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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 151–160 of 655 papers

TitleStatusHype
Thompson Sampling for Stochastic Bandits with Noisy Contexts: An Information-Theoretic Regret Analysis—0
Model-Free Approximate Bayesian Learning for Large-Scale Conversion Funnel Optimization—0
Decentralized Multi-Agent Active Search and Tracking when Targets Outnumber Agents—0
Improving sample efficiency of high dimensional Bayesian optimization with MCMC—0
Zero-Inflated Bandits—0
Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse HypergraphsCode0
Best Arm Identification in Batched Multi-armed Bandit Problems—0
Bayesian Analysis of Combinatorial Gaussian Process Bandits—0
RoME: A Robust Mixed-Effects Bandit Algorithm for Optimizing Mobile Health InterventionsCode0
Sample-based Dynamic Hierarchical Transformer with Layer and Head Flexibility via Contextual Bandit—0
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