A brief Review of the ChaLearn AutoML Challenge: Any-time Any-dataset Learning without Human Intervention

By Isabelle M Guyon, Imad Chaabane, Hugo Jair Escalante, Sergio Escalera, Damir Jajetic, James Robert Lloyd, Núria Macià, Bisakha Ray, Lukasz Romaszko, Michèle Sebag and 3 more. 2016

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Citations in the corpus

Survey on Automated Machine Learning (2019, methodology): “To solve some of these problems, the ChaLearn AutoML challenge (Guyon et al., 2015, 2016, 2018) has been introduced.”
An Open Source AutoML Benchmark (2019, methodology): “Datasets We selected 39 classiﬁcation datasets from previous AutoML papers (Thornton et al., 2013), competitions (Isabelle et al., 2016), and machine learning benchmarks (Bischl et al., 2017a) 3 , according to a predeﬁned list of criteria.”
Probabilistic Matrix Factorization for Automated Machine Learning (2017, methodology): “In this section, we compare our method to a series of baselines as well as to auto-sklearn (Feurer et al., 2015), the current state-of-the-art approach and overall winner of the ChaLearn AutoML competition (Guyon et al., 2016).”
Analysing the Overfit of the Auto-sklearn Automated Machine Learning Tool (2019, methodology): “The state-of-the-art method for Auto-ML is the auto-sklearn tool [3, 8], which was the overall winner of the ﬁrst ChaLearn Auto-ML challenge [4], and a variant of auto-sklearn also won the second, latest AutoML challenge [1].”
Deep Learning in the Wild (2018, background): “Scientific state-of-the-art Auto-sklearn [17] is the most successful automated machine learning framework in past competitions [23].”
Autostacker: a compositional evolutionary learning system (2018, result): “We further compare our results to those of the TPOT model [29], one of the more recent and popular AutoML systems, as well as AutoSklearn [23], which won 1st place in the ﬁnal phase of 2016 AutoML challenge [26].”
Analysis of the AutoML Challenge Series 2015-2018 (2019, background): “, 2015a) and (Guyon et al., 2016) we review milestone and final results presented at the ICML 2015 and 2016 AutoML workshops.”
P4ML: A Phased Performance-Based Pipeline Planner for Automated Machine Learning (2018, background): “Auto-sklearn (Feurer et al., 2015a), the overall winner of the ChaLearn AutoML challenge (Guyon et al., 2016), extends the Bayesian optimization approach of the Auto-WEKA system.”