thompson sampling

On provably robust meta-Bayesian optimization UAI 2022 On the Value of Prior in Online Learning to Rank AISTATS 2022 Partial likelihood Thompson sampling UAI 2022 Random Effect Bandits AISTATS 2022 Sample-Then-Optimize Batch Neural Thompson Sampling NeurIPS 2022 Society of Agents: Regret Bounds of Concurrent Thompson Sampling NeurIPS 2022 Thompson Sampling Achieves Õ(√T) Regret in Linear Quadratic Control COLT 2022 Thompson Sampling Efficiently Learns to Control Diffusion Processes NeurIPS 2022 Thompson Sampling for Bandit Learning in Matching Markets IJCAI 2022 Thompson Sampling for (Combinatorial) Pure Exploration ICML 2022 Thompson Sampling for Robust Transfer in Multi-Task Bandits ICML 2022 Thompson Sampling with a Mixture Prior AISTATS 2022 Top Two Algorithms Revisited NeurIPS 2022 Tsetlin Machine for Solving Contextual Bandit Problems NeurIPS 2022 When Combinatorial Thompson Sampling meets Approximation Regret NeurIPS 2022 A Closer Look at the Worst-case Behavior of Multi-armed Bandit Algorithms NeurIPS 2021 An Efficient Algorithm For Generalized Linear Bandit: Online Stochastic Gradient Descent and Thompson Sampling AISTATS 2021 A Provably Efficient Model-Free Posterior Sampling Method for Episodic Reinforcement Learning NeurIPS 2021 Asynchronous $ε$-Greedy Bayesian Optimisation UAI 2021 Batched Thompson Sampling NeurIPS 2021 Bayesian decision-making under misspecified priors with applications to meta-learning NeurIPS 2021 Combining Reinforcement Learning and Constraint Programming for Combinatorial Optimization AAAI 2021 Computing an Efficient Exploration Basis for Learning with Univariate Polynomial Features AAAI 2021 Deciding What to Learn: A Rate-Distortion Approach ICML 2021 Deep Bandits Show-Off: Simple and Efficient Exploration with Deep Networks NeurIPS 2021