6th ICML Workshop on Automated Machine Learning (AutoML 2019)
Frank Hutter · Joaquin Vanschoren · Katharina Eggensperger · Matthias Feurer · Matthias Feurer

Fri Jun 14th 08:30 AM -- 06:00 PM @ Grand Ballroom B
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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. 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.

09:00 AM Welcome (Presentation) Frank Hutter
09:05 AM Keynote by Peter Frazier: Grey-box Bayesian Optimization for AutoML (Keynote Talk) Video »  Peter I Frazier
09:40 AM Poster Session 1 (all papers) (Poster Session)
Matilde Gargiani, Yochai Zur, Chaim Baskin, Evgenii Zheltonozhskii, Liam Li, Ameet Talwalkar, Xuedong Shang, HARKIRAT Behl, Atilim Gunes Baydin, Ivo Couckuyt, Tom Dhaene, Chieh Hubert Lin, Wei Wei, Min Sun, Orchid Majumder, Michele Donini, Yoshihiko Ozaki, Ryan P. Adams, Christian Geißler, Ping Luo, zhanglin peng, , Ruimao Zhang, John Langford, Rich Caruana, Debadeepta Dey, Charles Weill, Xavi Gonzalvo, Scott Yang, Scott Yak, Eugen Hotaj, vlejd Macko, Mehryar Mohri, Corinna Cortes, Stefan Webb, JP Chen, Martin Jankowiak, Noah Goodman, Aaron Klein, Frank Hutter, Mojan Javaheripi, Mohammad Samragh, Sungbin Lim, Taesup Kim, SUNGWOONG KIM, Michael Volpp, Iddo Drori, Yamuna Krishnamurthy, Kyunghyun Cho, Stanislaw Jastrzebski, Quentin de Laroussilhe, Mingxing Tan, Xiao Ma, Neil Houlsby, Andrea Gesmundo, Zalán Borsos, Krzysztof Maziarz, Felipe Petroski Such, Joel Lehman, Ken Stanley, Jeff Clune, Pieter Gijsbers, Joaquin Vanschoren, Felix Mohr, Eyke Hüllermeier, Zheng Xiong, Wenpeng Zhang, wenwu zhu, Weijia Shao, Aleksandra Faust, Michal Valko, Michael Y Li, hugojair Escalante, Marcel Wever, Andrey Khorlin, Tara Javidi, Anthony Francis, Saurajit Mukherjee, Jungtaek Kim, Michael McCourt, Saehoon Kim, Tackgeun You, Seungjin Choi, Nicolas Knudde, Alexander Tornede, Ghassen Jerfel
11:00 AM Keynote by Rachel Thomas: Lessons Learned from Helping 200,000 non-ML experts use ML (Keynote Talk) Video »  RACHEL Thomas
11:35 AM Contributed Talk 1: A Boosting Tree Based AutoML System for Lifelong Machine Learning (Contributed Talk) Video »  Zheng Xiong
12:00 PM Poster Session 2 (all papers) (Poster Session)
12:50 PM Lunch Break (Break)
02:00 PM Keynote by Jeff Dean: An Overview of Google's Work on AutoML and Future Directions (Keynote Talk) Video »  Jeff Dean
02:35 PM Contributed Talk 2: Transfer NAS: Knowledge Transfer between Search Spaces with Transformer Agents (Contributed Talk) Video »  Zalán Borsos
03:00 PM Poster Session 3 (all papers) (Poster Session)
04:00 PM Contributed Talk 3: Random Search and Reproducibility for Neural Architecture Search (Contributed Talk) Video »  Liam Li
04:25 PM Keynote by Charles Sutton: Towards Semi-Automated Machine Learning (Keynote Talk) Video »  Charles Sutton
05:00 PM Panel Discussion (Panel) Video »  Wenpeng Zhang, Charles Sutton, Liam Li, RACHEL Thomas, Erin LeDell
06:00 PM Closing Remarks (Presentation) Frank Hutter

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 (Eindhoven University of Technology)
Katharina Eggensperger (University of Freiburg)
Matthias Feurer (University of Freiburg)
Matthias Feurer (University of Freiburg)

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