News & Updates
Sep 15, 2020: Reviewing mentorship sign-up & recommendation form released!
Sep 2, 2020: CMT open for submissions!
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.
Some of the fundamental questions that this workshop aims to address are:
- How can we exploit our domain knowledge to effectively guide the meta-learning process?
- What are the meta-learning processes in nature (e.g., in humans), and how can we take inspiration from them?
- Which machine learning approaches are best suited for meta-learning, in which circumstances, and why?
- What principles can we learn from meta-learning to help us design the next generation of learning systems?
- How can we design more sample-efficient meta-learning methods?
In this edition of the meta-learning workshop, we want to stimulate discussion on several key underlying and unsolved questions, particularly:
- Task distributions: What constitutes a distribution of tasks, domains, or problems? What does it mean to have learned this distribution, and to generalize outside of this distribution?
- Transfer/continual/lifelong learning: What is the relationship between meta-learning and transfer/continual or lifelong learning? How do the notions of “meta-train” and “meta-test” datasets map to continual or lifelong learning?
- Inductive biases: What is the role of inductive biases, be they algorithmic or architectural? How do they manifest in evolution, neural architecture search, etc.? What kinds of useful inductive biases can we learn from neuroscience or cognitive science?
This workshop aims to bring together researchers from all the different communities and topics that fall under the umbrella of meta-learning. We expect that the presence of these different communities will result in a fruitful exchange of ideas and stimulate an open discussion about the current challenges in meta-learning as well as possible solutions.
In terms of prospective participants, our main targets are machine learning researchers interested in the processes related to understanding and improving current meta-learning algorithms. Specific target communities within machine learning include, but are not limited to meta-learning, AutoML, reinforcement learning, deep learning, optimization, evolutionary computation, and Bayesian optimization. We also invite submissions from researchers who study human learning and neuroscience to provide a broad perspective to the attendees.
In terms of organizing this workshop in a virtual format, we understand that accessibility across time zones is a significant challenge for a virtual meeting. We will thus offer a mix of synchronous and asynchronous formats that allow participation despite this complication. In thinking about this format, we took inspiration from best practices that emerged from past conferences.
Specifically, we will require both invited speakers and authors of accepted papers to make pre-recorded videos available in advance, allowing registered participants to engage with the recordings at any time. This will be accompanied by live offerings such as a panel session and Q&A for invited talks or spotlights, where participants will be given the opportunity to submit and vote on questions using a Q&A platform during and in advance of the livestream. One of the organizers will facilitate as a moderator for each live session.
For our virtual poster sessions, we plan to use a tool that enables audience participation such as Gather.Town. We will allow authors to choose between one or more of three sessions held several hours apart, allowing a choice based on the most suitable time in their local time zone.
Finally, we will use a dedicated channel on a chat service throughout the event to provide a means of interaction between workshop participants. A schedule will be set up to include three poster sessions throughout the day to ensure that virtual attendees in all timezones will be able to participate in at least one.
More detailed instructions will be given closer to the workshop date.
- Timothy Hospedales (University of Edinburgh)
- Frank Hutter (University of Freiburg)
- Louis Kirsch (IDSIA)
- Fei-Fei Li (Stanford University)
- Kate Rakelly (UC Berkeley)
- Luisa Zintgraf (University of Oxford)
- Roberto Calandra (Facebook AI Research)
- Jeff Clune (OpenAI)
- Erin Grant (UC Berkeley)
- Jonathan Schwarz (University College London, Deepmind)
- Joaquin Vanschoren (Eindhoven University of Technology)
- Francesco Visin (DeepMind)
- Jane Wang (DeepMind)
- Submission deadline: 2 October 2020, 06:00 PM PDT
- Notification: 30 October 2020, by 06:00 PM PDT
- Camera-ready: 14 November 2020
- Workshop: 11 December 2020
The workshop schedule is aligned with 11 AM to 8 PM UTC; please see this converter for conversion to your specific time zone.
