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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 451–475 of 655 papers

TitleStatusHype
Making Sense of Reinforcement Learning and Probabilistic Inference—0
Randomized Exploration for Non-Stationary Stochastic Linear BanditsCode0
Solving Bernoulli Rank-One Bandits with Unimodal Thompson Sampling—0
Ordinal Bayesian Optimisation—0
Thompson Sampling and Approximate Inference—0
Thompson Sampling for Multinomial Logit Contextual BanditsCode0
Bayesian Optimization for Categorical and Category-Specific Continuous InputsCode0
Automatic Ensemble Learning for Online Influence Maximization—0
Multi-Agent Thompson Sampling for Bandit Applications with Sparse Neighbourhood StructuresCode0
Information-Theoretic Confidence Bounds for Reinforcement Learning—0
Adaptive Portfolio by Solving Multi-armed Bandit via Thompson Sampling—0
Incentivized Exploration for Multi-Armed Bandits under Reward Drift—0
Safe Linear Thompson Sampling with Side Information—0
On Online Learning in Kernelized Markov Decision Processes—0
On Batch Bayesian Optimization—0
Thompson Sampling for Contextual Bandit Problems with Auxiliary Safety Constraints—0
Thompson Sampling via Local UncertaintyCode0
Fixed-Confidence Guarantees for Bayesian Best-Arm Identification—0
Thompson Sampling in Non-Episodic Restless Bandits—0
Regret Analysis of Bandit Problems with Causal Background Knowledge—0
Old Dog Learns New Tricks: Randomized UCB for Bandit ProblemsCode0
Robust Dynamic Assortment Optimization in the Presence of Outlier Customers—0
A Quantile-based Approach for Hyperparameter Transfer Learning—0
A Copula approach for hyperparameter transfer learning—0
Efficient Multivariate Bandit Algorithm with Path Planning—0
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