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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 351–375 of 655 papers

TitleStatusHype
Optimality of Thompson Sampling with Noninformative Priors for Pareto Bandits—0
Optimal Learning for Dynamic Coding in Deadline-Constrained Multi-Channel Networks—0
Optimal No-regret Learning in Repeated First-price Auctions—0
Optimal Recommendation to Users that React: Online Learning for a Class of POMDPs—0
Optimistic posterior sampling for reinforcement learning: worst-case regret bounds—0
Optimistic Thompson Sampling for No-Regret Learning in Unknown Games—0
Optimization of a SSP's Header Bidding Strategy using Thompson Sampling—0
Optimizing Adaptive Experiments: A Unified Approach to Regret Minimization and Best-Arm Identification—0
Ordinal Bayesian Optimisation—0
Parallel and Distributed Thompson Sampling for Large-scale Accelerated Exploration of Chemical Space—0
Parallel Bayesian Optimization Using Satisficing Thompson Sampling for Time-Sensitive Black-Box Optimization—0
Parallel Contextual Bandits in Wireless Handover Optimization—0
Parallelizing Thompson Sampling—0
Partial Likelihood Thompson Sampling—0
Partially Observable Contextual Bandits with Linear Payoffs—0
Partially Observable Online Change Detection via Smooth-Sparse Decomposition—0
PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits—0
Planning and Learning in Risk-Aware Restless Multi-Arm Bandit Problem—0
Policy Gradient Optimization of Thompson Sampling Policies—0
Position-Based Multiple-Play Bandits with Thompson Sampling—0
Posterior Sampling-Based Bayesian Optimization with Tighter Bayesian Regret Bounds—0
Posterior sampling for reinforcement learning: worst-case regret bounds—0
Posterior Sampling via Autoregressive Generation—0
Practical Adversarial Attacks on Stochastic Bandits via Fake Data Injection—0
Preferential Multi-Objective Bayesian Optimization—0
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