Sarath Sreedharan, Tathagata Chakraborti, et al.
AAAI 2020
We study the AutoML problem of automatically configuring machine learning pipelines by jointly selecting algorithms and their appropriate hyper-parameters for all steps in supervised learning pipelines. This black-box (gradient-free) optimization with mixed integer & continuous variables is a challenging problem. We propose a novel AutoML scheme by leveraging the alternating direction method of multipliers (ADMM). The proposed framework is able to (i) decompose the optimization problem into easier sub-problems that have a reduced number of variables and circumvent the challenge of mixed variable categories, and (ii) incorporate black-box constraints alongside the black-box optimization objective. We empirically evaluate the flexibility (in utilizing existing AutoML techniques), effectiveness (against open source AutoML toolkits), and unique capability (of executing AutoML with practically motivated black-box constraints) of our proposed scheme on a collection of binary classification data sets from UCI ML & OpenML repositories. We observe that on an average our framework provides significant gains in comparison to other AutoML frameworks (Auto-sklearn & TPOT), highlighting the practical advantages of this framework.
Sarath Sreedharan, Tathagata Chakraborti, et al.
AAAI 2020
Gosia Lazuka, Andreea Simona Anghel, et al.
SC 2024
Yidi Wu, Thomas Bohnstingl, et al.
ICML 2025
Ben Fei, Jinbai Liu
IEEE Transactions on Neural Networks