Constrained Learning with Non-Convex Losses
This work addresses the problem of ensuring safe and fair machine learning systems for applications in social, industrial, and medical domains, offering a foundational approach to constrained learning.
The paper tackles the challenge of imposing constraints on learning problems with non-convex losses, which is crucial for preventing biased or unsafe systems in critical applications. It proposes a method that learns in the empirical dual domain to make constrained problems tractable, providing generalization bounds and a practical algorithm, with applications in fairness and adversarial robustness.
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.