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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 251–300 of 655 papers

TitleStatusHype
Risk-averse Contextual Multi-armed Bandit Problem with Linear Payoffs—0
Analysis of Thompson Sampling for Controlling Unknown Linear Diffusion Processes—0
Thompson Sampling for (Combinatorial) Pure Exploration—0
Thompson Sampling for Robust Transfer in Multi-Task BanditsCode0
Thompson Sampling Achieves O(T) Regret in Linear Quadratic Control—0
A Contextual Combinatorial Semi-Bandit Approach to Network Bottleneck Identification—0
On Provably Robust Meta-Bayesian OptimizationCode0
Top Two Algorithms Revisited—0
Regret Bounds for Information-Directed Reinforcement Learning—0
A Simple and Optimal Policy Design with Safety against Heavy-Tailed Risk for Stochastic Bandits—0
Finite-Time Regret of Thompson Sampling Algorithms for Exponential Family Multi-Armed Bandits—0
Bandit Theory and Thompson Sampling-Guided Directed Evolution for Sequence Optimization—0
Incentivizing Combinatorial Bandit Exploration—0
Mixed-Effect Thompson SamplingCode0
Surrogate modeling for Bayesian optimization beyond a single Gaussian process—0
Lifting the Information Ratio: An Information-Theoretic Analysis of Thompson Sampling for Contextual Bandits—0
Information-Directed Selection for Top-Two AlgorithmsCode0
Fast Change Identification in Multi-Play Bandits and its Applications in Wireless Networks—0
Semi-Parametric Contextual Bandits with Graph-Laplacian Regularization—0
Adjusted Expected Improvement for Cumulative Regret Minimization in Noisy Bayesian Optimization—0
Non-Stationary Bandit Learning via Predictive Sampling—0
Evolutionary Multi-Armed Bandits with Genetic Thompson SamplingCode0
Thompson Sampling for Bandit Learning in Matching MarketsCode0
On Kernelized Multi-Armed Bandits with Constraints—0
Multi-armed bandits for resource efficient, online optimization of language model pre-training: the use case of dynamic maskingCode0
Thompson Sampling on Asymmetric α-Stable Bandits—0
Regenerative Particle Thompson Sampling—0
Multi-Agent Active Search using Detection and Location Uncertainty—0
An Analysis of Ensemble Sampling—0
Partial Likelihood Thompson Sampling—0
Scalable Bayesian Optimization Using Vecchia Approximations of Gaussian ProcessesCode0
Towards Scalable and Robust Structured Bandits: A Meta-Learning Framework—0
Thompson Sampling with Unrestricted Delays—0
Double Thompson Sampling in Finite stochastic Games—0
Adaptive Experimentation in the Presence of Exogenous Nonstationary Variation—0
Fast online inference for nonlinear contextual bandit based on Generative Adversarial Network—0
Synthetically Controlled Bandits—0
Remote Contextual Bandits—0
Fourier Representations for Black-Box Optimization over Categorical Variables—0
On learning Whittle index policy for restless bandits with scalable regret—0
Bayesian Non-stationary Linear Bandits for Large-Scale Recommender SystemsCode0
Tsetlin Machine for Solving Contextual Bandit ProblemsCode0
Deep Hierarchy in Bandits—0
Evaluating Deep Vs. Wide & Deep Learners As Contextual Bandits For Personalized Email Promo RecommendationsCode0
Optimal Regret Is Achievable with Bounded Approximate Inference Error: An Enhanced Bayesian Upper Confidence Bound FrameworkCode0
Modeling Human Exploration Through Resource-Rational Reinforcement LearningCode0
Augmented RBMLE-UCB Approach for Adaptive Control of Linear Quadratic Systems—0
IBAC: An Intelligent Dynamic Bandwidth Channel Access Avoiding Outside Warning Range Problem—0
On Dynamic Pricing with Covariates—0
Algorithms for Adaptive Experiments that Trade-off Statistical Analysis with Reward: Combining Uniform Random Assignment and Reward Maximization—0
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