Invited Talk #2 - A data-centric view on reliable generalization: From ImageNet to LAION-5B & DataComp [Speaker: Ludwig Schmidt (Anthropic, Stanford, and U Washington)]
in
Workshop: Navigating and Addressing Data Problems for Foundation Models (DPFM)
Abstract
Title: A data-centric view on reliable generalization: From ImageNet to LAION-5B & DataComp
Bio: Ludwig Schmidt is a member of the technical staff at Anthropic and an assistant professor at the University of Washington (on leave) and Stanford University (incoming). Ludwig’s research interests revolve around the empirical foundations of machine learning, often with a focus on datasets, reliable generalization, and large models. Recently, Ludwig’s research group contributed to open source machine learning by creating OpenCLIP, OpenFlamingo, and the LAION-5B dataset. Ludwig completed his PhD at MIT and was a postdoc at UC Berkeley. Ludwig’s research received a new horizons award at EAAMO, best paper awards at ICML & NeurIPS, a best paper finalist at CVPR, and the Sprowls dissertation award from MIT.