regret bound

Improved Online Confidence Bounds for Multinomial Logistic Bandits ICML 2025 Improved Regret Bounds for Online Fair Division with Bandit Learning AAAI 2025 Infinite-Horizon Reinforcement Learning with Multinomial Logit Function Approximation AISTATS 2025 Instance-Dependent Regret Bounds for Learning Two-Player Zero-Sum Games with Bandit Feedback COLT 2025 Is Prior-Free Black-Box Non-Stationary Reinforcement Learning Feasible? AISTATS 2025 Lasso Bandit with Compatibility Condition on Optimal Arm ICLR 2025 LC-Tsallis-INF: Generalized Best-of-Both-Worlds Linear Contextual Bandits AISTATS 2025 Learning from Imperfect Human Feedback: A Tale from Corruption-Robust Dueling ICLR 2025 Learning Infinite-Horizon Average-Reward Linear Mixture MDPs of Bounded Span AISTATS 2025 Learning the Pareto Front Using Bootstrapped Observation Samples AISTATS 2025 Learning with contextual information in non-stationary environments L4DC 2025 Lexicographic Lipschitz Bandits: New Algorithms and a Lower Bound JMLR 2025 Linear Contextual Bandits With Interference ICML 2025 Lipschitz Bandits in Optimal Space ICLR 2025 Logarithmic Regret for Linear Markov Decision Processes with Adversarial Corruptions AAAI 2025 Logarithmic Regret for Nonlinear Control L4DC 2025 Logarithmic Regret for Online KL-Regularized Reinforcement Learning ICML 2025 Log-Sum-Exponential Estimator for Off-Policy Evaluation and Learning ICML 2025 Lower Bounds for Time-Varying Kernelized Bandits AISTATS 2025 Minimax Optimal Regret Bound for Reinforcement Learning with Trajectory Feedback ICML 2025 Minimax Optimal Reinforcement Learning with Quasi-Optimism ICLR 2025 Minimum Empirical Divergence for Sub-Gaussian Linear Bandits AISTATS 2025 Misspecified Q-Learning with Sparse Linear Function Approximation: Tight Bounds on Approximation Error ICLR 2025 Mixture of Online and Offline Experts for Non-Stationary Time Series AAAI 2025 Model-based RL as a Minimalist Approach to Horizon-Free and Second-Order Bounds ICLR 2025