Learning Theory

Optimism and Delays in Episodic Reinforcement Learning AISTATS 2023 Optimism in Face of a Context:Regret Guarantees for Stochastic Contextual MDP AAAI 2023 Optimistic Rates for Multi-Task Representation Learning NeurIPS 2023 Optimization and Bayes: A Trade-off for Overparameterized Neural Networks NeurIPS 2023 Optimize Planning Heuristics to Rank, not to Estimate Cost-to-Goal NeurIPS 2023 Optimizing Spca-based Continual Learning: A Theoretical Approach ICLR 2023 Oracle-Efficient Smoothed Online Learning for Piecewise Continuous Decision Making COLT 2023 Ordering-based Conditions for Global Convergence of Policy Gradient Methods NeurIPS 2023 Origins of Low-Dimensional Adversarial Perturbations AISTATS 2023 Over-Parameterization Exponentially Slows Down Gradient Descent for Learning a Single Neuron COLT 2023 PAC-Bayesian Generalization Bounds for Adversarial Generative Models ICML 2023 PAC-Bayesian Learning of Optimization Algorithms AISTATS 2023 PAC-Bayesian Offline Contextual Bandits With Guarantees ICML 2023 PAC-Bayesian Spectrally-Normalized Bounds for Adversarially Robust Generalization NeurIPS 2023 PAC Generalization via Invariant Representations ICML 2023 PAC Learning and Stabilizing Hedonic Games: Towards a Unifying Approach. AAAI 2023 PAC-learning for Strategic Classification JMLR 2023 PAC Learning Linear Thresholds from Label Proportions NeurIPS 2023 PAC Learning of Halfspaces with Malicious Noise in Nearly Linear Time AISTATS 2023 PAC Reinforcement Learning for Predictive State Representations ICLR 2023 PAC Verification of Statistical Algorithms COLT 2023 Pareto Regret Analyses in Multi-objective Multi-armed Bandit ICML 2023 Partial-Label Regression AAAI 2023 Partially Observable RL with B-Stability: Unified Structural Condition and Sharp Sample-Efficient Algorithms ICLR 2023 Patch-level Routing in Mixture-of-Experts is Provably Sample-efficient for Convolutional Neural Networks ICML 2023