Generalization via Derandomization

Part of Proceedings of the International Conference on Machine Learning 1 pre-proceedings (ICML 2020)

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Authors

Jeffrey Negrea, Gintare Karolina Dziugaite, Daniel Roy

Abstract

<p>We propose to study the generalization error of a learned predictor h^ in terms of that of a surrogate (potentially randomized) classifier that is coupled to h^ and designed to trade empirical risk for control of generalization error. In the case where h^ interpolates the data, it is interesting to consider theoretical surrogate classifiers that are partially derandomized or rerandomized, e.g., fit to the training data but with modified label noise. We show that replacing h^ by its conditional distribution with respect to an arbitrary sigma-field is a viable method to derandomize. We give an example, inspired by the work of Nagarajan and Kolter (2019), where the learned classifier h^ interpolates the training data with high probability, has small risk, and, yet, does not belong to a nonrandom class with a tight uniform bound on two-sided generalization error. At the same time, we bound the risk of h^ in terms of a surrogate that is constructed by conditioning and shown to belong to a nonrandom class with uniformly small generalization error. </p>