regret bound

Adaptive Algorithms for Multi-armed Bandit with Composite and Anonymous Feedback AAAI 2021 Adaptive Approximate Policy Iteration AISTATS 2021 Adaptive Discretization for Adversarial Lipschitz Bandits COLT 2021 Adaptive Learning in Continuous Games: Optimal Regret Bounds and Convergence to Nash Equilibrium COLT 2021 A Domain-Shrinking based Bayesian Optimization Algorithm with Order-Optimal Regret Performance NeurIPS 2021 Adversarial Combinatorial Bandits with General Non-linear Reward Functions ICML 2021 A Gang of Adversarial Bandits NeurIPS 2021 A Hybrid Bandit Framework for Diversified Recommendation AAAI 2021 AI-Assisted Scientific Data Collection with Iterative Human Feedback AAAI 2021 A Kernel-Based Approach to Non-Stationary Reinforcement Learning in Metric Spaces AISTATS 2021 Almost Optimal Anytime Algorithm for Batched Multi-Armed Bandits ICML 2021 An Adaptive News-Driven Method for CVaR-sensitive Online Portfolio Selection in Non-Stationary Financial Markets IJCAI 2021 An Algorithm for Stochastic and Adversarial Bandits with Switching Costs ICML 2021 An Efficient Algorithm For Generalized Linear Bandit: Online Stochastic Gradient Descent and Thompson Sampling AISTATS 2021 An Efficient Pessimistic-Optimistic Algorithm for Stochastic Linear Bandits with General Constraints NeurIPS 2021 An Optimistic Acceleration of AMSGrad for Nonconvex Optimization ACML 2021 A One-Size-Fits-All Solution to Conservative Bandit Problems AAAI 2021 A Parameter-Free Algorithm for Misspecified Linear Contextual Bandits AISTATS 2021 Approximation Theory Based Methods for RKHS Bandits ICML 2021 A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning NeurIPS 2021 Asymptotically Optimal Information-Directed Sampling COLT 2021 Asynchronous Decentralized Online Learning NeurIPS 2021 Bandit Learning with Delayed Impact of Actions NeurIPS 2021 Bandit Phase Retrieval NeurIPS 2021 Bandits with Knapsacks beyond the Worst Case NeurIPS 2021