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

作者：Luis Oala, M. Maskey, Lilith Bat-Leah, Alicia Parrish, Nezihe Merve Gurel, Tzu-Sheng Kuo, Yang Liu, Rotem Dror, Danilo Brajovic, Xiaozhe Yao, Max Bartolo, W. G. Rojas, Ryan Hileman, Rainier Aliment, Michael W. Mahoney, Meg Risdal, Matthew Lease, Wojciech Samek, Debojyoti Dutta, Curtis G. Northcutt, C. Coleman, Braden Hancock, Bernard J. Koch, G. Tadesse, Bojan Karlavs, Ahmed Alaa, A. B. Dieng, Natasha Noy, V. Reddi, James Zou, Praveen K. Paritosh, M. Schaar, K. Bollacker, L. Aroyo, Ce Zhang, J. Vanschoren, Isabelle Guyon, Peter Mattson · 发表于：J. Data-centric Mach. Learn. Res. · 年份：2023 · DOI：10.48550/arXiv.2311.13028 · 被引用次数：18 · 研究领域：Computer Science、Engineering

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.