Sample Complexity

Instance-Dependent Bounds for Zeroth-order Lipschitz Optimization with Error Certificates NeurIPS 2021 Learning sparse mixtures of permutations from noisy information COLT 2021 Navigating to the Best Policy in Markov Decision Processes NeurIPS 2021 Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes COLT 2021 On Query-efficient Planning in MDPs under Linear Realizability of the Optimal State-value Function COLT 2021 On the Sample Complexity of Learning under Geometric Stability NeurIPS 2021 Optimal Uniform OPE and Model-based Offline Reinforcement Learning in Time-Homogeneous, Reward-Free and Task-Agnostic Settings NeurIPS 2021 Optimism in Reinforcement Learning with Generalized Linear Function Approximation ICLR 2021 Sample-Efficient Reinforcement Learning for Linearly-Parameterized MDPs with a Generative Model NeurIPS 2021 SGD: The Role of Implicit Regularization, Batch-size and Multiple-epochs NeurIPS 2021 The Sample Complexity of Level Set Approximation AISTATS 2021 The Sample Complexity of Robust Covariance Testing COLT 2021 The Sample Complexity of Teaching by Reinforcement on Q-Learning AAAI 2021 Tighter Expected Generalization Error Bounds via Wasserstein Distance NeurIPS 2021 Weak learning convex sets under normal distributions COLT 2021 What are the Statistical Limits of Offline RL with Linear Function Approximation? ICLR 2021 A Nearly Optimal Variant of the Perceptron Algorithm for the Uniform Distribution on the Unit Sphere COLT 2020 An Improved Analysis of (Variance-Reduced) Policy Gradient and Natural Policy Gradient Methods NeurIPS 2020 A Stochastic Derivative-Free Optimization Method with Importance Sampling: Theory and Learning to Control AAAI 2020 Bounds and Complexity Results for Learning Coalition-Based Interaction Functions in Networked Social Systems AAAI 2020 Efficient Learning of Discrete Graphical Models NeurIPS 2020 Extending Learnability to Auxiliary-Input Cryptographic Primitives and Meta-PAC Learning COLT 2020 Information Theoretic Optimal Learning of Gaussian Graphical Models COLT 2020 Limits on Testing Structural Changes in Ising Models NeurIPS 2020 Model-Based Reinforcement Learning with a Generative Model is Minimax Optimal COLT 2020