regret bound

Multi-agent Multi-armed Bandit Regret Complexity and Optimality AISTATS 2025 Multi-agent Stochastic Bandits Robust to Adversarial Corruptions L4DC 2025 Multi-armed Bandits with Missing Outcomes UAI 2025 Multi-objective Linear Reinforcement Learning with Lexicographic Rewards ICML 2025 Multi-Player Approaches for Dueling Bandits AISTATS 2025 Multi-player Multi-armed Bandits with Delayed Feedback IJCAI 2025 Multi-play Multi-armed Bandits with Shareable Arm Capacities Revisited: Settling Scarce Capacity ACML 2025 Multiple Trade-offs: An Improved Approach for Lexicographic Linear Bandits AAAI 2025 Narrowing the Gap between Adversarial and Stochastic MDPs via Policy Optimization AISTATS 2025 Nearly Optimal Algorithms for Contextual Dueling Bandits from Adversarial Feedback ICML 2025 Near-Optimal Algorithm for Non-Stationary Kernelized Bandits AISTATS 2025 Near-Optimal Online Learning for Multi-Agent Submodular Coordination: Tight Approximation and Communication Efficiency ICLR 2025 Near-Optimal Regret Bounds for Federated Multi-armed Bandits with Fully Distributed Communication UAI 2025 Near-Polynomially Competitive Active Logistic Regression AISTATS 2025 Neural Combinatorial Clustered Bandits for Recommendation Systems AAAI 2025 Non-stochastic Bandits With Evolving Observations ALT 2025 Non-stochastic Budgeted Online Pricing with Semi-Bandit Feedback AAAI 2025 No-Regret Bayesian Optimization with Stochastic Observation Failures AISTATS 2025 No Regret Reinforcement Learning Algorithms for Online Scheduling with Multi-Stage Tasks IJCAI 2025 On Bits and Bandits: Quantifying the Regret-Information Trade-off ICLR 2025 On Constant Regret for Low-Rank MDPs UAI 2025 Online Assortment and Price Optimization Under Contextual Choice Models AISTATS 2025 Online Clustering of Dueling Bandits ICML 2025 Online Convex Optimization with a Separation Oracle COLT 2025 Online Episodic Convex Reinforcement Learning ICML 2025