Efficient AutoML Pipeline Search with Matrix and Tensor Factorization

Chengrun Yang, Jicong Fan, Ziyang Wu, and Madeleine Udell This is an extended version of AutoML Pipeline Selection: Efficiently Navigating the Combinatorial Space (DOI: 10.1145/3394486.3403197) at the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 2020. Abstract--Data scientists seeking a good supervised learning model on a new dataset have many choices to make: they must preprocess the data, select features, possibly reduce the dimension, select an estimation algorithm, and choose hyperparameters for each of these pipeline components. With new pipeline components comes a combinatorial explosion in the number of choices! In this work, we design a new AutoML system to address this challenge: an automated system to design a supervised learning pipeline. Our system uses matrix and tensor factorization as surrogate models to model the combinatorial pipeline search space.