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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 501–550 of 655 papers

TitleStatusHype
Randomised Bayesian Least-Squares Policy Iteration—0
Sampling Acquisition Functions for Batch Bayesian Optimization—0
On Multi-Armed Bandit Designs for Dose-Finding Clinical Trials—0
Sample-Efficient Model-Free Reinforcement Learning with Off-Policy CriticsCode0
Meta Dynamic Pricing: Transfer Learning Across Experiments—0
Constrained Thompson Sampling for Wireless Link Optimization—0
Fully Distributed Bayesian Optimization with Stochastic Policies—0
Multi-Armed Bandit Strategies for Non-Stationary Reward Distributions and Delayed Feedback Processes—0
Scalable Thompson Sampling via Optimal Transport—0
Thompson Sampling with Information Relaxation PenaltiesCode0
KLUCB Approach to Copeland Bandits—0
First-Order Bayesian Regret Analysis of Thompson Sampling—0
Contextual Multi-armed Bandit Algorithm for Semiparametric Reward Model—0
Thompson Sampling for a Fatigue-aware Online Recommendation SystemCode0
Parallel Contextual Bandits in Wireless Handover Optimization—0
Information-Directed Exploration for Deep Reinforcement LearningCode0
MergeDTS: A Method for Effective Large-Scale Online Ranker EvaluationCode0
Thompson Sampling for Noncompliant Bandits—0
Bandit Learning with Implicit FeedbackCode0
Optimal Learning for Dynamic Coding in Deadline-Constrained Multi-Channel Networks—0
Adapting multi-armed bandits policies to contextual bandits scenariosCode0
Thompson Sampling for Pursuit-Evasion Problems—0
Practical Bayesian Learning of Neural Networks via Adaptive Optimisation MethodsCode0
A Unified Approach to Translate Classical Bandit Algorithms to the Structured Bandit Setting—0
Combining Bayesian Optimization and Lipschitz Optimization—0
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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