Functional Bandits
Machine Learning
2014-05-13 v1 Machine Learning
Abstract
We introduce the functional bandit problem, where the objective is to find an arm that optimises a known functional of the unknown arm-reward distributions. These problems arise in many settings such as maximum entropy methods in natural language processing, and risk-averse decision-making, but current best-arm identification techniques fail in these domains. We propose a new approach, that combines functional estimation and arm elimination, to tackle this problem. This method achieves provably efficient performance guarantees. In addition, we illustrate this method on a number of important functionals in risk management and information theory, and refine our generic theoretical results in those cases.
Cite
@article{arxiv.1405.2432,
title = {Functional Bandits},
author = {Long Tran-Thanh and Jia Yuan Yu},
journal= {arXiv preprint arXiv:1405.2432},
year = {2014}
}