The Data-Centric Machine Learning Research (DMLR) Workshop

Description:
The Data-Centric Machine Learning Research (DMLR) Workshop will be held in conjunction with the International Conference on Machine Learning (ICML) in Vienna, Austria, on July 26-27, 2024. The workshop aims to explore the critical role of datasets in shaping the future of foundation models and advance research in this area. The DMLR Workshop builds on the success of prior data-centric workshops and brings together the DMLR, DataComp, and AI for Good communities. The workshop invites paper submissions on topics including data sources for large-scale datasets, construction of datasets from unlabeled data, model-assisted dataset construction, quality signals for large-scale datasets, datasets for evaluation, datasets for specific applications, impact of dataset drifts in large-scale models, ethical considerations and governance of large-scale datasets, data curation, and HCI. The workshop welcomes two types of paper submissions: research papers and extended abstracts. The submission deadline is May 24, 2024, and the notification of acceptance will be on June 17, 2024. The workshop will feature selected exceptional research papers that will be invited to contribute to the DMLR journal, a top archival venue for high-quality scholarly articles focused on the data aspect of machine learning research. The workshop organizers include Adam Mahdi, Ludwig Schmidt, Alex Dimakis, Rotem Dror, Georgia Gkioxari, Sang T. Truong, Lilith Bat-Leah, Fatimah Alzamzami, Georgios Smyrnis, Thao Nguyen, Nezihe Merve Gürel, Paolo Climaco, Luis Oala, Hailey Schoelkopf, Andrew Michael Bean, Berivan Isik, Vaishaal Shankar, Mayee F Chen, and Achal Dave. If you have any questions about paper submission and the workshop, please join the Discord channel, Linkedin group, or Twitter page.