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Constrained Learning with Non-Convex Losses. (arXiv:2103.05134v4 [cs.LG] UPDATED)
June 29, 2022, 1:11 a.m. | Luiz F. O. Chamon, Santiago Paternain, Miguel Calvo-Fullana, Alejandro Ribeiro
stat.ML updates on arXiv.org arxiv.org
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 …
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