regret bound

Near-Optimal Representation Learning for Linear Bandits and Linear RL ICML 2021 Neural Pseudo-Label Optimism for the Bank Loan Problem NeurIPS 2021 Non-Exponentially Weighted Aggregation: Regret Bounds for Unbounded Loss Functions ICML 2021 Non-Stationary Off-Policy Optimization AISTATS 2021 No-regret Algorithms for Multi-task Bayesian Optimization AISTATS 2021 No-regret Online Learning over Riemannian Manifolds NeurIPS 2021 No-Regret Reinforcement Learning with Heavy-Tailed Rewards AISTATS 2021 No Regrets for Learning the Prior in Bandits NeurIPS 2021 Observation-Free Attacks on Stochastic Bandits NeurIPS 2021 Offline Contextual Bandits with Overparameterized Models ICML 2021 On Information Gain and Regret Bounds in Gaussian Process Bandits AISTATS 2021 Online Adaptation to Label Distribution Shift NeurIPS 2021 Online A-Optimal Design and Active Linear Regression ICML 2021 Online Control of Unknown Time-Varying Dynamical Systems NeurIPS 2021 Online Convex Optimization with Continuous Switching Constraint NeurIPS 2021 Online DR-Submodular Maximization: Minimizing Regret and Constraint Violation AAAI 2021 Online Forgetting Process for Linear Regression Models AISTATS 2021 Online k-means Clustering AISTATS 2021 Online Learning from Optimal Actions COLT 2021 Online learning in MDPs with linear function approximation and bandit feedback. NeurIPS 2021 Online Learning in Unknown Markov Games ICML 2021 Online Learning with Optimism and Delay ICML 2021 Online Learning with Simple Predictors and a Combinatorial Characterization of Minimax in 0/1 Games COLT 2021 Online Markov Decision Processes with Aggregate Bandit Feedback COLT 2021 Online Model Selection for Reinforcement Learning with Function Approximation AISTATS 2021