linear bandit

Corruption-Robust Linear Bandits: Minimax Optimality and Gap-Dependent Misspecification NeurIPS 2024 Directional Optimism for Safe Linear Bandits AISTATS 2024 Ensemble sampling for linear bandits: small ensembles suffice NeurIPS 2024 Fixed-Budget Differentially Private Best Arm Identification ICLR 2024 Improved Bayes Regret Bounds for Multi-Task Hierarchical Bayesian Bandit Algorithms NeurIPS 2024 Improved Regret of Linear Ensemble Sampling NeurIPS 2024 Linear bandits with polylogarithmic minimax regret COLT 2024 Local Anti-Concentration Class: Logarithmic Regret for Greedy Linear Contextual Bandit NeurIPS 2024 Noise-Adaptive Confidence Sets for Linear Bandits and Application to Bayesian Optimization ICML 2024 One Shot Inverse Reinforcement Learning for Stochastic Linear Bandits UAI 2024 On the Minimax Regret for Contextual Linear Bandits and Multi-Armed Bandits with Expert Advice NeurIPS 2024 Optimal Batched Linear Bandits ICML 2024 Optimal Exploration is no harder than Thompson Sampling AISTATS 2024 Pure Exploration in Asynchronous Federated Bandits UAI 2024 Sparsity-Agnostic Linear Bandits with Adaptive Adversaries NeurIPS 2024 SPEED: Experimental Design for Policy Evaluation in Linear Heteroscedastic Bandits AISTATS 2024 Symmetric Linear Bandits with Hidden Symmetry NeurIPS 2024 An Exploration-by-Optimization Approach to Best of Both Worlds in Linear Bandits NeurIPS 2023 Anytime Model Selection in Linear Bandits NeurIPS 2023 A Unified Analysis of Nonstochastic Delayed Feedback for Combinatorial Semi-Bandits, Linear Bandits, and MDPs COLT 2023 Best-of-three-worlds Analysis for Linear Bandits with Follow-the-regularized-leader Algorithm COLT 2023 Best-of-Three-Worlds Linear Bandit Algorithm with Variance-Adaptive Regret Bounds COLT 2023 Bypassing the Simulator: Near-Optimal Adversarial Linear Contextual Bandits NeurIPS 2023 Contexts can be Cheap: Solving Stochastic Contextual Bandits with Linear Bandit Algorithms COLT 2023 Does Sparsity Help in Learning Misspecified Linear Bandits? ICML 2023