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Fri Jun 14 08:30 AM -- 06:00 PM (PDT) @ Grand Ballroom B
6th ICML Workshop on Automated Machine Learning (AutoML 2019)
Frank Hutter · Joaquin Vanschoren · Katharina Eggensperger · Matthias Feurer · Matthias Feurer

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.

Welcome (Presentation)
Keynote by Peter Frazier: Grey-box Bayesian Optimization for AutoML (Keynote Talk)
Poster Session 1 (all papers) (Poster Session)
Keynote by Rachel Thomas: Lessons Learned from Helping 200,000 non-ML experts use ML (Keynote Talk)
Contributed Talk 1: A Boosting Tree Based AutoML System for Lifelong Machine Learning (Contributed Talk)
Poster Session 2 (all papers) (Poster Session)
Lunch Break (Break)
Keynote by Jeff Dean: An Overview of Google's Work on AutoML and Future Directions (Keynote Talk)
Contributed Talk 2: Transfer NAS: Knowledge Transfer between Search Spaces with Transformer Agents (Contributed Talk)
Poster Session 3 (all papers) (Poster Session)
Contributed Talk 3: Random Search and Reproducibility for Neural Architecture Search (Contributed Talk)
Keynote by Charles Sutton: Towards Semi-Automated Machine Learning (Keynote Talk)
Panel Discussion (Panel)
Closing Remarks (Presentation)