Learning Theory

A Refined Margin Distribution Analysis for Forest Representation Learning NeurIPS 2019 Are sample means in multi-armed bandits positively or negatively biased? NeurIPS 2019 Are we there yet? Manifold identification of gradient-related proximal methods AISTATS 2019 A Robust Non-Clairvoyant Dynamic Mechanism for Contextual Auctions NeurIPS 2019 Artificial Constraints and Hints for Unbounded Online Learning COLT 2019 A Sharper Generalization Bound for Divide-and-Conquer Ridge Regression AAAI 2019 A Sharp Lower Bound for Agnostic Learning with Sample Compression Schemes ALT 2019 A Strongly Asymptotically Optimal Agent in General Environments IJCAI 2019 A Theory of Selective Prediction COLT 2019 A Theory of State Abstraction for Reinforcement Learning AAAI 2019 A tight excess risk bound via a unified PAC-Bayesian–Rademacher–Shtarkov–MDL complexity ALT 2019 A Unified Theory of Early Visual Representations from Retina to Cortex through Anatomically Constrained Deep CNNs ICLR 2019 Average-Case Information Complexity of Learning ALT 2019 Bandit Online Learning with Unknown Delays AISTATS 2019 Batch Policy Learning under Constraints ICML 2019 Batch-Size Independent Regret Bounds for the Combinatorial Multi-Armed Bandit Problem COLT 2019 Beating Stochastic and Adversarial Semi-bandits Optimally and Simultaneously ICML 2019 Be Greedy: How Chromatic Number meets Regret Minimization in Graph Bandits UAI 2019 Belief Change and Non-Monotonic Reasoning Sans Compactness AAAI 2019 Benchmarking Hierarchical Script Knowledge NAACL 2019 Best Arm Identification for Contaminated Bandits JMLR 2019 Best of many worlds: Robust model selection for online supervised learning AISTATS 2019 Better generalization with less data using robust gradient descent ICML 2019 Beyond Least-Squares: Fast Rates for Regularized Empirical Risk Minimization through Self-Concordance COLT 2019 Bias Reduction via End-to-End Shift Learning: Application to Citizen Science AAAI 2019