sample complexity

Model-Based Multi-Agent RL in Zero-Sum Markov Games with Near-Optimal Sample Complexity NeurIPS 2020 Model-Based Reinforcement Learning with a Generative Model is Minimax Optimal COLT 2020 Momentum-Based Policy Gradient Methods ICML 2020 Near-Optimal Reinforcement Learning with Self-Play NeurIPS 2020 Near-optimal sample complexity bounds for learning Latent $k-$polytopes and applications to Ad-Mixtures ICML 2020 Neural Networks Learning and Memorization with (almost) no Over-Parameterization NeurIPS 2020 Online Planning with Lookahead Policies NeurIPS 2020 Online Sinkhorn: Optimal Transport distances from sample streams NeurIPS 2020 On Reward-Free Reinforcement Learning with Linear Function Approximation NeurIPS 2020 On Suboptimality of Least Squares with Application to Estimation of Convex Bodies COLT 2020 On Testing of Samplers NeurIPS 2020 On the Complexity of Proper Distribution-Free Learning of Linear Classifiers ALT 2020 On the Sample Complexity of Adversarial Multi-Source PAC Learning ICML 2020 On the Sample Complexity of Learning Sum-Product Networks AISTATS 2020 On the Theory of Transfer Learning: The Importance of Task Diversity NeurIPS 2020 Optimal $δ$-Correct Best-Arm Selection for Heavy-Tailed Distributions ALT 2020 Optimal Prediction of the Number of Unseen Species with Multiplicity NeurIPS 2020 PAC learning with stable and private predictions COLT 2020 Pan-Private Uniformity Testing COLT 2020 PC-PG: Policy Cover Directed Exploration for Provable Policy Gradient Learning NeurIPS 2020 Periodic Q-Learning L4DC 2020 Planning in Markov Decision Processes with Gap-Dependent Sample Complexity NeurIPS 2020 Polylogarithmic width suffices for gradient descent to achieve arbitrarily small test error with shallow ReLU networks ICLR 2020 Private Learning of Halfspaces: Simplifying the Construction and Reducing the Sample Complexity NeurIPS 2020 Privately Learning Thresholds: Closing the Exponential Gap COLT 2020