regret bound

Learning Adversarial Low-rank Markov Decision Processes with Unknown Transition and Full-information Feedback NeurIPS 2023 Learning and Collusion in Multi-unit Auctions NeurIPS 2023 Learning in online MDPs: is there a price for handling the communicating case? UAI 2023 Learning Kernelized Contextual Bandits in a Distributed and Asynchronous Environment ICLR 2023 Learning Rate Schedules in the Presence of Distribution Shift ICML 2023 Learning Regions of Interest for Bayesian Optimization with Adaptive Level-Set Estimation ICML 2023 Learning to Bid in Repeated First-Price Auctions with Budgets ICML 2023 Learning to Rank under Multinomial Logit Choice JMLR 2023 Leveraging Demonstrations to Improve Online Learning: Quality Matters ICML 2023 Linear Partial Monitoring for Sequential Decision Making: Algorithms, Regret Bounds and Applications JMLR 2023 Logarithmic regret in communicating MDPs: Leveraging known dynamics with bandits ACML 2023 Logarithmic-Regret Quantum Learning Algorithms for Zero-Sum Games NeurIPS 2023 Lower Bounds for Learning in Revealing POMDPs ICML 2023 Maximize to Explore: One Objective Function Fusing Estimation, Planning, and Exploration NeurIPS 2023 Maximum Average Randomly Sampled: A Scale Free and Non-parametric Algorithm for Stochastic Bandits NeurIPS 2023 Max-Quantile Grouped Infinite-Arm Bandits ALT 2023 Meta-Learning Adversarial Bandit Algorithms NeurIPS 2023 Meta-Learning for Simple Regret Minimization AAAI 2023 Model-Based Reinforcement Learning with Multinomial Logistic Function Approximation AAAI 2023 Model-Free Reinforcement Learning with the Decision-Estimation Coefficient NeurIPS 2023 Multi-Agent Learning with Heterogeneous Linear Contextual Bandits NeurIPS 2023 Multiclass Online Learning and Uniform Convergence COLT 2023 Multi-Objective Online Learning ICLR 2023 Multiplier Bootstrap-based Exploration ICML 2023 Multitask Learning with No Regret: from Improved Confidence Bounds to Active Learning NeurIPS 2023