Title: Probabilistic Matrix Factorization for Automated Machine Learning,
Abstract: In order to achieve state-of-the-art performance, modern machine learning
techniques require careful data pre-processing and hyperparameter tuning.
Moreover, given the ever increasing number of machine learning models being
developed, model selection is becoming increasingly important. Automating the
selection and tuning of machine learning pipelines consisting of data
pre-processing methods and machine learning models, has long been one of the
goals of the machine learning community. In this paper, we tackle this
meta-learning task by combining ideas from collaborative filtering and Bayesian
optimization. Using probabilistic matrix factorization techniques and
acquisition functions from Bayesian optimization, we exploit experiments
performed in hundreds of different datasets to guide the exploration of the
space of possible pipelines. In our experiments, we show that our approach
quickly identifies high-performing pipelines across a wide range of datasets,
significantly outperforming the current state-of-the-art.