019 A Refined Margin Distribution Analysis for Forest Representation Learning NeurIPS 2019 A Theory of State Abstraction for Reinforcement Learning AAAI 2019 Can SGD Learn Recurrent Neural Networks with Provable Generalization? NeurIPS 2019 Conference on Learning Theory 2019: Preface COLT 2019 Dimension-Free Error Bounds from Random Projections AAAI 2019 Fast-rate PAC-Bayes Generalization Bounds via Shifted Rademacher Processes NeurIPS 2019 Generalization Bounds in the Predict-then-Optimize Framework NeurIPS 2019 Graph-based Discriminators: Sample Complexity and Expressiveness NeurIPS 2019 Hypothesis Set Stability and Generalization NeurIPS 2019 Iterative Classroom Teaching AAAI 2019 Learning and Generalization in Overparameterized Neural Networks, Going Beyond Two Layers NeurIPS 2019 Learning and Inference for Structured Prediction: A Unifying Perspective IJCAI 2019 Learning to Screen NeurIPS 2019