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DMLR 2023 : ICML Workshop on Data-Centric Machine Learning Research

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Link: http://dmlr.ai/cfp
 
When Jul 29, 2023 - Jul 29, 2023
Where Honolulu, Hawaii
Submission Deadline May 26, 2023
Notification Due Jun 19, 2023
Final Version Due Jul 3, 2023
Categories    computer science   machine learning   artificial intelligence
 

Call For Papers

Submission site: https://cmt3.research.microsoft.com/DMLR2023/.

Important Dates
(Time zone: Anywhere on Earth)
Paper Submission deadline: 26 May, 2023
Notification of Acceptance: 19 June, 2023
Camera Ready Copy due: (Tentative) 3 July, 2023
Workshop: 29 July, 2023
Topics

We are focused on data, and creating a top-tier venue for research in data. Our ultimate goal is to engage the vibrant interdisciplinary community of researchers, practitioners, and engineers that tackle practical data problems related to the following list (not exclusive) of topics:

- Benchmarking data collection, data generation, data labeling, data augmentation processes, generalizability of datasets, feature representations, text, and image generation models
- Datasets for machine learning research, AI, and AGI.
- Data governance, debt, and its solutions.
- Data bias, variance, uncertainty, and its influence to ML.
- Active learning, Data cleaning, and acquisition for ML.
- Data-centric quality evaluation for data benchmarking.
- Influence of data benchmarks on ML research.
- Data-centric approaches to AI alignment.

Submission
We welcome three 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 Acceptable material includes original and high-quality unpublished contributions to the theory, practical aspects, as well as position papers relevant to the workshop topics.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.
- Dataperf submission papers: up to 4 pages describing the details of your submission to DataPerf v0.5. DataPerf is a suite of data-centric challenges that evaluate the quality of training and test data, and the algorithms for constructing or optimizing such datasets, such as core set selection or labeling error debugging, across a range of common ML tasks such as image classification. We plan to leverage the DataPerf benchmarks through challenges and leaderboards. Our workshop collaborates with the DataPerf challenge and we welcome submissions to the challenge as well. For more details: https://dataperf.org/.

Submissions should adhere to the ICML 2023 guidelines and style templates https://icml.cc/Conferences/2023/StyleAuthorInstructions.

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, however, a few exceptional selected research papers will be invited to the DMLR journal (see below for details).
DMLR Journal

The Journal of Data-centric Machine Learning Research (DMLR) is 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. The top submissions to the DMLR workshops will be invited to submit extended version of their paper to the DMLR journal.
Contact

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

Newsha Ardalani · Max Bartolo · Rotem Dror · Nezihe Merve Gürel · Najoung Kim · Daniel Kondermann · Tzu-Sheng Kuo · Lilith Bat-Leah · Yang Liu · Manil Maskey · Luis Oala · Praveen Paritosh · Alicia Parrish · William Gaviria Rojas · Ludwig Schmidt · Ce Zhang

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