Sequential Automated Machine Learning: Bandits-driven Exploration using a Collaborative Filtering Representation

Abstract

The goal of Automated Machine Learning (AutoML) is to make Machine Learning (ML) tools more accessible. Collaborative Filtering (CF) methods have shown great success in automating the creation of machine learning pipelines. In this work, we frame the AutoML problem under a sequential setting where datasets arrive one at a time. On each dataset, an agent can try a small number of pipelines (exploration) before recommending a pipeline for this dataset (recommendation). The goal is to maximize the performance of the recommended pipelines over the sequence of datasets. More specifically, we focus on the exploration policy used for selecting the pipelines to explore before making the recommendation. We propose an approach based on the LinUCB bandit algorithm that leverages the latent representations extracted from matrix factorization (MF). We show that the exploration policy impacts the recommendation performance and that MF-based latent representations are more useful for exploration than for recommendation.

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