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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 251–275 of 655 papers

TitleStatusHype
From Predictions to Decisions: The Importance of Joint Predictive Distributions—0
Evaluation of Explore-Exploit Policies in Multi-result Ranking Systems—0
Bayesian Learning of Optimal Policies in Markov Decision Processes with Countably Infinite State-Space—0
Expected Improvement-based Contextual Bandits—0
A Copula approach for hyperparameter transfer learning—0
Bayesian Optimization with LLM-Based Acquisition Functions for Natural Language Preference Elicitation—0
An Information-Theoretic Analysis of Thompson Sampling—0
Generalized Probabilistic Bisection for Stochastic Root-Finding—0
Feel-Good Thompson Sampling for Contextual Bandits and Reinforcement Learning—0
An Information-Theoretic Analysis for Thompson Sampling with Many Actions—0
Adaptively Learning to Select-Rank in Online Platforms—0
Practical Batch Bayesian Sampling Algorithms for Online Adaptive Traffic Experimentation—0
Generalized Regret Analysis of Thompson Sampling using Fractional Posteriors—0
Online Learning with Cumulative Oversampling: Application to Budgeted Influence Maximization—0
Bayesian Optimization-Based Beam Alignment for MmWave MIMO Communication Systems—0
Feel-Good Thompson Sampling for Contextual Dueling Bandits—0
Bayesian Optimization with Inexact Acquisition: Is Random Grid Search Sufficient?—0
Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits—0
First-Order Bayesian Regret Analysis of Thompson Sampling—0
Fixed-Confidence Guarantees for Bayesian Best-Arm Identification—0
Fourier Representations for Black-Box Optimization over Categorical Variables—0
Freshness-Aware Thompson Sampling—0
From Bandits Model to Deep Deterministic Policy Gradient, Reinforcement Learning with Contextual Information—0
Fully Distributed Bayesian Optimization with Stochastic Policies—0
A Contextual Combinatorial Semi-Bandit Approach to Network Bottleneck Identification—0
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