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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

Showing 231240 of 655 papers

TitleStatusHype
Atlas: Automate Online Service Configuration in Network SlicingCode0
Optimizing Pessimism in Dynamic Treatment Regimes: A Bayesian Learning ApproachCode0
Meta Learning of Interface Conditions for Multi-Domain Physics-Informed Neural Networks0
Deep Active Ensemble Sampling For Image Classification0
The Typical Behavior of Bandit Algorithms0
Cost Aware Asynchronous Multi-Agent Active Search0
Thompson Sampling with Virtual Helping Agents0
Double Doubly Robust Thompson Sampling for Generalized Linear Contextual Bandits0
A Nonparametric Contextual Bandit with Arm-level Eligibility Control for Customer Service Routing0
Sample Efficient Learning of Factored Embeddings of Tensor Fields0
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