[2311.13028] DMLR: Data-centric Machine Learning Research -- Past, Present and Future
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Computer Science > Machine Learning

arXiv:2311.13028 (cs)
[Submitted on 21 Nov 2023 (v1), last revised 1 Jun 2024 (this version, v2)]

Title:DMLR: Data-centric Machine Learning Research -- Past, Present and Future

Authors:Luis Oala, Manil Maskey, Lilith Bat-Leah, Alicia Parrish, Nezihe Merve Gürel, Tzu-Sheng Kuo, Yang Liu, Rotem Dror, Danilo Brajovic, Xiaozhe Yao, Max Bartolo, William A Gaviria Rojas, Ryan Hileman, Rainier Aliment, Michael W. Mahoney, Meg Risdal, Matthew Lease, Wojciech Samek, Debojyoti Dutta, Curtis G Northcutt, Cody Coleman, Braden Hancock, Bernard Koch, Girmaw Abebe Tadesse, Bojan Karlaš, Ahmed Alaa, Adji Bousso Dieng, Natasha Noy, Vijay Janapa Reddi, James Zou, Praveen Paritosh, Mihaela van der Schaar, Kurt Bollacker, Lora Aroyo, Ce Zhang, Joaquin Vanschoren, Isabelle Guyon, Peter Mattson
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Abstract:Drawing from discussions at the inaugural DMLR workshop at ICML 2023 and meetings prior, in this report we outline the relevance of community engagement and infrastructure development for the creation of next-generation public datasets that will advance machine learning science. We chart a path forward as a collective effort to sustain the creation and maintenance of these datasets and methods towards positive scientific, societal and business impact.
Comments: Published in the Journal of Data-centric Machine Learning Research (DMLR) at this https URL
Subjects: Machine Learning (cs.LG); Artificial Intelligence (cs.AI); Distributed, Parallel, and Cluster Computing (cs.DC); Signal Processing (eess.SP)
Cite as: arXiv:2311.13028 [cs.LG]
  (or arXiv:2311.13028v2 [cs.LG] for this version)
  https://doi.org/10.48550/arXiv.2311.13028

Submission history

From: Luis Oala [view email]
[v1] Tue, 21 Nov 2023 22:29:25 UTC (3,674 KB)
[v2] Sat, 1 Jun 2024 13:28:30 UTC (3,692 KB)
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