linear bandit

Dynamical Linear Bandits ICML 2023 Exploration in Linear Bandits with Rich Action Sets and its Implications for Inference AISTATS 2023 Federated Linear Bandits with Finite Adversarial Actions NeurIPS 2023 Heavy-tailed linear bandit with Huber regression UAI 2023 Incentivizing Exploration with Linear Contexts and Combinatorial Actions ICML 2023 Linear Partial Monitoring for Sequential Decision Making: Algorithms, Regret Bounds and Applications JMLR 2023 Multi-task Representation Learning for Pure Exploration in Linear Bandits ICML 2023 Nash Regret Guarantees for Linear Bandits NeurIPS 2023 No-Regret Linear Bandits beyond Realizability UAI 2023 On the Interplay Between Misspecification and Sub-optimality Gap in Linear Contextual Bandits ICML 2023 Replicable Bandits ICLR 2023 Risk-aware linear bandits with convex loss AISTATS 2023 Stochastic Contextual Bandits with Long Horizon Rewards AAAI 2023 Tackling Heavy-Tailed Rewards in Reinforcement Learning with Function Approximation: Minimax Optimal and Instance-Dependent Regret Bounds NeurIPS 2023 Thresholded linear bandits AISTATS 2023 Variance-Dependent Regret Bounds for Linear Bandits and Reinforcement Learning: Adaptivity and Computational Efficiency COLT 2023 Active Learning with Safety Constraints NeurIPS 2022 An Analysis of Ensemble Sampling NeurIPS 2022 A Reduction-Based Framework for Conservative Bandits and Reinforcement Learning ICLR 2022 A Simple and Provably Efficient Algorithm for Asynchronous Federated Contextual Linear Bandits NeurIPS 2022 Breaking the √T Barrier: Instance-Independent Logarithmic Regret in Stochastic Contextual Linear Bandits ICML 2022 Choosing Answers in Epsilon-Best-Answer Identification for Linear Bandits ICML 2022 Collaborative Linear Bandits with Adversarial Agents: Near-Optimal Regret Bounds NeurIPS 2022 Corralling a Larger Band of Bandits: A Case Study on Switching Regret for Linear Bandits COLT 2022 Encrypted Linear Contextual Bandit AISTATS 2022