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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 476–500 of 655 papers

TitleStatusHype
An Arm-Wise Randomization Approach to Combinatorial Linear Semi-Bandits—0
Online Causal Inference for Advertising in Real-Time Bidding Auctions—0
A Batched Multi-Armed Bandit Approach to News Headline Testing—0
A Bayesian Choice Model for Eliminating Feedback Loops—0
Thompson Sampling with Approximate Inference—0
Scaling Multi-Armed Bandit Algorithms—0
Convergence Rates of Posterior Distributions in Markov Decision Process—0
Adaptive Thompson Sampling Stacks for Memory Bounded Open-Loop PlanningCode0
Thompson Sampling on Symmetric α-Stable Bandits—0
Thompson Sampling for Combinatorial Network Optimization in Unknown Environments—0
Mixed-Variable Bayesian Optimization—0
Bandit Learning for Diversified Interactive Recommendation—0
Thompson Sampling for Adversarial Bit Prediction—0
Revised Progressive-Hedging-Algorithm Based Two-layer Solution Scheme for Bayesian Reinforcement Learning—0
Sparse Spectrum Gaussian Process for Bayesian Optimization—0
Stochastic Neural Network with Kronecker Flow—0
The Intrinsic Robustness of Stochastic Bandits to Strategic Manipulation—0
Regret Bounds for Thompson Sampling in Episodic Restless Bandit ProblemsCode0
Connections Between Mirror Descent, Thompson Sampling and the Information Ratio—0
Feedback graph regret bounds for Thompson Sampling and UCB—0
Adaptive Model Selection Framework: An Application to Airline Pricing—0
Adaptive Sensor Placement for Continuous Spaces—0
On the Performance of Thompson Sampling on Logistic Bandits—0
Memory Bounded Open-Loop Planning in Large POMDPs using Thompson SamplingCode0
AutoSeM: Automatic Task Selection and Mixing in Multi-Task Learning—0
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