Abstract

Recent years have seen rapid progress in meta-learning methods, which transfer knowledge across tasks and domains to efficiently learn new tasks, 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, to learning representations, and finally to learning algorithms that themselves acquire representations, classifiers, and policies for acting in environments. In practice, meta-learning has been shown to yield new state-of-the-art automated machine learning methods, novel deep learning architectures, and substantially improved one-shot learning systems. Moreover, improving one’s own learning capabilities through experience can also be viewed as a hallmark of intelligent beings, and neuroscience shows a strong connection between human and reward learning and the growing sub-field of meta-reinforcement learning.

Some of the fundamental questions that this workshop aims to address are:

As prospective participants, we primarily target machine learning researchers interested in the questions and foci outlined above. 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 and interdisciplinary perspective to the attendees.

Invited Speakers

TBA

Organizers

Program

Schedule

TBA

Important Dates

Formatting

We have provided a modified .sty file here that appropriately lists the name of the workshop when \neuripsfinal is enabled. Please use this style file in conjunction with the corresponding LaTeX .tex template from the NeurIPS website to submit a final camera-ready copy. Both the submission and the camera-ready can be up to 8 pages (excluding references) and we explicitly encourage submitting works shorter than 8 pages (e.g., works that may show preliminary but novel results).

Publication

Accepted papers and supplementary material will be made available on the workshop website. However, these do not constitute archival publications and no formal workshop proceedings will be made available, meaning contributors are free to publish their work in archival journals or conferences.

FAQ

  1. Can supplementary material be added beyond the 8-page limit for submissions, 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.

  2. Are references included in the 8-page limit?

    No, references will not count towards the page limit.

  3. 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.

  4. 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).

  5. 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?

    From our side, it is perfectly fine to submit a condensed version of a parallel conference submission if it is 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.

Review Process

tl;dr: The review process will be double-blind. Please sign up to be, or recommend, a reviewer via https://forms.gle/EW4icbYv5uA8A13KA.

Important Dates

Reviewing Guidelines

We encourage all reviewers, both junior and senior, to check our reviewing guidelines.

Reviewing Mentorship

Last year we trialed a new reviewer mentorship scheme aiming to improve the future pool of expert reviewers in machine learning. 61 Junior reviewers provided reviews guided by 39 senior reviewers, who gave them feedback and advice throughout the reviewing process. The program was a success with lots of discussion behind the scenes, and we’re excited to renew the program this year.

If you would like to sign up, or recommend somebody, to be either a junior or senior reviewer, please fill out this form by 22 September 2022.

Additionally, 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.

Program Committee

We thank the program committee (senior and junior reviewers) for shaping the excellent technical program; they are (in alphabetical order):

Aaron Klein, Abhishek Gupta, Alex De Sa, Alexander Tornede, Alexander Wang, Andrei Alex Rusu, Andrew Brock, Aroof Aimen, Artur Souza, Aviral Kumar, Badr AlKhamissi, Benjamin Eysenbach, Benjamin Letham, Bradly C. Stadie, Changbin Li, Chen Zhao, Chia Hsiang Kao, Clément Bonnet, Daniel Hernández-Lobato, Davide Buffelli, Eleni Triantafillou, Eric Mitchell, Fangqin Zhou, Giorgio Giannone, Hadi Samer Jomaa, Haozhu Wang, Homanga Bharadhwaj, Huaxiu Yao, Ievgen Redko, Ishita Dasgupta, Jake Snell, Jaya Krishna Mandivarapu, Jiajun Wu, Johannes Von Oswald, Jonas Hanselle, Jonas Rothfuss, Julien Niklas Siems, Karsten Roth, Kate Rakelly, Kelvin Xu, Lars Kotthoff, Louis Kirsch, Lucas Zimmer, Luisa M Zintgraf, Marius Lindauer, Marvin Zhang, Massimiliano Patacchiola, Mateusz Ochal, Maximilian Igl, Mengye Ren, Michael Chang, Michael Y. Li, Mihai Suteu, Mike Huisman, Mikhail Mekhedkin Meskhi, Mingyu Kim, Nasik Muhammad Nafi, Nayan Saxena, Nicholas I-Hsien Kuo, Nico Courts, Nikita Dhawan, Ondrej Bohdal, Paul Caron, Pedro Sandoval-Segura, Praneet Dutta, Quentin Bouniot, Ramnath Kumar, Renkun Ni, Sahil Manchanda, Sean M. Hendryx, Sharare Zehtabian, Sreejan Kumar, Sungryull Sohn, Tejaswini Pedapati, Thomas Elsken, Tim Postuvan, Valentin Guillet, Valerio Perrone, Yandong Li

Past Workshops

Workshop on Meta-Learning (MetaLearn 2017) @ NeurIPS 2017

Workshop on Meta-Learning (MetaLearn 2018) @ NeurIPS 2018

Workshop on Meta-Learning (MetaLearn 2019) @ NeurIPS 2019

Workshop on Meta-Learning (MetaLearn 2020) @ NeurIPS 2020

Workshop on Meta-Learning (MetaLearn 2021) @ NeurIPS 2021

Contacts

For any further questions, you can contact us at metalearn2022@googlegroups.com.