We are organizing a series of virtual seminars about Automatic Machine Learning (AutoML) as part of the ELLIS units Berlin and Freiburg. The idea is to have bi-weekly, one hour sessions with a 40 minute talk followed by a 20 minute Q/A session. The seminars are free and open to anyone. If you are interested in getting notified the details about the seminars and how to join, please sign up to the group listed below.

Talks will not be recorded, since some attendees would require additional approval to join, and to encourage questions during the talk.

We do not share additional details only to avoid spam, everyone will be accepted in the group and invited to all seminars.

Signup

To get the invites for the seminars, sign up here.

Instructions to join the virtual seminars are available here.

Upcoming seminars

All seminars are happening on Thursday 4-5pm (CET), unless the description specifies a different day/time.

• Nov. 19th – Sebastian Flennerhag (Deepmind) - Jointly learning a model and its learning rule
• Dec. 3rd – Binxin (Robin) Ru (University of Oxford) - Interpretable Neural Architecture Search and Efficient Performance Estimation

Become a speaker

If you want to become a speaker, please send an email to automl-seminars - at - googlegroups - dot - com or contact the organizers.

Organizers

• Giovanni Zappella (Amazon)
• Aaron Klein (Amazon)
• Arber Zela (University of Freiburg)
• Frank Hutter (University of Freiburg)
• Cédric Archambeau (Amazon)

Past seminars

• David Salinas (Naver Labs): “A quantile-based approach for hyperparameter transfer learning”
• Fabio Maria Carlucci (Huawei): Why NAS evaluation is frustratingly hard and how Generator-based optimization can help” (Slides)
• Matthias Seeger (Amazon): “Model-based Asynchronous Hyperparameter and Neural Architecture Search”
• Valerio Perrone (Amazon): “Fair Bayesian Optimization”(ICML Video)
• Frank Hutter (University of Freiburg) - Neural Architecture Search: Coming of Age(slides)
• Joaquin Vanschoren (Eindhoven University of Technology) - Towards ever-learning AutoML
• Matthias Polozeck (Uber AI) - Scalable Bayesian optimization for Industrial Applications
• Rodolphe Jenatton (Google Brain) - Hyperparameter Ensembles for Robustness and Uncertainty Quantification
• Zohar Karnin (Amazon AWS) - Practical and sample efficient zero-shot HPO
• Colin White (Abacus.ai) - Bananas, Encodings, and Local Search: Insights into Neural Architecture Search (ref1,ref2,ref3)