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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 601610 of 655 papers

TitleStatusHype
Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte CarloCode0
Thompson Sampling for Bandit Learning in Matching MarketsCode0
Differentially Private Online Bayesian Estimation With Adaptive TruncationCode0
Multi-Agent Active Search using Realistic Depth-Aware Noise ModelCode0
Optimal Regret Analysis of Thompson Sampling in Stochastic Multi-armed Bandit Problem with Multiple PlaysCode0
Multi-armed bandits for resource efficient, online optimization of language model pre-training: the use case of dynamic maskingCode0
Optimal Regret Is Achievable with Bounded Approximate Inference Error: An Enhanced Bayesian Upper Confidence Bound FrameworkCode0
Improving Portfolio Optimization Results with Bandit NetworksCode0
Thompson Sampling for Robust Transfer in Multi-Task BanditsCode0
Sequential Monte Carlo BanditsCode0
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