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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 381–390 of 655 papers

TitleStatusHype
Improving Thompson Sampling via Information Relaxation for Budgeted Multi-armed Bandits—0
Incentivized Exploration for Multi-Armed Bandits under Reward Drift—0
Incentivizing Combinatorial Bandit Exploration—0
Incentivizing Exploration with Linear Contexts and Combinatorial Actions—0
Incorporating Behavioral Constraints in Online AI Systems—0
Increasing Students' Engagement to Reminder Emails Through Multi-Armed Bandits—0
Indexed Minimum Empirical Divergence-Based Algorithms for Linear Bandits—0
In-Domain African Languages Translation Using LLMs and Multi-armed Bandits—0
Influence Diagram Bandits: Variational Thompson Sampling for Structured Bandit Problems—0
Influencing Bandits: Arm Selection for Preference Shaping—0
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