thompson sampling

Thompson Sampling with Less Exploration is Fast and Optimal ICML 2023 Tight Regret and Complexity Bounds for Thompson Sampling via Langevin Monte Carlo AISTATS 2023 Towards Scalable and Robust Structured Bandits: A Meta-Learning Framework AISTATS 2023 TS-UCB: Improving on Thompson Sampling With Little to No Additional Computation AISTATS 2023 Adaptive Gating for Single-Photon 3D Imaging CVPR 2022 An Analysis of Ensemble Sampling NeurIPS 2022 Augmented RBMLE-UCB Approach for Adaptive Control of Linear Quadratic Systems NeurIPS 2022 A Unifying Theory of Thompson Sampling for Continuous Risk-Averse Bandits AAAI 2022 Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization NeurIPS 2022 Causal Bandits without prior knowledge using separating sets CLeaR 2022 Deep Hierarchy in Bandits ICML 2022 Diversified Sampling for Batched Bayesian Optimization with Determinantal Point Processes AISTATS 2022 EE-Net: Exploitation-Exploration Neural Networks in Contextual Bandits ICLR 2022 ESCADA: Efficient Safety and Context Aware Dose Allocation for Precision Medicine NeurIPS 2022 Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits NeurIPS 2022 Fourier Representations for Black-Box Optimization over Categorical Variables AAAI 2022 Hierarchical Bayesian Bandits AISTATS 2022 Incentivizing Combinatorial Bandit Exploration NeurIPS 2022 Langevin Monte Carlo for Contextual Bandits ICML 2022 Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits NeurIPS 2022 Linearizing contextual bandits with latent state dynamics UAI 2022 Machine Learning for Online Algorithm Selection under Censored Feedback AAAI 2022 Maillard Sampling: Boltzmann Exploration Done Optimally AISTATS 2022 Metalearning Linear Bandits by Prior Update AISTATS 2022 Near-optimal Thompson sampling-based algorithms for differentially private stochastic bandits UAI 2022