arxiv:2006.09635

Solving Constrained CASH Problems with ADMM

Published on Jun 17, 2020

Authors:

Dakuo Wang ,

Abstract

The paper proposes using ADMM to decompose the CASH problem and integrate black-box constraints in automated ML pipeline configurations.

AI-generated summary

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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