Search Results for author:
Found 2 papers, 1 papers with code
A Bayesian Approach to Online Learning for Contextual Restless Bandits with Applications to Public Health
Restless multi-armed bandits (RMABs) are used to model sequential resource allocation in public health intervention programs.
An Experimental Design for Anytime-Valid Causal Inference on Multi-Armed Bandits
We develop the Mixture Adaptive Design (MAD), a new experimental design for multi-armed bandit experiments that produces powerful and anytime-valid inference on the ATE for \emph{any} bandit algorithm of the experimenter's choice, even those without probabilistic treatment assignment.
Cannot find the paper you are looking for? You can
Submit a new open access paper.
