2021
DOI: 10.48550/arxiv.2103.05134
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Constrained Learning with Non-Convex Losses

Luiz F. O. Chamon,
Santiago Paternain,
Miguel Calvo-Fullana
et al.

Abstract: Though learning has become a core technology of modern information processing, there is now ample evidence that it can lead to biased, unsafe, and prejudiced solutions. 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 learning problems is only exacerbated by the introduction of constraints. Whereas good unconstrained solutions can often be learned using empiric… Show more

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“…In the existing literature, classical meta-learning, hyperparameter optimization problems do not consider constraints. There is an imperative need for constrained learning to incorporate safety, fairness, and other high-level specifications [24], [5]. Main motivation arises from the fact that meta-learning can be formed as a bilevel optimization problem where the "inner" optimization deals with the adaptation for a particular task and the "outer" optimization can be modified for meta-training with the safety constraints in order to restrict biased and risky scenarios.…”
Section: Introductionmentioning
confidence: 99%
“…In the existing literature, classical meta-learning, hyperparameter optimization problems do not consider constraints. There is an imperative need for constrained learning to incorporate safety, fairness, and other high-level specifications [24], [5]. Main motivation arises from the fact that meta-learning can be formed as a bilevel optimization problem where the "inner" optimization deals with the adaptation for a particular task and the "outer" optimization can be modified for meta-training with the safety constraints in order to restrict biased and risky scenarios.…”
Section: Introductionmentioning
confidence: 99%