Michael Galkin, Intel AI Lab Abstract: Foundation models in graph learning are hard to design due to the lack of common invariances that transfer across different structures and domains. In this talk, I will give an overview of the two main tracks of my research at Intel AI: creating foundation models for knowledge graph reasoning […]
The first TILOS Fireside Chat of Fall 2023 will be a conversation about theory in the age of modern AI led by TILOS members Nisheeth Vishnoi, Tara Javidi, Misha Belkin, and Arya Mazumdar (moderator). This will be a great opportunity to discuss implications of AI and roles of theory (especially with the recent development in […]
Masashi Sugiyama, University of Tokyo and RIKEN Abstract: In statistical inference and machine learning, we face a variety of uncertainties such as training data with insufficient information, label noise, and bias. In this talk, I will give an overview of our research on reliable machine learning, including weakly supervised classification (positive unlabeled classification, positive confidence classification, […]
