In this talk, I will discuss the technical methods behind recent progress towards robust and efficient AutoML systems. After a brief recap of the early AutoML systems Auto-WEKA and Auto-sklearn, I will discuss the next generation, Auto-learn 2.0 and Auto-PyTorch. 

The talk will focus on the components that make these approaches far more efficient than Auto-sklearn 1.0, including practical considerations, multi-fidelity optimization, portfolio construction, and automated policy selection. 

This methods talk will be complemented by a hands-on tutorial on Auto-sklearn 2.0 given by Matthias Feurer and Katharina Eggensperger.

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Instructor's Bio

Frank Hutter, PhD

 Professor Of Computer Science | University of Freiburg

Frank Hutter is a Full Professor for Machine Learning at the Computer Science Department of the University of Freiburg (Germany), as well as Chief Expert AutoML at the Bosch Center for Artificial Intelligence.


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    Evolution of Efficient and Robust AutoML Systems

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