Scalable Inference of Dynamic Graphical Models with Combinatorial Structures

Explore a dynamic graphical models lecture focusing on scalable inference techniques for structures with combinatorial properties. Delve into advanced concepts presented by Salar Fattahi, Assistant Professor of Industrial and Operations Engineering at the University of Michigan. Gain insights into cutting-edge research and methodologies in this 30-minute talk, which offers valuable knowledge for those interested in graphical models, combinatorial optimization, and scalable inference algorithms.

Salar Fattahi: Scalable Inference of Dynamic Graphical Models with Combinatorial Structures