ICLR 2020Workshop Tackling Climate Change with Machine Learning

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.

About ICLR

The International Conference on Learning Representations (ICLR) 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 ICLR conference. Those interested should register for the Workshops component of ICLR at https://iclr.cc/Register/view-registration starting January 21. (A number of spots will be reserved for accepted submissions.)

About the workshop

• Date: Sunday, April 26, 2020
• Location: Addis Ababa, Ethiopia
• Mentorship program application deadline: January 14
• Paper/Proposal submission deadline: February 4, 11:59 PM Pacific Time
• Notification: February 25
• Submission website: https://cmt3.research.microsoft.com/CCAIICLR2020
• Contact: climatechangeai.iclr2020@gmail.com

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
• Transportation
• Buildings and cities
• Industry
• Carbon capture and sequestration
• Agriculture, forestry and other land use
• Extreme weather events
• Disaster management and relief
• Societal adaptation
• Ecosystems and natural resources
• Data presentation and management
• Climate finance and markets

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 ICLR style template (please do not uncomment the \iclrfinalcopy macro as it will deanonymize your submission).

Please see the Tips for Submissions and FAQ, and contact climatechangeai.iclr2020@gmail.com with questions.

Submission tracks

There are two tracks for submissions. Submissions are limited to 4 pages for the Papers track and 3 pages for the Proposals track (in PDF format), and must be anonymized. References do not count towards the page 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.

For examples of previous submissions, see our ICML 2019 and NeurIPS 2019 workshops. We strongly encourage authors to consider applying for our mentorship program, for which applications are due Jan 14 (more information here).

PAPERS track

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.

PROPOSALS track

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 workshops at ICML 2019 and NeurIPS 2019.
• 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.

Travel Grants

We are excited to announce limited travel grants for the workshop. Travel grant applications can be submitted at https://forms.gle/VZZSk7Fj6xcmQkR3A, and are due March 3.

We also encourage workshop participants to apply for other grants for which they may be eligible. If you are aware of additional funding opportunities that may be relevant to workshop attendees, please contact the workshop organizers so we can make this information available.