# Solving Constrained CASH Problems with ADMM

arXiv: https://arxiv.org/abs/2006.09635 · HTML: https://mlsanity.com/paper/2006.09635/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 17 Jun 2020.

## Built on by (most cited first, 2 of 2)

- [Etat de l'art sur l'application des bandits multi-bras](https://mlsanity.com/paper/2101.00001/) (2021)
- [Toward Theoretical Guidance for Two Common Questions in Practical Cross-Validation based Hyperparameter Selection](https://mlsanity.com/paper/2301.05131/) (2023)

## Abstract (arXiv)

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.