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In order to achieve state-of-the-art performance, modern machine learningtechniques require careful data pre-processing and hyperparameter tuning.Moreover, given the ever increasing number of machine learning models beingdeveloped, model selection is becoming increasingly important. Automating theselection and tuning of machine learning pipelines consisting of datapre-processing methods and machine learning models, has long been one of thegoals of the machine learning community. In this paper, we tackle thismeta-learning task by combining ideas from collaborative filtering and Bayesianoptimization. Using probabilistic matrix factorization techniques andacquisition functions from Bayesian optimization, we exploit experimentsperformed in hundreds of different datasets to guide the exploration of thespace of possible pipelines. In our experiments, we show that our approachquickly identifies high-performing pipelines across a wide range of datasets,significantly outperforming the current state-of-the-art.

Probabilistic Matrix Factorization for Automated Machine Learning