DMLR: Data-centric Machine Learning Research -- Past, Present and Future
Machine Learning
2024-06-04 v2 Artificial Intelligence
Distributed, Parallel, and Cluster Computing
Signal Processing
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
@article{arxiv.2311.13028,
title = {DMLR: Data-centric Machine Learning Research -- Past, Present and Future},
author = {Luis Oala and Manil Maskey and Lilith Bat-Leah and Alicia Parrish and Nezihe Merve Gürel and Tzu-Sheng Kuo and Yang Liu and Rotem Dror and Danilo Brajovic and Xiaozhe Yao and Max Bartolo and William A Gaviria Rojas and Ryan Hileman and Rainier Aliment and Michael W. Mahoney and Meg Risdal and Matthew Lease and Wojciech Samek and Debojyoti Dutta and Curtis G Northcutt and Cody Coleman and Braden Hancock and Bernard Koch and Girmaw Abebe Tadesse and Bojan Karlaš and Ahmed Alaa and Adji Bousso Dieng and Natasha Noy and Vijay Janapa Reddi and James Zou and Praveen Paritosh and Mihaela van der Schaar and Kurt Bollacker and Lora Aroyo and Ce Zhang and Joaquin Vanschoren and Isabelle Guyon and Peter Mattson},
journal= {arXiv preprint arXiv:2311.13028},
year = {2024}
}
Comments
Published in the Journal of Data-centric Machine Learning Research (DMLR) at https://data.mlr.press/assets/pdf/v01-5.pdf