Constrained Learning with NonConvex Losses
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 nonconvexity 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 (nonconvex) 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 rateconstrained learning applications arising in fairness and adversarial robustness.
 Publication:

arXiv eprints
 Pub Date:
 March 2021
 DOI:
 10.48550/arXiv.2103.05134
 arXiv:
 arXiv:2103.05134
 Bibcode:
 2021arXiv210305134C
 Keywords:

 Computer Science  Machine Learning;
 Mathematics  Statistics Theory;
 Statistics  Machine Learning
 EPrint:
 IEEE Transactions on Information Theory