This paper presents a benchmark of supervised Automated Machine Learning (AutoML) tools. Firstly, we analyze the characteristics of eight recent open-source AutoML tools (Auto-Keras, Auto-PyTorch, Auto-Sklearn, AutoGluon, H2O AutoML, rminer, TPOT and TransmogrifAI) and describe twelve popular OpenML datasets that were used in the benchmark (divided into regression, binary and multi-class classification tasks). Then, we perform a comparison study with hundreds of computational experiments based on three scenarios: General Machine Learning (GML), Deep Learning (DL) and XGBoost (XGB). To select the best tool, we used a lexicographic approach, considering first the average prediction score for each task and then the computational effort. The best predictive results were achieved for GML, which were further compared with the best OpenML public results. Overall, the best GML AutoML tools obtained competitive results, outperforming the best OpenML models in five datasets. These results confirm the potential of the general-purpose AutoML tools to fully automate the Machine Learning (ML) algorithm selection and tuning.

/pdf/a-comparison-of-automl-tools-for-machine-learning-deep-33myfrok54.pdf

A Comparison of AutoML Tools for Machine Learning, Deep Learning and XGBoost

Deep generative models often perform poorly in real-world applications due to the heterogeneity of natural data sets. Heterogeneity arises from data containing different types of features (categorical, ordinal, continuous, etc.) and features of the same type having different marginal distributions. We propose an extension of variational autoencoders (VAEs) called VAEM to handle such heterogeneous data. VAEM is a deep generative model that is trained in a two stage manner such that the first stage provides a more uniform representation of the data to the second stage, thereby sidestepping the problems caused by heterogeneous data. We provide extensions of VAEM to handle partially observed data, and demonstrate its performance in data generation, missing data prediction and sequential feature selection tasks. Our results show that VAEM broadens the range of real-world applications where deep generative models can be successfully deployed.

pdf/vaem-a-deep-generative-model-for-heterogeneous-mixed-type-jnzk4a7tfk.pdf

VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data

VAEM: a Deep Generative Model for Heterogeneous Mixed Type Data.

With ever more data becoming available, there has been a recent drive to develop modelling tools for heterogeneous data sets such as electronic healthcare records. Therein appears both Bayesian non-parametric latent feature models, as well as methods for automatically determining the statistical data type (e.g. ordinal or categorical) of the attributes present in the data. We present a model which combines both of these attractive features in an end-to-end framework. By jointly learning the model complexity and statistical type of the data, we demonstrate that redundant information can be discarded while higher accuracy statistical type estimates are produced on real experimental data.

An Automatic Type-Inferential General Latent Feature Model

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An Automatic Type-Inferential General Latent Feature Model