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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 121–130 of 655 papers

TitleStatusHype
Understanding the Training and Generalization of Pretrained Transformer for Sequential Decision Making—0
Smart Routing with Precise Link Estimation: DSEE-Based Anypath Routing for Reliable Wireless Networking—0
Analyzing and Enhancing Queue Sampling for Energy-Efficient Remote Control of Bandits—0
Thompson Sampling for Infinite-Horizon Discounted Decision Processes—0
Constructing Adversarial Examples for Vertical Federated Learning: Optimal Client Corruption through Multi-Armed BanditCode0
Efficient and Adaptive Posterior Sampling Algorithms for Bandits—0
Bayesian Optimization with LLM-Based Acquisition Functions for Natural Language Preference Elicitation—0
Bayesian-Guided Generation of Synthetic Microbiomes with Minimized Pathogenicity—0
Randomized Exploration in Cooperative Multi-Agent Reinforcement Learning—0
Feel-Good Thompson Sampling for Contextual Dueling Bandits—0
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