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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 501–525 of 655 papers

TitleStatusHype
Thompson Sampling with Virtual Helping Agents—0
Time-Sensitive Bandit Learning and Satisficing Thompson Sampling—0
Top Two Algorithms Revisited—0
Towards Optimal Algorithms for Prediction with Expert Advice—0
Towards Scalable and Robust Structured Bandits: A Meta-Learning Framework—0
Tree Ensembles for Contextual Bandits—0
Truthful mechanisms for linear bandit games with private contexts—0
TSEB: More Efficient Thompson Sampling for Policy Learning—0
TSEC: a framework for online experimentation under experimental constraints—0
TS-UCB: Improving on Thompson Sampling With Little to No Additional Computation—0
Two-Stage Resource Allocation in Reconfigurable Intelligent Surface Assisted Hybrid Networks via Multi-Player Bandits—0
Uncertainty-Aware Search and Value Models: Mitigating Search Scaling Flaws in LLMs—0
Understanding the Training and Generalization of Pretrained Transformer for Sequential Decision Making—0
Reinforcement Learning in Credit Scoring and Underwriting—0
Unimodal Thompson Sampling for Graph-Structured Arms—0
Using Adaptive Experiments to Rapidly Help Students—0
Variable Selection via Thompson Sampling—0
Variational Bayesian Optimistic Sampling—0
WAPTS: A Weighted Allocation Probability Adjusted Thompson Sampling Algorithm for High-Dimensional and Sparse Experiment Settings—0
When and Whom to Collaborate with in a Changing Environment: A Collaborative Dynamic Bandit Solution—0
When and why randomised exploration works (in linear bandits)—0
When Combinatorial Thompson Sampling meets Approximation Regret—0
Practical Batch Bayesian Sampling Algorithms for Online Adaptive Traffic Experimentation—0
Zero-Inflated Bandits—0
A Bandit Approach to Online Pricing for Heterogeneous Edge Resource Allocation—0
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