|Beijing (CST)||Berlin (CET)||London (UTC)||New York (EST)||Vancouver (PST)|
|19:00||12:00||11:00||06:00||03:00||Introduction and opening remarks|
|19:10||12:10||11:10||06:10||03:10||Invited talk 1|
|19:40||12:40||11:40||06:40||03:40||Contributed talk 1|
|20:00||13:00||12:00||07:00||04:00||Poster session 1|
|21:00||14:00||13:00||08:00||05:00||Invited talk 2|
|21:30||14:30||13:30||08:30||05:30||Invited talk 3|
|23:00||16:00||15:00||10:00||07:00||Poster session 2|
|24:00||17:00||16:00||11:00||08:00||Invited talk 4|
|24:30||17:30||16:30||11:30||08:30||Invited talk 5|
|01:00||18:00||17:00||12:00||09:00||Poster session 3|
|02:00||19:00||18:00||13:00||10:00||Invited talk 6|
|02:30||19:30||18:30||13:30||10:30||Contributed talk 2|
|02:45||19:45||18:45||13:45||10:45||Contributed talk 3|
Papers must be in the latest NeurIPS format, but with a maximum of 6 pages (excluding references and supplementary material). Papers should be anonymized upon submission.
We understand that many submissions might be previously unsuccessful submissions to conferences such as NeurIPS or ICML. In this case, submissions may be 8 pages, but we ask that you include the conference reviews and meta-review and as part of supplementary materials. Please detail how your submission has been revised based on these reviews, or provide reasoning for why no changes have been made. Please note that, although papers which have been already reviewed and suitably revised will be considered stronger, they are still subject to review and might be rejected due to a lack of fit with the workshop.
Accepted papers and additional supplementary material will be made available on the workshop website. However, this does not constitute an archival publication, and no formal workshop proceedings will be made available, meaning contributors are free to publish their work in archival journals or conferences.
The three best papers submitted will be presented as 15-minute contributed talks.
Submissions can be made at https://cmt3.research.microsoft.com/METALEARN2020/Submission/Index by 2 October 2020, 06:00 PM PDT.
Can supplementary material be added beyond the 6-page limit, and are there any restrictions on it?
Yes, you may include additional supplementary material, but you should ensure that the main paper is self-contained, since looking at supplementary material is at the discretion of the reviewers. The supplementary material should also follow the same NeurIPS format as the paper and be limited to a reasonable amount (max 10 pages in addition to the main submission).
Can a submission to this workshop be submitted to another NeurIPS workshop in parallel?
We discourage this, as it leads to more work for reviewers across multiple workshops. Our suggestion is to pick one workshop to submit to.
Can a paper be submitted to the workshop that has already appeared at a previous conference with published proceedings?
We won’t be accepting such submissions unless they have been adapted to contain significantly new results (where novelty is one of the qualities reviewers will be asked to evaluate).
Can a paper be submitted to the workshop that is currently under review or will be under review at a conference during the review phase?
MetaLearn 2020 submissions are 6 pages, i.e., much shorter than standard conference submissions. But from our side, it is perfectly fine to submit a condensed version of a parallel conference submission if it also fine for the conference in question. Our workshop does not have archival proceedings, and therefore parallel submissions of extended versions to other conferences are acceptable.
This year we are trialing a new reviewer mentorship scheme, which we hope will improve the future pool of expert reviewers in machine learning. Junior reviewers will be able to provide reviews in a guided setting and will be linked with senior reviewers who will give them feedback and advice throughout the reviewing process. It is our hope that this will be a useful learning experience for reviewers and improve the overall quality of reviewing.
In order for this to be feasible, all submissions will be asked to provide two contacts who have agreed to review for the workshop. These volunteers can, of course, be authors of the submission, or people who have agreed to review on behalf of the authors. Depending on their experience reviewing, these contacts will be assigned to either a junior or senior reviewer role. All submissions will be ensured at least one senior reviewer, since we will still be directly recruiting for reviewers as in previous years.
If you would like to sign up, or recommend somebody, to be either a junior or senior reviewer, please fill out this form by 2 October 2020.
We anticipate that all reviewers will receive 2-3 papers to review, which will need to be finished by 23 October 2020.
Workshop on Meta-Learning (MetaLearn 2017) @ NeurIPS 2017
Workshop on Meta-Learning (MetaLearn 2018) @ NeurIPS 2018
Workshop on Meta-Learning (MetaLearn 2019) @ NeurIPS 2019
For any further questions, you can contact us at firstname.lastname@example.org.