regret bound

Local Anti-Concentration Class: Logarithmic Regret for Greedy Linear Contextual Bandit NeurIPS 2024 Local Linearity: the Key for No-regret Reinforcement Learning in Continuous MDPs NeurIPS 2024 Low-rank Matrix Bandits with Heavy-tailed Rewards UAI 2024 Making RL with Preference-based Feedback Efficient via Randomization ICLR 2024 Maximizing utility in multi-agent environments by anticipating the behavior of other learners NeurIPS 2024 MetaCURL: Non-stationary Concave Utility Reinforcement Learning NeurIPS 2024 Mitigating Underfitting in Learning to Defer with Consistent Losses AISTATS 2024 Mixed-Effects Contextual Bandits AAAI 2024 Monte Carlo Tree Search in the Presence of Transition Uncertainty AAAI 2024 Multi-Agent Bandit Learning through Heterogeneous Action Erasure Channels AISTATS 2024 Multi-agent coverage control with transient behavior consideration L4DC 2024 Multi-armed bandits with guaranteed revenue per arm AISTATS 2024 Multiclass Learning from Noisy Labels for Non-decomposable Performance Measures AISTATS 2024 Multiclass Transductive Online Learning NeurIPS 2024 Multi-fidelity Bayesian Optimization with Multiple Information Sources of Input-dependent Fidelity UAI 2024 Multiobjective Lipschitz Bandits under Lexicographic Ordering AAAI 2024 Multitask Online Learning: Listen to the Neighborhood Buzz AISTATS 2024 Near-continuous time Reinforcement Learning for continuous state-action spaces ALT 2024 Nearly Minimax Optimal Regret for Multinomial Logistic Bandit NeurIPS 2024 Nearly Minimax Optimal Submodular Maximization with Bandit Feedback NeurIPS 2024 Nearly Optimal Regret for Decentralized Online Convex Optimization COLT 2024 Near-optimal Per-Action Regret Bounds for Sleeping Bandits AISTATS 2024 NeoRL: Efficient Exploration for Nonepisodic RL NeurIPS 2024 No Internal Regret with Non-convex Loss Functions AAAI 2024 Noise-Adaptive Confidence Sets for Linear Bandits and Application to Bayesian Optimization ICML 2024