DMLR: Data-Centric Machine Learning Research - Past, Present and Future
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.
Cite
Text
Oala et al. "DMLR: Data-Centric Machine Learning Research - Past, Present and Future." Data-centric Machine Learning Research, 2024.Markdown
[Oala et al. "DMLR: Data-Centric Machine Learning Research - Past, Present and Future." Data-centric Machine Learning Research, 2024.](https://mlanthology.org/dmlr/2024/oala2024dmlr-dmlr/)BibTeX
@article{oala2024dmlr-dmlr,
title = {{DMLR: Data-Centric Machine Learning Research - Past, Present and Future}},
author = {Oala, Luis and Maskey, Manil and Bat-Leah, Lilith and Parrish, Alicia and Gürel, Nezihe Merve and Kuo, Tzu-Sheng and Liu, Yang and Dror, Rotem and Brajovic, Danilo and Yao, Xiaozhe and Bartolo, Max and Rojas, William A Gaviria and Hileman, Ryan and Aliment, Rainier and Mahoney, Michael W. and Risdal, Meg and Lease, Matthew and Samek, Wojciech and Dutta, Debojyoti and Northcutt, Curtis G and Coleman, Cody and Hancock, Braden and Koch, Bernard and Tadesse, Girmaw Abebe and Karlaš, Bojan and Alaa, Ahmed and Dieng, Adji Bousso and Noy, Natasha and Reddi, Vijay Janapa and Zou, James and Paritosh, Praveen and van der Schaar, Mihaela and Bollacker, Kurt and Aroyo, Lora and Zhang, Ce and Vanschoren, Joaquin and Guyon, Isabelle and Mattson, Peter},
journal = {Data-centric Machine Learning Research},
year = {2024},
pages = {1-27},
volume = {1},
url = {https://mlanthology.org/dmlr/2024/oala2024dmlr-dmlr/}
}