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arXiv · 2006.09635

Solving Constrained CASH Problems with ADMM

Parikshit Ram·Sijia Liu·Deepak Vijaykeerthi·Dakuo Wang·Djallel Bouneffouf·Greg Bramble·Horst Samulowitz·Alexander G. Gray
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Abstract

The CASH problem has been widely studied in the context of automated configurations of machine learning (ML) pipelines and various solvers and toolkits are available. However, CASH solvers do not directly handle black-box constraints such as fairness, robustness or other domain-specific custom constraints. We present our recent approach [Liu, et al., 2020] that leverages the ADMM optimization framework to decompose CASH into multiple small problems and demonstrate how ADMM facilitates incorporation of black-box constraints.

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Parikshit Ram, Sijia Liu, Deepak Vijaykeerthi, Dakuo Wang, Djallel Bouneffouf, Greg Bramble, Horst Samulowitz, Alexander G. Gray. 2020-06-17. Solving Constrained CASH Problems with ADMM. https://arxiv.org/abs/2006.09635

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