We invite submissions related to the role of data practices in machine learning, including but not limited to the following topics of interest:
- Data repository design and challenges, particularly those specific to ML
- Dataset publication and citation
- FAIR and AI-ready datasets
- Licensing for ML datasets
- ML dataset search and discovery
- Comprehensive data documentation
- Data documentation methods for foundation models
- Data curation and quality assurance
- Best practices for revising and deprecating datasets
- Dataset usability
- Dataset reproducibility
- FAIR ML models
- Benchmark reproducibility
- Holistic and contextualized benchmarking
- Benchmarking and leaderboard ranking techniques
- Overfitting and overuse of benchmark datasets
- Non-traditional/alternative benchmarking paradigms
We are accepting both full-length submissions (i.e., up to 10 pages) and Tiny Papers submissions (3-5 pages) in ICLR format. This year, ICLR is discontinuing the separate “Tiny Papers” track, and is instead requiring each workshop to accept short (3–5 pages in ICLR format, exact page length to be determined by each workshop) paper submissions, with an eye towards inclusion; see https://iclr.cc/Conferences/2025/CallForTinyPapers for more details. Authors of these papers will be earmarked for potential funding from ICLR, but need to submit a separate application for Financial Assistance that evaluates their eligibility. This application for Financial Assistance to attend ICLR 2025 will become available on https://iclr.cc/Conferences/2025/ at the beginning of February and close on March 2nd.
This is a nonarchival venue with no proceedings. Workshop submissions can be subsequently or concurrently submitted to other venues. Accepted submissions will be made public on OpenReview; submissions that are not accepted will remain private.
Submissions can be made on our OpenReview homepage. Submissions are due February 3, 2025 AoE and decisions will be released by March 5, 2025 AoE.