On Making Stochastic Classifiers Deterministic
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 losses. Despite their utility, the inherent randomness of stochastic classifiers may cause them to 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 different 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 (1)
Tasks
FairnessSimilar Papers 제목 키워드 기반
A Framework for Bounding Deterministic Risk with PAC-Bayes: Applications to Majority Votes
PAC-Bayes is a popular and efficient framework for obtaining generalization guarantees in situations involving uncountable hypothesis spaces. Unfortunately, in its classical formulation, it only provides guarantees on th…
PAC-Bayesian Generalization Guarantees for Fairness on Stochastic and Deterministic Classifiers
Classical PAC generalization bounds on the prediction risk of a classifier are insufficient to provide theoretical guarantees on fairness when the goal is to learn models balancing predictive risk and fairness constraint…
On the disintegration of the stochastic majority vote: From PAC-Bayesian bounds to a self-bounding algorithm
Weighted majority votes are central to many successful ensemble methods. PAC-Bayesian theory provides tight generalization guarantees for such models by analyzing the expected risk of stochastic classifiers, while analyz…
Polynomial Regret Concentration of UCB for Non-Deterministic State Transitions
Monte Carlo Tree Search (MCTS) has proven effective in solving decision-making problems in perfect information settings. However, its application to stochastic and imperfect information domains remains limited. This pape…
Decision MakingCounterfactually Comparing Abstaining Classifiers
Abstaining classifiers have the option to abstain from making predictions on inputs that they are unsure about. These classifiers are becoming increasingly popular in high-stakes decision-making problems, as they can wit…
Causal InferencecounterfactualDecision Making