Machine Learning and Friends Lunch

Agustinus Kristiadi is a postdoctoral fellow at the Vector Institute and previously obtained his PhD from the University of Tuebingen in Germany.

In this talk, Shi will present two recent projects that use statistical methods to deepen our understanding of LLMs.

Silvia Sellán (Columbia University) will advocate for a perspective shift that allows us to design algorithms directly for safety-critical applications.

Training deep neural networks requires tens of thousands to millions of examples, so curating multimodal vision datasets amounts to numerous man-hours.

Join Andrew Wu for a presentation on version 2.0 of Marabou, a toolkit for formally verifying user-defined properties on deep neural networks.

Causal inference traditionally involves analyzing tabular data where variables like treatment, outcome, covariates, and colliders are manually labeled by humans

The Machine Learning and Friends Lunch series continues with Xiaolong Wang's "Learning Humanoid Robots."

This talk explores the challenges of out-of domain (OOD) generalization in computer vision, encompassing tasks like domain adaptation.

Machine Learning and Friends Lunch

In this talk, Xinya Du will discuss his research on synergizing LMs and knowledge.