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.
Two-block vs. Multi-block ADMM: An empirical evaluation of convergence
Lawrence Livermore National Laboratory（劳伦斯利弗莫尔国家实验室）；College of Humanities and Sciences（人文与科学学院）；Northeast Normal University（东北师范大学）；University of Minnesota, Twin Cities（明尼苏达大学双城分校）
University of Maryland（马里兰大学）；Instituto de Telecomunicações（电信研究所）；Instituto Superior Técnico（高等技术学院）；Universidade de Lisboa（里斯本大学）；Cornell University（康奈尔大学）
Nonconvex Zeroth-Order Stochastic ADMM Methods with Lower Function Query Complexity
University of Pittsburgh（匹兹堡大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）；Duke University（杜克大学）；University of Maryland College Park（马里兰大学帕克分校）
Asynchronous Distributed ADMM for Large-Scale Optimization- Part II: Linear Convergence Analysis and Numerical Performance
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；University of Minnesota（明尼苏达大学）；Iowa State University（爱荷华州立大学）；East China Normal University（华东师范大学）
Adaptive Relaxed ADMM: Convergence Theory and Practical Implementation
University of Maryland（马里兰大学）；Instituto de Telecomunicações（电信研究所）；Instituto Superior Técnico（高等理工学院）；Universidade de Lisboa（里斯本大学）；Hong Kong Baptist University（香港浸会大学）；Cornell University（康奈尔大学）
Asynchronous Distributed ADMM for Large-Scale Optimization- Part I: Algorithm and Convergence Analysis
The Chinese University of Hong Kong, Shenzhen（香港中文大学（深圳））；Iowa State University（爱荷华州立大学）；University of Minnesota（明尼苏达大学）；East China Normal University（华东师范大学）
