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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 351–400 of 655 papers

TitleStatusHype
Feel-Good Thompson Sampling for Contextual Bandits and Reinforcement Learning—0
Feel-Good Thompson Sampling for Contextual Dueling Bandits—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
Gaussian Process Thompson Sampling via Rootfinding—0
Generalized Bayesian deep reinforcement learning—0
Generalized Probabilistic Bisection for Stochastic Root-Finding—0
Generalized Regret Analysis of Thompson Sampling using Fractional Posteriors—0
Generalized Thompson Sampling for Contextual Bandits—0
Generator-Mediated Bandits: Thompson Sampling for GenAI-Powered Adaptive Interventions—0
Geometry-Aware Approaches for Balancing Performance and Theoretical Guarantees in Linear Bandits—0
Graph Neural Thompson Sampling—0
Feedback graph regret bounds for Thompson Sampling and UCB—0
Greedy Bandits with Sampled Context—0
Greedy k-Center from Noisy Distance Samples—0
GuideBoot: Guided Bootstrap for Deep Contextual Bandits—0
GUTS: Generalized Uncertainty-Aware Thompson Sampling for Multi-Agent Active Search—0
gym-saturation: Gymnasium environments for saturation provers (System description)—0
Hierarchical Bayesian Bandits—0
High-dimensional near-optimal experiment design for drug discovery via Bayesian sparse sampling—0
Horde of Bandits using Gaussian Markov Random Fields—0
Human collective intelligence as distributed Bayesian inference—0
Hypermodels for Exploration—0
IBAC: An Intelligent Dynamic Bandwidth Channel Access Avoiding Outside Warning Range Problem—0
Improved Bayesian Regret Bounds for Thompson Sampling in Reinforcement Learning—0
Improved Regret Bounds for Thompson Sampling in Linear Quadratic Control Problems—0
Improved Worst-Case Regret Bounds for Randomized Least-Squares Value Iteration—0
Improving Reward-Conditioned Policies for Multi-Armed Bandits using Normalized Weight Functions—0
Improving sample efficiency of high dimensional Bayesian optimization with MCMC—0
Improving Thompson Sampling via Information Relaxation for Budgeted Multi-armed Bandits—0
Incentivized Exploration for Multi-Armed Bandits under Reward Drift—0
Incentivizing Combinatorial Bandit Exploration—0
Incentivizing Exploration with Linear Contexts and Combinatorial Actions—0
Incorporating Behavioral Constraints in Online AI Systems—0
Increasing Students' Engagement to Reminder Emails Through Multi-Armed Bandits—0
Indexed Minimum Empirical Divergence-Based Algorithms for Linear Bandits—0
In-Domain African Languages Translation Using LLMs and Multi-armed Bandits—0
Influence Diagram Bandits: Variational Thompson Sampling for Structured Bandit Problems—0
Influencing Bandits: Arm Selection for Preference Shaping—0
Information Directed Sampling and Bandits with Heteroscedastic Noise—0
Information Directed Sampling for Stochastic Bandits with Graph Feedback—0
Information-Theoretic Confidence Bounds for Reinforcement Learning—0
IntelligentPooling: Practical Thompson Sampling for mHealth—0
Joint User Association and Pairing in Multi-UAV-Assisted NOMA Networks: A Decaying-Epsilon Thompson Sampling Framework—0
KABB: Knowledge-Aware Bayesian Bandits for Dynamic Expert Coordination in Multi-Agent Systems—0
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