Constrained Learning with Non-Convex Losses

TL;DR

This paper introduces a novel approach to constrained statistical learning by transforming non-convex problems into unconstrained ones in the empirical dual domain, enabling better handling of constraints in critical applications.

Contribution

It proposes a dual domain learning framework that makes constrained non-convex problems tractable and provides theoretical bounds and a practical algorithm for such problems.

Findings

Bounded the empirical duality gap for the proposed approach

Developed a practical constrained learning algorithm

Validated the approach in fairness and adversarial robustness applications

Abstract

Though learning has become a core component of modern information processing, there is now ample evidence that it can lead to biased, unsafe, and prejudiced systems. The need to impose requirements on learning is therefore paramount, especially as it reaches critical applications in social, industrial, and medical domains. However, the non-convexity of most modern statistical problems is only exacerbated by the introduction of constraints. Whereas good unconstrained solutions can often be learned using empirical risk minimization, even obtaining a model that satisfies statistical constraints can be challenging. All the more so, a good one. In this paper, we overcome this issue by learning in the empirical dual domain, where constrained statistical learning problems become unconstrained and deterministic. We analyze the generalization properties of this approach by bounding the empirical duality gap -- i.e., the difference between our approximate, tractable solution and the solution of the original (non-convex) statistical problem -- and provide a practical constrained learning algorithm. These results establish a constrained counterpart to classical learning theory, enabling the explicit use of constraints in learning. We illustrate this theory and algorithm in rate-constrained learning applications arising in fairness and adversarial robustness.