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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 491–500 of 655 papers

TitleStatusHype
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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