An ADMM Based Framework for AutoML Pipeline Configuration

Sijia Liu; Parikshit Ram; Deepak Vijaykeerthy; Djallel Bouneffouf; Gregory Bramble; Horst Samulowitz; Dakuo Wang; Andrew Conn; Alexander Gray

arXiv · 1905.00424

An ADMM Based Framework for AutoML Pipeline Configuration

Sijia Liu·Parikshit Ram·Deepak Vijaykeerthy·Djallel Bouneffouf·Gregory Bramble·Horst Samulowitz·Dakuo Wang·Andrew Conn·Alexander Gray

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Abstract

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 along-side 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.

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Sijia Liu, Parikshit Ram, Deepak Vijaykeerthy, Djallel Bouneffouf, Gregory Bramble, Horst Samulowitz, Dakuo Wang, Andrew Conn, Alexander Gray. 2019-12-06. An ADMM Based Framework for AutoML Pipeline Configuration. https://arxiv.org/abs/1905.00424

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