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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 426–450 of 655 papers

TitleStatusHype
Meta-Reinforcement Learning With Informed Policy Regularization—0
Meta-Thompson Sampling—0
Minimal Exploration in Structured Stochastic Bandits—0
TS-RSR: A provably efficient approach for batch Bayesian Optimization—0
Mixed-Variable Bayesian Optimization—0
Model-based Meta Reinforcement Learning using Graph Structured Surrogate Models—0
Model-Free Approximate Bayesian Learning for Large-Scale Conversion Funnel Optimization—0
Modified Meta-Thompson Sampling for Linear Bandits and Its Bayes Regret Analysis—0
Module-wise Adaptive Distillation for Multimodality Foundation Models—0
Monte Carlo Tree Search Algorithms for Risk-Aware and Multi-Objective Reinforcement Learning—0
Monte-Carlo tree search with uncertainty propagation via optimal transport—0
MOTS: Minimax Optimal Thompson Sampling—0
Multi-Agent Active Search using Detection and Location Uncertainty—0
Multi-armed Bandit Algorithms on System-on-Chip: Go Frequentist or Bayesian?—0
Multi-Armed Bandit Strategies for Non-Stationary Reward Distributions and Delayed Feedback Processes—0
Multi-armed Bandits with Cost Subsidy—0
Multi-dueling Bandits with Dependent Arms—0
Multi-Task Combinatorial Bandits for Budget Allocation—0
Near Optimal Adversarial Attacks on Stochastic Bandits and Defenses with Smoothed Responses—0
Neural Contextual Bandits Under Delayed Feedback Constraints—0
Neural Dueling Bandits: Preference-Based Optimization with Human Feedback—0
Neural Model-based Optimization with Right-Censored Observations—0
New Insights into Bootstrapping for Bandits—0
No Algorithmic Collusion in Two-Player Blindfolded Game with Thompson Sampling—0
Nonparametric General Reinforcement Learning—0
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