On Making Stochastic Classifiers Deterministic

Andrew Cotter, Maya Gupta, Harikrishna Narasimhan‎

Abstract: Stochastic classifiers arise in a number of machine learning problems, and have become especially prominent of late, as they often result from constrained optimization problems, e.g. for fairness, churn, or custom loss optimization. Despite their utility, the inherent randomness of stochastic classifiers may be problematic to use in practice for a variety of practical reasons. In this paper, we attempt to answer the theoretical question of how well a stochastic classifier can be approximated by a deterministic one, and compare several possible approaches, proving lower and upper bounds. We also experimentally investigate the pros and cons of these methods, not only in regard to how successfully each deterministic classifier approximates the original stochastic classifier, but also in terms of how well each addresses the other issues that can make stochastic classifiers undesirable.

Code Link: https://github.com/google-research/google-research/tree/master/stochastic_to_deterministic

CMT Num: 5841