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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

Showing 381390 of 655 papers

TitleStatusHype
Multi-armed Bandits with Cost Subsidy0
Greedy k-Center from Noisy Distance Samples0
Screening for an Infectious Disease as a Problem in Stochastic Control0
Bandit Policies for Reliable Cellular Network Handovers in Extreme Mobility0
Sub-sampling for Efficient Non-Parametric Bandit ExplorationCode0
Improved Worst-Case Regret Bounds for Randomized Least-Squares Value Iteration0
Bayesian Algorithms for Decentralized Stochastic BanditsCode0
Federated Bayesian Optimization via Thompson SamplingCode1
Reinforcement Learning for Efficient and Tuning-Free Link Adaptation0
Double-Linear Thompson Sampling for Context-Attentive Bandits0
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