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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 126–150 of 655 papers

TitleStatusHype
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
Online Learning of Decision Trees with Thompson SamplingCode0
Feel-Good Thompson Sampling for Contextual Dueling Bandits—0
A Reinforcement Learning based Reset Policy for CDCL SAT Solvers—0
On the Importance of Uncertainty in Decision-Making with Large Language Models—0
Meta Learning in Bandits within Shared Affine Subspaces—0
A resource-constrained stochastic scheduling algorithm for homeless street outreach and gleaning edible food—0
ε-Neural Thompson Sampling of Deep Brain Stimulation for Parkinson Disease Treatment—0
Cramming Contextual Bandits for On-policy Statistical Evaluation—0
TS-RSR: A provably efficient approach for batch Bayesian Optimization—0
Chained Information-Theoretic bounds and Tight Regret Rate for Linear Bandit Problems—0
Epsilon-Greedy Thompson Sampling to Bayesian Optimization—0
Influencing Bandits: Arm Selection for Preference Shaping—0
Towards Efficient and Optimal Covariance-Adaptive Algorithms for Combinatorial Semi-Bandits—0
Optimizing Adaptive Experiments: A Unified Approach to Regret Minimization and Best-Arm Identification—0
Thompson Sampling in Partially Observable Contextual Bandits—0
Diffusion Models Meet Contextual Bandits with Large Action Spaces—0
Tree Ensembles for Contextual Bandits—0
Context in Public Health for Underserved Communities: A Bayesian Approach to Online Restless Bandits—0
Optimistic Thompson Sampling for No-Regret Learning in Unknown Games—0
Efficient Exploration for LLMs—0
Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte CarloCode0
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