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Found 5 papers, 0 papers with code
Synthetically Controlled Bandits
This paper presents a new dynamic approach to experiment design in settings where, due to interference or other concerns, experimental units are coarse.
Optimistic Gittins Indices
We show that the use of these approximations in concert with the use of an increasing discount factor appears to offer a compelling alternative to a variety of index schemes proposed for the Bayesian MAB problem in recent years.
Non-parametric Approximate Dynamic Programming via the Kernel Method
This paper presents a novel non-parametric approximate dynamic programming (ADP) algorithm that enjoys graceful, dimension-independent approximation and sample complexity guarantees.
A Smoothed Approximate Linear Program
We present a novel linear program for the approximation of the dynamic programming cost-to-go function in high-dimensional stochastic control problems.
A Data-Driven Approach to Modeling Choice
We visit the following fundamental problem: For a `generic model of consumer choice (namely, distributions over preference lists) and a limited amount of data on how consumers actually make decisions (such as marginal preference information), how may one predict revenues from offering a particular assortment of choices?
