An ADMM Based Framework for AutoML Pipeline Configuration

Understand

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.