regret

Distributionally Robust Regret Optimal Control Under Moment-Based Ambiguity Sets L4DC 2026 A Classification View on Meta Learning Bandits ICML 2025 Adaptive Sample Sharing for Multi Agent Linear Bandits ICML 2025 Adversarial Combinatorial Semi-bandits with Graph Feedback ICML 2025 An Optimistic Algorithm for online CMDPS with Anytime Adversarial Constraints ICML 2025 Avoiding Catastrophe in Online Learning by Asking for Help ICML 2025 Beyond Worst-Case Online Classification: VC-Based Regret Bounds for Relaxed Benchmarks COLT 2025 Capacity-Constrained Online Learning with Delays: Scheduling Frameworks and Regret Trade-offs COLT 2025 Constrained Online Convex Optimization with Polyak Feasibility Steps ICML 2025 Corrupted Learning Dynamics in Games COLT 2025 EVOLvE: Evaluating and Optimizing LLMs For In-Context Exploration ICML 2025 Faster Rates for Private Adversarial Bandits ICML 2025 Federated Q-Learning with Reference-Advantage Decomposition: Almost Optimal Regret and Logarithmic Communication Cost ICLR 2025 Improved Regret Analysis in Gaussian Process Bandits: Optimality for Noiseless Reward, RKHS norm, and Non-Stationary Variance ICML 2025 Learning-Augmented Algorithms for MTS with Bandit Access to Multiple Predictors ICML 2025 Learning to Incentivize in Repeated Principal-Agent Problems with Adversarial Agent Arrivals ICML 2025 Linear Bandits on Ellipsoids: Minimax Optimal Algorithms COLT 2025 Market Making without Regret COLT 2025 MetaOptimize: A Framework for Optimizing Step Sizes and Other Meta-parameters ICML 2025 Multi-Armed Bandits with Interference: Bridging Causal Inference and Adversarial Bandits ICML 2025 Near-optimal Regret Using Policy Optimization in Online MDPs with Aggregate Bandit Feedback ICML 2025 Non-Monetary Mechanism Design without Distributional Information: Using Scarce Audits Wisely (Extended Abstract) COLT 2025 Online Learning in Risk Sensitive constrained MDP ICML 2025 Online Learning with Unknown Constraints ICML 2025 Optimal Algorithm for Max-Min Fair Bandit ICML 2025