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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 526–550 of 655 papers

TitleStatusHype
Thompson Sampling Algorithms for Cascading Bandits—0
Contextual Multi-Armed Bandits for Causal Marketing—0
Efficient Linear Bandits through Matrix Sketching—0
Incorporating Behavioral Constraints in Online AI Systems—0
Analysis of Thompson Sampling for Combinatorial Multi-armed Bandit with Probabilistically Triggered Arms—0
Adaptive Grey-Box Fuzz-Testing with Thompson Sampling—0
Nonparametric Gaussian Mixture Models for the Multi-Armed BanditCode0
Sequential Monte Carlo BanditsCode0
Deep Contextual Multi-armed Bandits—0
Tsallis-INF: An Optimal Algorithm for Stochastic and Adversarial Bandits—0
Optimization of a SSP's Header Bidding Strategy using Thompson Sampling—0
Improved Regret Bounds for Thompson Sampling in Linear Quadratic Control Problems—0
On The Differential Privacy of Thompson Sampling With Gaussian Prior—0
Randomized Value Functions via Multiplicative Normalizing FlowsCode0
Sequential Test for the Lowest Mean: From Thompson to Murphy Sampling—0
An Information-Theoretic Analysis for Thompson Sampling with Many Actions—0
Myopic Bayesian Design of Experiments via Posterior Sampling and Probabilistic ProgrammingCode0
New Insights into Bootstrapping for Bandits—0
Analysis of Thompson Sampling for Graphical Bandits Without the Graphs—0
PG-TS: Improved Thompson Sampling for Logistic Contextual Bandits—0
Profitable Bandits—0
Thompson Sampling for Combinatorial Semi-Bandits—0
Active Reinforcement Learning with Monte-Carlo Tree Search—0
Satisficing in Time-Sensitive Bandit Learning—0
Deep Bayesian Bandits Showdown: An Empirical Comparison of Bayesian Deep Networks for Thompson SamplingCode0
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