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Thompson Sampling
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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.
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Achieving adaptivity and optimality for multi-armed bandits using Exponential-Kullback Leibler Maillard Sampling
Robust Policy Switching for Antifragile Reinforcement Learning for UAV Deconfliction in Adversarial Environments
Context Attribution with Multi-Armed Bandit Optimization
Adaptive Data Augmentation for Thompson Sampling
Bayesian Optimization with Inexact Acquisition: Is Random Grid Search Sufficient?
Efficient kernelized bandit algorithms via exploration distributions
Asymptotically Optimal Linear Best Feasible Arm Identification with Fixed Budget
Stable Thompson Sampling: Valid Inference via Variance Inflation
Simplifying Bayesian Optimization Via In-Context Direct Optimum Sampling
Thompson Sampling in Online RLHF with General Function Approximation
Representative Action Selection for Large Action-Space Meta-Bandits
Generator-Mediated Bandits: Thompson Sampling for GenAI-Powered Adaptive Interventions
Scalable and Interpretable Contextual Bandits: A Literature Review and Retail Offer Prototype
Deconfounded Warm-Start Thompson Sampling with Applications to Precision Medicine
In-Domain African Languages Translation Using LLMs and Multi-armed Bandits
Steering Generative Models with Experimental Data for Protein Fitness Optimization
Dynamic Decision-Making under Model Misspecification
Addressing Missing Data Issue for Diffusion-based Recommendation
Thompson Sampling-like Algorithms for Stochastic Rising Bandits
Leveraging Offline Data from Similar Systems for Online Linear Quadratic Control
Connecting Thompson Sampling and UCB: Towards More Efficient Trade-offs Between Privacy and Regret
Bayesian learning of the optimal action-value function in a Markov decision process
Neural Contextual Bandits Under Delayed Feedback Constraints
Dynamic Assortment Selection and Pricing with Censored Preference Feedback
Counterfactual Inference under Thompson Sampling
Sparse Nonparametric Contextual Bandits
Bandit-Based Prompt Design Strategy Selection Improves Prompt Optimizers
An Adversarial Analysis of Thompson Sampling for Full-information Online Learning: from Finite to Infinite Action Spaces
Uncertainty-Aware Search and Value Models: Mitigating Search Scaling Flaws in LLMs
When and why randomised exploration works (in linear bandits)
KABB: Knowledge-Aware Bayesian Bandits for Dynamic Expert Coordination in Multi-Agent Systems
Contextual Thompson Sampling via Generation of Missing Data
An Information-Theoretic Analysis of Thompson Sampling with Infinite Action Spaces
Active RLHF via Best Policy Learning from Trajectory Preference Feedback
FedRTS: Federated Robust Pruning via Combinatorial Thompson Sampling
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