regret bound

Learn to Match with No Regret: Reinforcement Learning in Markov Matching Markets NeurIPS 2022 Leveraging the Hints: Adaptive Bidding in Repeated First-Price Auctions NeurIPS 2022 Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits NeurIPS 2022 Linear Bandit Algorithms with Sublinear Time Complexity ICML 2022 Linear Combinatorial Semi-Bandit with Causally Related Rewards IJCAI 2022 Linearizing contextual bandits with latent state dynamics UAI 2022 Lipschitz Bandits with Batched Feedback NeurIPS 2022 Locally Differentially Private Reinforcement Learning for Linear Mixture Markov Decision Processes ACML 2022 Logarithmic Regret for Episodic Continuous-Time Linear-Quadratic Reinforcement Learning over a Finite-Time Horizon JMLR 2022 Luckiness in Multiscale Online Learning NeurIPS 2022 Maillard Sampling: Boltzmann Exploration Done Optimally AISTATS 2022 Making SGD Parameter-Free COLT 2022 Matching in Multi-arm Bandit with Collision NeurIPS 2022 Maximizing and Satisficing in Multi-armed Bandits with Graph Information NeurIPS 2022 Metalearning Linear Bandits by Prior Update AISTATS 2022 Minimax Optimal Algorithms for Fixed-Budget Best Arm Identification NeurIPS 2022 Minimax Regret for Partial Monitoring: Infinite Outcomes and Rustichini’s Regret COLT 2022 Minimax Regret Optimization for Robust Machine Learning under Distribution Shift COLT 2022 Modeling Attrition in Recommender Systems with Departing Bandits AAAI 2022 Movement Penalized Bayesian Optimization with Application to Wind Energy Systems NeurIPS 2022 Multi-Agent Multi-Armed Bandits with Limited Communication JMLR 2022 Multi-Agent Online Optimization with Delays: Asynchronicity, Adaptivity, and Optimism JMLR 2022 Multi-armed Bandit Algorithm against Strategic Replication AISTATS 2022 Multi-Armed Bandit Problem with Temporally-Partitioned Rewards: When Partial Feedback Counts IJCAI 2022 Multiple-Play Stochastic Bandits with Shareable Finite-Capacity Arms ICML 2022