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

• DOI: 10.48550/arxiv.2311.13028
• Publication Date: 2023-01-01
• 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. Gaviri...; 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 M. Alaa; Adji Bousso Dieng; Natasha Noy; Vijay Janapa Reddi; James Zou; Praveen Paritosh; Mihaela van der S...; Kurt Bollacker; Lora Aroyo; Ce Zhang; Joaquin Vanschoren; Isabelle Guyon; Peter Mattson
• Keywords: Relevance

Drawing from discussions at the inaugural DMLR workshop ICML 2023 and meetings prior, in this report we outline relevance of community engagement infrastructure development for creation next-generation public datasets that will advance machine learning science. We chart a path forward as collective effort to sustain maintenance these methods towards positive scientific, societal business impact.