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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
Optimistic Thompson Sampling for No-Regret Learning in Unknown Games—0
Context in Public Health for Underserved Communities: A Bayesian Approach to Online Restless Bandits—0
Efficient Exploration for LLMs—0
Accelerating Approximate Thompson Sampling with Underdamped Langevin Monte CarloCode0
Thompson Sampling for Stochastic Bandits with Noisy Contexts: An Information-Theoretic Regret Analysis—0
Model-Free Approximate Bayesian Learning for Large-Scale Conversion Funnel Optimization—0
Decentralized Multi-Agent Active Search and Tracking when Targets Outnumber Agents—0
Improving sample efficiency of high dimensional Bayesian optimization with MCMC—0
Adaptive Anytime Multi-Agent Path Finding Using Bandit-Based Large Neighborhood SearchCode1
Zero-Inflated Bandits—0
Finite-Time Frequentist Regret Bounds of Multi-Agent Thompson Sampling on Sparse HypergraphsCode0
Best Arm Identification in Batched Multi-armed Bandit Problems—0
Bayesian Analysis of Combinatorial Gaussian Process Bandits—0
RoME: A Robust Mixed-Effects Bandit Algorithm for Optimizing Mobile Health InterventionsCode0
Sample-based Dynamic Hierarchical Transformer with Layer and Head Flexibility via Contextual Bandit—0
The Sliding Regret in Stochastic Bandits: Discriminating Index and Randomized Policies—0
Thompson sampling for zero-inflated count outcomes with an application to the Drink Less mobile health study—0
Probabilistic Inference in Reinforcement Learning Done Right—0
A Distributed Neural Linear Thompson Sampling Framework to Achieve URLLC in Industrial IoT—0
Adaptive Interventions with User-Defined Goals for Health Behavior ChangeCode0
Exploration via linearly perturbed loss minimisation—0
Posterior Sampling-Based Bayesian Optimization with Tighter Bayesian Regret Bounds—0
Batch Bayesian Optimization for Replicable Experimental Design—0
Improved Bayesian Regret Bounds for Thompson Sampling in Reinforcement Learning—0
Dual-Directed Algorithm Design for Efficient Pure Exploration—0
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