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