Malign Overfitting: Interpolation Can Provably Preclude Invariance

TL;DR

This paper demonstrates that interpolating classifiers in over-parameterized models inherently lack invariance properties crucial for fairness and robustness, and proposes a non-interpolating method to achieve invariance.

Contribution

The paper provides a theoretical proof that interpolating classifiers cannot satisfy invariance properties and introduces an algorithm that learns invariant, non-interpolating classifiers.

Findings

Interpolating classifiers fail to satisfy invariance properties.

A non-interpolating algorithm can successfully learn invariant classifiers.

Theoretical results validated on simulated data and Waterbirds dataset.

Abstract

Learned classifiers should often possess certain invariance properties meant to encourage fairness, robustness, or out-of-distribution generalization. However, multiple recent works empirically demonstrate that common invariance-inducing regularizers are ineffective in the over-parameterized regime, in which classifiers perfectly fit (i.e. interpolate) the training data. This suggests that the phenomenon of "benign overfitting", in which models generalize well despite interpolating, might not favorably extend to settings in which robustness or fairness are desirable. In this work we provide a theoretical justification for these observations. We prove that -- even in the simplest of settings -- any interpolating learning rule (with arbitrarily small margin) will not satisfy these invariance properties. We then propose and analyze an algorithm that -- in the same setting -- successfully learns a non-interpolating classifier that is provably invariant. We validate our theoretical observations on simulated data and the Waterbirds dataset.