Practical Automated Machine Learning for the AutoML Challenge 2018

By Matthias Feurer, Katharina Eggensperger, Stefan Falkner, Marius Thomas Lindauer, Frank Hutter. 2018

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Citations in the corpus

Benchmark and Survey of Automated Machine Learning Frameworks (2019, methodology): “Auto-Sklearn auto-sklearn (Feurer et al., 2015a, 2018) is a tool for building classification and regression pipelines.”
Survey on Automated Machine Learning (2019, methodology): “auto-sklearn (Feurer et al., 2015a, 2018) is a tool for building classification and regression pipelines.”
Analysing the Overfit of the Auto-sklearn Automated Machine Learning Tool (2019, background): “of auto-sklearn also won the second, latest AutoML challenge [1].”
Auto-Sklearn 2.0: Hands-free AutoML via Meta-Learning (2020, methodology): “The work presented in this paper is in part based on two earlier workshop papers introducing some of the presented ideas in preliminary form (Feurer et al., 2018; Feurer and Hutter, 2018).”
Blockchain for AI: Review and Open Research Challenges (2019, background): “Table 1 shows few emerging trends in AI applications such as explainable AI [9], [30], digital twins [31], automated machine learning [32], hybrid learning models [33], and lean and augmented data learning [34], and the perceived benefits”
Hyperparameter Ensembles for Robustness and Uncertainty Quantification (2020, background): “, [19] and specifically for neural networks [53] as well as in computer vision [60] and genetics [35].”
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data (2020, methodology): “It helped auto-sklearn previously win time-constrained AutoML competitions (Feurer et al., 2018).”
Automated Machine Learning in Practice: State of the Art and Recent Results (2019, methodology): “Portfolio Hyperband [13], [43]: Inspired by PoSH Autosklearn [43] that combines a portfolio of initial configurations with successive halving (SH) and Bayesian optimization, we”