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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 171–180 of 655 papers

TitleStatusHype
Little Exploration is All You Need—0
Making RL with Preference-based Feedback Efficient via Randomization—0
Parallel Bayesian Optimization Using Satisficing Thompson Sampling for Time-Sensitive Black-Box Optimization—0
Using Adaptive Bandit Experiments to Increase and Investigate Engagement in Mental HealthCode0
Optimal Exploration is no harder than Thompson Sampling—0
Module-wise Adaptive Distillation for Multimodality Foundation Models—0
Thompson Exploration with Best Challenger Rule in Best Arm Identification—0
From Bandits Model to Deep Deterministic Policy Gradient, Reinforcement Learning with Contextual Information—0
Monte-Carlo tree search with uncertainty propagation via optimal transport—0
Task Selection and Assignment for Multi-modal Multi-task Dialogue Act Classification with Non-stationary Multi-armed Bandits—0
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