Connectionists: Call for Papers]: Second CFP for DMLR Workshop: Data-Centric Machine Learning Research at ICLR 2024

[Fatimah Alzamzami]

DMLR Workshop: Data-Centric Machine Learning Research at ICLR 2024
2024 - 2nd Call for Papers
May 11th – Vienna, Austria
DMLR Workshop ’24 Website<https://dmlr.ai/>

Theme: Harnessing Momentum for Science


We invite submissions to the 4th  workshop on Data-Centric Machine Learning Research co-located with the International Conference on Learning Representations (ICLR) – 2024 in Vienna, Austria.


Scope

AI for science uses AI to tackle unique scientific challenges, uncover rare phenomena, deepen our understanding of scientific domains, and accelerate discoveries. The traditional model-centric AI approach primarily focuses on algorithmic improvements and often overlooks the foundational role of data. This is particularly problematic in scientific contexts where there are strong emphases on both prediction from and explanation of data. In science, ML pipelines are often interwoven with the inputs and outputs of theoretical models which depend on robust data and reliable model outputs. In large science projects and missions that produce vast amounts of data, efficient data-centric AI frameworks are essential for maximizing the potential of expensive experiments and missions. This being said, the integrity of AI systems is intrinsically tied to the quality of their training data. The high stakes in science leave no room for errors due to poor data. For scientists to trust AI systems, data quality, including precise labeling and comprehensive coverage, is vital.


This workshop aims to showcase the latest research in the area of AI for science. By showcasing these breakthroughs and fostering collaboration, it seeks to break down the research barriers between AI and science.


Topics will include, but are not limited to:

  *   Data collection and benchmarking techniques

  *   Data governance frameworks for ML

  *   Impact of data bias, variance, and drifts

  *   Role of data in foundation models: pre-training, prompting, fine-tuning

  *   Optimal data for standard evaluation framework in the context of changing model landscape

  *   Domain specific data issues

  *   Data-centric explainable AI

  *   Data-centric approaches to AI alignment

  *   Active learning, Data cleaning, acquisition for ML

Bridging the gap between seasoned expertise and fresh perspectives, our keynote/invited talks will showcase the work of established, mid and early-career researchers. This well-rounded mix promises a vibrant exchange of ideas and fosters dynamic advancements in the field.


Submission


All authors and submissions should adhere to the ICLR policy<https://www.iclr.cc/Conferences/2024/AuthorGuide>.


  *   We welcome two types of paper submissions:

     *   Research papers: up to 8 pages (not including references and appendices). Acceptable material includes original and high-quality unpublished contributions to the theory, practical aspects, as well as position papers relevant to the workshop topics.

     *   Extended abstracts: up to 2 pages (not including references and appendices). Acceptable material includes work which has already been submitted or published, preliminary results and controversial findings.

  *   Posting all versions of a paper (with the exception of the final published version) that is submitted to DMLR workshop, on preprint servers like ArXiv is permitted. Once the paper is accepted, the preprint version should be marked with the publication information including DOI.

  *   All submissions must represent original work and not previously published elsewhere.

  *   The use of LLMs is allowed as a general-purpose writing assist tool. Authors should understand that they take full responsibility for the contents of their papers, including content generated by LLMs that could be construed as plagiarism or scientific misconduct (e.g., fabrication of facts). LLMs are not eligible for authorship.

  *   ​​Authors who choose to create new datasets must provide access to the datasets (view and download) to help reviewers assess submitted works. We strongly encourage authors to submit supplementary material (as a separate PDF) including:

     *   Data Card: we recommend authors to check <https://sites.research.google/datacardsplaybook/> data card template<https://sites.research.google/datacardsplaybook/>.

     *   Data Sheet: Check a <https://arxiv.org/abs/1803.09010> datasheet example<https://arxiv.org/abs/1803.09010>.

  *   Authors are strongly encouraged to include a paragraph-long Reproducibility Statement<https://www.iclr.cc/Conferences/2024/AuthorGuide> at the end of the main text (before references) to discuss the efforts that have been made to ensure reproducibility. This optional reproducibility statement will not count toward the page limit, but should not be more than 1 page. We encourage authors to check <https://arxiv.org/abs/1810.03993> model card template<https://arxiv.org/abs/1810.03993>.

  *   Submissions should adhere to the DMLR style templates: Latex template<https://github.com/JmlrOrg/dmlr-style-file>

  *   Submissions are only accepted in written English.

  *   All papers must be proofread (not just spell checked) by the authors before submission.

  *   Submission URL: https://openreview.net/group?id=ICLR.cc/2024/Workshop/DMLR

Accepted research papers will be presented at the workshop either as a talk or as a poster. Accepted extended abstracts will be presented as posters. We do not intend to publish paper proceedings.




Important Dates


(Time zone: Anywhere on Earth)

Paper Submission deadline: 03 February 2024

Notification of Acceptance: 03 March 2024

Camera Ready Copy due: Coming Soon

Workshop: 11 May 2024



Awards

(1)  A few selected exceptional research papers from DMLR workshop 2024 will be invited to contribute to the DMLR journal; the latest member of the JMLR family, aiming to provide a top archival venue for high-quality scholarly articles focused on the data aspect of machine learning research.

(2) Papers with methods, datasets or applications that are relevant to Sustainable Development Goals (https://sdgs.un.org/goals) will have the opportunity to present their paper in a spotlight talk on the AI for Good platform (https://aiforgood.itu.int/) and the in the in-person summit in Geneva, Switzerland.

Contact

If you have any questions about the paper submission and the workshop, please join our Discord channel: https://discord.gg/jYk3FNfYqG



Join the DMLR Discord Server!<https://discord.gg/jYk3FNfYqG>
Check out the DMLR community on Discord - hang out with 320 other members and enjoy free voice and text chat.
discord.gg


Workshop Organizers

Manil Maskey, Alicia Parrish, Lilith Bat-Leah, Praveen Paritosh, Chanjun Park, Fatimah Alzamzami, Xiaozhe Yao, Holger Caesar, Bernard Koch, Zhangyang “Atlas” Wang, Jerone Andrews, Paolo Climaco, Bolei Ma, Steffen Vogler, Danilo Brajovic, Mayee Chen, Sang Truong


Best Regards,
The Organizing Committee of DMLR Workshop 2024
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