@article{Liu_Ram_Vijaykeerthy_Bouneffouf_Bramble_Samulowitz_Wang_Conn_Gray_2020, title={An ADMM Based Framework for AutoML Pipeline Configuration}, volume={34}, url={https://ojs.aaai.org/index.php/AAAI/article/view/5926}, DOI={10.1609/aaai.v34i04.5926}, abstractNote={<p>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 <em>black-box</em> (gradient-free) optimization with <em>mixed</em> 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.</p>}, number={04}, journal={Proceedings of the AAAI Conference on Artificial Intelligence}, author={Liu, Sijia and Ram, Parikshit and Vijaykeerthy, Deepak and Bouneffouf, Djallel and Bramble, Gregory and Samulowitz, Horst and Wang, Dakuo and Conn, Andrew and Gray, Alexander}, year={2020}, month={Apr.}, pages={4892-4899} }