论文标题
解决ADMM的限制现金问题
Solving Constrained CASH Problems with ADMM
论文作者
论文摘要
现金问题已在机器学习（ML）管道的自动配置和各种求解器和工具包的背景下进行了广泛的研究。但是，现金求解器不会直接处理黑盒约束，例如公平，鲁棒性或其他特定领域的自定义约束。我们介绍了我们最近的方法[Liu等，2020]，该方法利用ADMM优化框架将现金分解为多个小问题，并证明ADMM如何促进黑盒约束的融合。
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.
