Many in the ML community wish to take action on climate change, yet feel their skills are inapplicable. This workshop aims to show that in fact the opposite is true: while no silver bullet, ML can be an invaluable tool both in reducing greenhouse gas emissions and in helping society adapt to the effects of climate change. Climate change is a complex problem, for which action takes many forms - from designing smart electrical grids to tracking deforestation in satellite imagery. Many of these actions represent high-impact opportunities for real-world change, as well as being interesting problems for ML research.
NeurIPS (formerly written “NIPS”) is one of the premier conferences on machine learning, and includes a wide audience of researchers and practitioners in academia, industry, and related fields. It is possible to attend the workshop without either presenting at or attending the main NeurIPS conference. Those interested should register for the Workshops component of NeurIPS at https://neurips.cc/Register/view-registration. Note that in past years registration has sold out quickly; we suggest monitoring the website to ensure you get a ticket if you would like to attend. A number of spots will be reserved for accepted submissions.
About the workshop
- Date: Friday, December 13 or Saturday, December 14, 2019
- Location: Vancouver, British Columbia, Canada
- Submission deadline: September 11, 11:59 PM Pacific Time
- Notification: October 1
- Submission website: https://cmt3.research.microsoft.com/CCAINeurIPS2019
- Contact: firstname.lastname@example.org
Call For Submissions
We invite submissions of short papers using machine learning to address problems in climate mitigation, adaptation, or modeling, including but not limited to the following topics:
- Power generation and grids
- Smart buildings and cities
- Industrial optimization
- Carbon capture and sequestration
- Agriculture, forestry and other land use
- Climate science
- Extreme weather events
- Disaster management and relief
- Societal adaptation
- Ecosystems and natural resources
- Data presentation and management
- Climate finance
All machine learning techniques are welcome, from kernel methods to deep learning. Each submission should make clear why the application has (or could have) positive impacts regarding climate change. We highly encourage submissions which make their data publicly available. Accepted submissions will be invited to give poster presentations, of which some will be selected for spotlight talks.
The workshop does not record proceedings, and submissions are non-archival. Submission to this workshop does not preclude future publication. Previously published work may be submitted under certain circumstances (see the FAQ).
All submissions must be through the submission website. Submissions will be reviewed double-blind; do your best to anonymize your submission, and do not include identifying information for authors in the PDF. We encourage, but do not require, use of the NeurIPS style template (please do not use the “Accepted” format as it will deanonymize your submission).
Please see the Tips for Submissions and FAQ, and contact email@example.com with questions.
There are two tracks for submissions. Submissions are limited to 3 pages for the Papers track, and 2 pages for the Proposals track, in PDF format (see examples here). References do not count towards this total. Supplementary appendices are allowed but will be read at the discretion of the reviewers. All submissions must explain why the proposed work has (or could have) positive impacts regarding climate change.
Work that is in progress, published, and/or deployed
Submissions for the Papers track should describe projects relevant to climate change that involve machine learning. These may include (but are not limited to) academic research; deployed results from startups, industry, public institutions, etc.; and climate-relevant datasets.
Submissions should provide experimental or theoretical validation of the method presented, as well as specifying what gap the method fills. Algorithms need not be novel from a machine learning perspective if they are applied in a novel setting. Details of methodology need not be revealed if they are proprietary, though transparency is highly encouraged.
Submissions creating novel datasets are welcomed. Datasets should be designed to permit machine learning research (e.g. formatted with clear benchmarks for evaluation). In this case, baseline experimental results on the dataset are preferred, but not required.
Detailed descriptions of ideas for future work
Submissions for the Proposals track should describe detailed ideas for how machine learning can be used to solve climate-relevant problems. While less constrained than the Papers track, Proposals will be subject to a very high standard of review. No results need to be demonstrated, but ideas should be justified as extensively as possible, including motivation for why the problem being solved is important in tackling climate change, discussion of why current methods are inadequate, and explanation of the proposed method.
Tips for submissions
- For examples of typical formatting and content, see submissions from our previous workshop.
- Be explicit: Describe how your proposed approach addresses climate change, demonstrating an understanding of the application area.
- Frame your work: The specific problem and/or data proposed should be contextualized in terms of prior work.
- Address the impact: Describe the practical ramifications of your method in addressing the problem you identify, as well as any relevant societal impacts or potential side-effects.
- Explain the ML: Readers may not be familiar with the exact techniques you are using or may desire further detail.
- Justify the ML: Describe why the ML method involved is needed, and why it is a good match for the problem.
- Avoid jargon: Jargon is sometimes unavoidable but should be minimized. Ideal submissions will be accessible both to an ML audience and to experts in other relevant fields, without the need for field-specific knowledge. Feel free to direct readers to accessible overviews or review articles for background, where it is impossible to include context directly.
David Rolnick (UPenn)
Alexandre Lacoste (Element AI)
Tegan Maharaj (Mila)
Priya Donti (CMU)
Lynn Kaack (ETH Zürich)
John Platt (Google AI)
Jennifer Chayes (Microsoft Research)
Yoshua Bengio (Mila)
Frequently Asked Questions
Q: How can I keep up to date on this kind of stuff?
A: Sign up for our mailing list! https://www.climatechange.ai/Mailing_list.html
Q: What is the date of the workshop / when will we know?
A: The workshop will be either Friday, December 13 or Saturday, December 14. We will update this page when the date is confirmed; according to the NeurIPS organizers this should be in the next few weeks.
Q: I’m not in machine learning. Can I still submit?
A: Yes, absolutely! We welcome submissions from many fields. Do bear in mind, however, that the majority of attendees of the workshop will have a machine learning background; therefore, other fields should be introduced sufficiently to provide context for the work.
Q: What if my submission is accepted but I can’t attend the workshop?
A: You may ask someone else to present your work in your stead.
Q: Do I need to use LaTeX or the NeurIPS style files?
A: No, although we encourage it.
Q: What do I do if I need an earlier decision for visa reasons?
A: Contact us at firstname.lastname@example.org and explain your situation and the date by which you require a decision and we will do our best to be accommodating.
Q: Can I send submissions directly by email?
A: No, please use the CMT website to make submissions.
Q: The submission website is asking for my name. Is this a problem for anonymization?
A: You should fill out your name and other info when asked on the submission website; CMT will keep your submission anonymous to reviewers.
Q: Do submissions for the Proposals track need to have experimental validation?
A: No, although some initial experiments or citation of published results would strengthen your submission.
Q: The submission website never sent me a confirmation email. Is this a problem?
A: No, the CMT system does not send automatic confirmation emails after a submission, though the submission should show up on the CMT page once submitted. If in any doubt regarding the submission process, please contact the organizers. Also please avoid making multiple submissions of the same article to CMT.
Q: Can I submit previously published work to this workshop?
A: If it was previously published in a non-ML venue, YES! If it was previously published in an ML venue, NO! If you are unsure, contact email@example.com. This policy is as per the NeurIPS workshop guidelines: “Workshops are not a venue for work that has been previously published in other conferences on machine learning or related fields. Work that is presented at the main NeurIPS conference should not appear in a workshop, including as part of an invited talk. Organizers should make this clear in their calls and explain in their proposal how they will discourage presentation of already finalized machine learning work. (Presenting work that has been published in other fields is, however, encouraged!)”