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

Fri Dec 11 03:00 AM -- 12:00 PM (PST) @
Event URL: https://meta-learn.github.io/2020/ »

How to join the virtual workshop: The 2020 Workshop on Meta-Learning will be a series of streamed pre-recorded talks + live question-and-answer (Q&A) periods, and poster sessions on Gather.Town. You can participate by:
* Accessing the livestream on our [ protected link dropped ] 2;
* MetaLearn 2020 Rocket.Chat!
* Entering panel discussion questions in this sli.do!

Focus of the workshop: 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 also 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.

Author Information

Jane Wang (DeepMind)

Jane Wang is a research scientist at DeepMind on the neuroscience team, working on meta-reinforcement learning and neuroscience-inspired artificial agents. Her background is in physics, complex systems, and computational and cognitive neuroscience.

Joaquin Vanschoren (Eindhoven University of Technology, OpenML)
Joaquin Vanschoren

Joaquin Vanschoren is Associate Professor in Machine Learning at the Eindhoven University of Technology. He holds a PhD from the Katholieke Universiteit Leuven, Belgium. His research focuses on understanding and automating machine learning, meta-learning, and continual learning. He founded and leads OpenML.org, a popular open science platform with over 250,000 users that facilitates the sharing and reuse of machine learning datasets and models. He is a founding member of the European AI networks ELLIS and CLAIRE, and an active member of MLCommons. He obtained several awards, including an Amazon Research Award, an ECMLPKDD Best Demo award, and the Dutch Data Prize. He was a tutorial speaker at NeurIPS 2018 and AAAI 2021, and gave over 30 invited talks. He co-initiated the NeurIPS Datasets and Benchmarks track and was NeurIPS Datasets and Benchmarks Chair from 2021 to 2023. He also co-organized the AutoML workshop series at ICML, and the Meta-Learning workshop series at NeurIPS. He is editor-in-chief of DMLR (part of JMLR), as well as an action editor for JMLR and machine learning moderator for ArXiv. He authored and co-authored over 150 scientific papers, as well as reference books on Automated Machine Learning and Meta-learning.

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

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