An ADMM Based Framework for AutoML Pipeline Configuration


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



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.




How to Cite

Liu, S., Ram, P., Vijaykeerthy, D., Bouneffouf, D., Bramble, G., Samulowitz, H., Wang, D., Conn, A., & Gray, A. (2020). An ADMM Based Framework for AutoML Pipeline Configuration. Proceedings of the AAAI Conference on Artificial Intelligence, 34(04), 4892-4899.



AAAI Technical Track: Machine Learning