regret bound

Bandits with many optimal arms NeurIPS 2021 Bandits with partially observable confounded data UAI 2021 Batched Thompson Sampling NeurIPS 2021 Bayesian Optimistic Optimisation with Exponentially Decaying Regret ICML 2021 Bellman-consistent Pessimism for Offline Reinforcement Learning NeurIPS 2021 Best-case lower bounds in online learning NeurIPS 2021 Best Model Identification: A Rested Bandit Formulation ICML 2021 Beyond Bandit Feedback in Online Multiclass Classification NeurIPS 2021 Beyond Value-Function Gaps: Improved Instance-Dependent Regret Bounds for Episodic Reinforcement Learning NeurIPS 2021 Black-Box Control for Linear Dynamical Systems COLT 2021 BooVI: Provably Efficient Bootstrapped Value Iteration NeurIPS 2021 Breaking the Moments Condition Barrier: No-Regret Algorithm for Bandits with Super Heavy-Tailed Payoffs NeurIPS 2021 Breaking the Sample Complexity Barrier to Regret-Optimal Model-Free Reinforcement Learning NeurIPS 2021 Causal Bandits with Unknown Graph Structure NeurIPS 2021 ChaCha for Online AutoML ICML 2021 Collaborative Bayesian Optimization with Fair Regret ICML 2021 Combinatorial Blocking Bandits with Stochastic Delays ICML 2021 Combinatorial Gaussian Process Bandits with Probabilistically Triggered Arms AISTATS 2021 Combinatorial semi-bandit in the non-stationary environment UAI 2021 Communication efficient parallel reinforcement learning UAI 2021 Confidence-Budget Matching for Sequential Budgeted Learning ICML 2021 Contextual Blocking Bandits AISTATS 2021 Continuous Mean-Covariance Bandits NeurIPS 2021 Convex Regularization in Monte-Carlo Tree Search ICML 2021 Cooperative and Stochastic Multi-Player Multi-Armed Bandit: Optimal Regret With Neither Communication Nor Collisions COLT 2021