Towards Further Automation in AutoML

By Matthias Feurer, Frank Hutter. 2018

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Marc-André Zöller
2. Marco F. Huber
3. Frank Hutter
4. Marius Thomas Lindauer
5. Matthias Feurer
6. Stefan Falkner
7. Katharina Eggensperger
8. Aaron Klein

Citations in the corpus

• Benchmark and Survey of Automated Machine Learning Frameworks (2019, background): “Research leading to frameworks with less hyperparameters is desirable (Feurer & Hutter, 2018).”
• Survey on Automated Machine Learning (2019, background): “Research leading to frameworks with less hyperparameters is desirable (Feurer and Hutter, 2018).”
• 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).”
• Analysing the Overfit of the Auto-sklearn Automated Machine Learning Tool (2019, methodology): “This conclusion is also actionable, since approaches can be developed to control learning-validation overfit during training, such as re-sampling the learning and validation sets or using cross-validation across SMACs iterations [2].”