Automated Machine Learning for Unsupervised Tabular Tasks

Abstract In this work, we present Learning to Learn with Optimal Transport for Unsupervised Scenarios (LOTUS), a simple yet effective method to perform model selection for multiple unsupervised machine learning (ML) tasks such as outlier detection and clustering. Our intuition behind this work is that a machine learning pipeline will perform well in a new dataset if it previously worked well on datasets with a similar underlying data distribution. We use Optimal Transport distances to find this similarity between unlabeled tabular datasets and recommend machine learning pipelines with one unified single method on two downstream unsupervised tasks: outlier detection and clustering. We present the effectiveness of our approach with experiments against strong baselines and show that LOTUS is a very promising first step toward model selection for multiple unsupervised ML tasks.