sample complexity

Learning Mixed Multinomial Logits with Provable Guarantees NeurIPS 2022 Learning Predictions for Algorithms with Predictions NeurIPS 2022 Learning Sparse Fixed-Structure Gaussian Bayesian Networks AISTATS 2022 Learning to Control Linear Systems can be Hard COLT 2022 Learning to Reach, Swim, Walk and Fly in One Trial: Data-Driven Control with Scarce Data and Side Information L4DC 2022 Lie Point Symmetry Data Augmentation for Neural PDE Solvers ICML 2022 Linear Label Ranking with Bounded Noise NeurIPS 2022 Low-Degree Multicalibration COLT 2022 Lyapunov Design for Robust and Efficient Robotic Reinforcement Learning CoRL 2022 MABSplit: Faster Forest Training Using Multi-Armed Bandits NeurIPS 2022 MDPGT: Momentum-Based Decentralized Policy Gradient Tracking AAAI 2022 Metric Entropy Duality and the Sample Complexity of Outcome Indistinguishability ALT 2022 Minimax M-estimation under Adversarial Contamination ICML 2022 Minimax Optimal Algorithms for Fixed-Budget Best Arm Identification NeurIPS 2022 Minimax-Optimal Multi-Agent RL in Markov Games With a Generative Model NeurIPS 2022 Model-based RL with Optimistic Posterior Sampling: Structural Conditions and Sample Complexity NeurIPS 2022 Multi-block-Single-probe Variance Reduced Estimator for Coupled Compositional Optimization NeurIPS 2022 Multiclass Learnability Beyond the PAC Framework: Universal Rates and Partial Concept Classes NeurIPS 2022 Multiple-Play Stochastic Bandits with Shareable Finite-Capacity Arms ICML 2022 Near Instance Optimal Model Selection for Pure Exploration Linear Bandits AISTATS 2022 Near Instance-Optimal PAC Reinforcement Learning for Deterministic MDPs NeurIPS 2022 Nearly Optimal Algorithms for Level Set Estimation AISTATS 2022 Nearly-Tight Bounds for Testing Histogram Distributions NeurIPS 2022 Near-Optimal Collaborative Learning in Bandits NeurIPS 2022 Near-optimal Offline Reinforcement Learning with Linear Representation: Leveraging Variance Information with Pessimism ICLR 2022