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Thompson Sampling

Thompson sampling, named after William R. Thompson, is a heuristic for choosing actions that addresses the exploration-exploitation dilemma in the multi-armed bandit problem. It consists of choosing the action that maximizes the expected reward with respect to a randomly drawn belief.

Papers

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Showing 61–70 of 655 papers

TitleStatusHype
Active RLHF via Best Policy Learning from Trajectory Preference Feedback—0
FedRTS: Federated Robust Pruning via Combinatorial Thompson SamplingCode0
EVaDE : Event-Based Variational Thompson Sampling for Model-Based Reinforcement Learning—0
Truthful mechanisms for linear bandit games with private contexts—0
Stochastically Constrained Best Arm Identification with Thompson Sampling—0
WAPTS: A Weighted Allocation Probability Adjusted Thompson Sampling Algorithm for High-Dimensional and Sparse Experiment Settings—0
On Improved Regret Bounds In Bayesian Optimization with Gaussian Noise—0
Generalized Bayesian deep reinforcement learning—0
An Information-Theoretic Analysis of Thompson Sampling for Logistic Bandits—0
BOTS: Batch Bayesian Optimization of Extended Thompson Sampling for Severely Episode-Limited RL Settings—0
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