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Jane Wang · Joaquin Vanschoren · Erin Grant · Jonathan Schwarz · Francesco Visin · Jeff Clune · Roberto Calandra

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Event URL: https://meta-learn.github.io/2020/ »

Recent years have seen rapid progress in meta-learning methods, which transfer knowledge across tasks and domains to learn new tasks more efficiently, optimize the learning process itself, and even generate new learning methods from scratch. Meta-learning can be seen as the logical conclusion of the arc that machine learning has undergone in the last decade, from learning classifiers and policies over hand-crafted features, to learning representations over which classifiers and policies operate, and finally to learning algorithms that themselves acquire representations, classifiers, and policies.

Meta-learning methods are of substantial practical interest. For instance, they have been shown to yield new state-of-the-art automated machine learning algorithms and architectures, and have substantially improved few-shot learning systems. Moreover, the ability to improve one’s own learning capabilities through experience can also be viewed as a hallmark of intelligent beings, and there are strong connections with work on human learning in cognitive science and reward learning in neuroscience.

Fri 3:00 a.m. - 3:10 a.m. [iCal]
Introduction and opening remarks (introduction)
Fri 3:10 a.m. - 3:35 a.m. [iCal]
Invited talk #1 (invited talk)
Frank Hutter
Fri 3:35 a.m. - 3:40 a.m. [iCal]
Q/A for invited talk #1 (question period)
Frank Hutter
Fri 3:40 a.m. - 3:55 a.m. [iCal]
Contributed talk #1 (contributed talk)
Fri 4:00 a.m. - 5:00 a.m. [iCal]
Poster session #1 (poster session)
Fri 5:00 a.m. - 5:25 a.m. [iCal]
Invited talk #2 (invited talk)
Luisa M Zintgraf
Fri 5:25 a.m. - 5:30 a.m. [iCal]
Q/A for invited talk #2 (question period)
Luisa M Zintgraf
Fri 5:30 a.m. - 5:55 a.m. [iCal]
Invited talk #3 (invited talk)
Timothy Hospedales
Fri 5:55 a.m. - 6:00 a.m. [iCal]
Q/A for invited talk #3 (question period)
Timothy Hospedales
Fri 6:00 a.m. - 7:00 a.m. [iCal]
Break (break)
Fri 7:00 a.m. - 8:00 a.m. [iCal]
Poster session #2 (poster session)
Fri 8:00 a.m. - 8:25 a.m. [iCal]
Invited talk #4 (invited talk)
Louis Kirsch
Fri 8:25 a.m. - 8:30 a.m. [iCal]
Q/A for invited talk #4 (question period)
Louis Kirsch
Fri 8:30 a.m. - 8:55 a.m. [iCal]
Invited talk #5 (invited talk)
Li Fei-Fei
Fri 8:55 a.m. - 9:00 a.m. [iCal]
Q/A for invited talk #5 (question period)
Li Fei-Fei
Fri 9:00 a.m. - 10:00 a.m. [iCal]
Poster session #3 (poster session)
Fri 10:00 a.m. - 10:25 a.m. [iCal]
Invited talk #6 (invited talk)
Kate Rakelly
Fri 10:25 a.m. - 10:30 a.m. [iCal]
Q/A for invited talk #6 (question period)
Kate Rakelly
Fri 10:30 a.m. - 10:45 a.m. [iCal]
Contributed talk #2 (contributed talk)
Fri 10:45 a.m. - 11:00 a.m. [iCal]
Contributed talk #3 (contributed talk)
Fri 11:00 a.m. - 12:00 p.m. [iCal]
Panel discussion (discussion panel)

Author Information

Jane Wang (DeepMind)
Joaquin Vanschoren (Eindhoven University of Technology, OpenML)

Joaquin Vanschoren is an Assistant Professor in Machine Learning at the Eindhoven University of Technology. He holds a PhD from the Katholieke Universiteit Leuven, Belgium. His research focuses on meta-learning and understanding and automating machine learning. He founded and leads OpenML.org, a popular open science platform that facilitates the sharing and reuse of reproducible empirical machine learning data. He obtained several demo and application awards and has been invited speaker at ECDA, StatComp, IDA, AutoML@ICML, CiML@NIPS, AutoML@PRICAI, MLOSS@NIPS, and many other occasions, as well as tutorial speaker at NIPS and ECMLPKDD. He was general chair at LION 2016, program chair of Discovery Science 2018, demo chair at ECMLPKDD 2013, and co-organizes the AutoML and meta-learning workshop series at NIPS 2018, ICML 2016-2018, ECMLPKDD 2012-2015, and ECAI 2012-2014. He is also editor and contributor to the book 'Automatic Machine Learning: Methods, Systems, Challenges'.

Erin Grant (UC Berkeley)
Jonathan Schwarz (DeepMind & Gatsby Unit, UCL)
Francesco Visin (DeepMind)
Jeff Clune (Uber AI Labs)
Roberto Calandra (Facebook AI Research)

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