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Workshop
7th ICML Workshop on Automated Machine Learning (AutoML 2020)
Frank Hutter · Joaquin Vanschoren · Marius Lindauer · Charles Weill · Katharina Eggensperger · Matthias Feurer · Matthias Feurer

Sat Jul 18 06:00 AM -- 02:55 PM (PDT) @
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Event URL: http://icml2020.automl.org/ »

Machine learning has achieved considerable successes in recent years, but this success often relies on human experts, who construct appropriate features, design learning architectures, set their hyperparameters, and develop new learning algorithms. Driven by the demand for off-the-shelf machine learning methods from an ever-growing community, the research area of AutoML targets the progressive automation of machine learning aiming to make effective methods available to everyone. Hence, the workshop targets a broad audience ranging from core machine learning researchers in different fields of ML connected to AutoML, such as neural architecture search, hyperparameter optimization, meta-learning, and learning to learn, to domain experts aiming to apply machine learning to new types of problems.

The schedule is wrt CEST (i.e., the time zone of Vienna)

Keywords: neural architecture search · automated data science · automated pipeline generation · automated machine learning · hyperparameter optimization

Author Information

Frank Hutter (University of Freiburg and Bosch Center for Artificial Intelligence)
Frank Hutter

Frank Hutter is a Full Professor for Machine Learning at the Computer Science Department of the University of Freiburg (Germany), where he has been a faculty member since 2013. Before that, he was at the University of British Columbia (UBC) for eight years, for his PhD and postdoc. Frank's main research interests lie in machine learning, artificial intelligence and automated algorithm design. For his 2009 PhD thesis on algorithm configuration, he received the CAIAC doctoral dissertation award for the best thesis in AI in Canada that year, and with his coauthors, he received several best paper awards and prizes in international competitions on automated machine learning, SAT solving, and AI planning. Since 2016 he holds an ERC Starting Grant for a project on automating deep learning based on Bayesian optimization, Bayesian neural networks, and deep reinforcement learning.

Joaquin Vanschoren (TU Eindhoven / OpenML)

Joaquin Vanschoren is an assistant professor at the Eindhoven University of Technology. His research focuses on automated machine learning and meta-learning. He has received the Dutch Data prize in 2016 and an Amazon Research Award in 2019. He is a regular invited speaker at international conferences and workshops (including NeurIPS, ICML, the UN summit on AI for Good, and the Dutch eScience Symposium), and regularly gives talks at companies (e.g. Amazon Cambridge, Amazon Berlin, IBM New York). He also gives regular tutorials on meta-learning (including at NeurIPS 2018 and the ACDL summer school). He is co-editor and co-author of the reference book on Automated Machine Learning. He co-organizes the Automated Machine Learning workshop series at ICML and the meta-learning workshop series at NeurIPS. He was also general chair, program chair, and/or demo chair at European machine learning conferences. He also founded and runs OpenML.org, a popular open science platform for machine learning, with over 150.000 yearly users worldwide. His work has been mentioned in Science Magazine, ACM Explorations, KDnuggets, and Open Science Radio.

Marius Lindauer (Leibniz University Hannover)
Charles Weill (Google Research)
Katharina Eggensperger (University of Freiburg)
Matthias Feurer (University of Freiburg)
Matthias Feurer (University of Freiburg)

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