sample complexity

Learning Graph Neural Networks with Approximate Gradient Descent AAAI 2021 Learning Online Algorithms with Distributional Advice ICML 2021 Learning Optimal Auctions with Correlated Valuations from Samples ICML 2021 Learning Partially Observed Linear Dynamical Systems from Logarithmic Number of Samples L4DC 2021 Learning Robust State Abstractions for Hidden-Parameter Block MDPs ICLR 2021 Learning sparse mixtures of permutations from noisy information COLT 2021 Learning Within an Instance for Designing High-Revenue Combinatorial Auctions IJCAI 2021 Learning with Safety Constraints: Sample Complexity of Reinforcement Learning for Constrained MDPs AAAI 2021 Local policy search with Bayesian optimization NeurIPS 2021 Mean-based Best Arm Identification in Stochastic Bandits under Reward Contamination NeurIPS 2021 Minimax sample complexity for turn-based stochastic game UAI 2021 Model-based micro-data reinforcement learning: what are the crucial model properties and which model to choose? ICLR 2021 Model-Based Reinforcement Learning for Infinite-Horizon Discounted Constrained Markov Decision Processes IJCAI 2021 Model-Free Reinforcement Learning: from Clipped Pseudo-Regret to Sample Complexity ICML 2021 Multi-Armed Bandits with Bounded Arm-Memory: Near-Optimal Guarantees for Best-Arm Identification and Regret Minimization NeurIPS 2021 Multiclass versus Binary Differentially Private PAC Learning NeurIPS 2021 Navigating to the Best Policy in Markov Decision Processes NeurIPS 2021 Nearly Horizon-Free Offline Reinforcement Learning NeurIPS 2021 Nearly Minimax Optimal Reinforcement Learning for Linear Mixture Markov Decision Processes COLT 2021 Near-Optimal Model-Free Reinforcement Learning in Non-Stationary Episodic MDPs ICML 2021 Near-optimal Offline and Streaming Algorithms for Learning Non-Linear Dynamical Systems NeurIPS 2021 Near-Optimal Offline Reinforcement Learning via Double Variance Reduction NeurIPS 2021 Near Optimal Reward-Free Reinforcement Learning ICML 2021 Never Go Full Batch (in Stochastic Convex Optimization) NeurIPS 2021 One for One, or All for All: Equilibria and Optimality of Collaboration in Federated Learning ICML 2021