Fall 2021

Algorithmic Advances for Statistical Inference with Combinatorial Structure

Oct 11, 2021 to Oct 15, 2021 

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Laurent Massoulie (Microsoft Research-INRIA Joint Centre; chair), Po-Ling Loh (University of Wisconsin, Madison), Jiaming Xu (Duke University)

The theme of this workshop is the interplay between problem structure and computational complexity, combining the strength of the statistical and algorithmic mindsets. The focus will be on understanding how algorithms can exploit problem structure and on understanding which tools in our algorithmic tool kit are suited for different structured inference tasks. 

The workshop will feature surprising and deep new algorithmic insights for prominent specific problems, such as graph matching, learning Gaussian graphical models, optimization in spin glasses, and more. At the same time, the workshop will highlight the broader emerging understanding of the power of classes of algorithms (such as gradient descent, message passing, generalized belief propagation, and convex programs) for families of structured problems. 

This event will be held in person and virtually.

Inquiries may be sent to the organizers workshop-si2 [at] (at this address).

Registration is required to attend this workshop. Space may be limited, and you are advised to register early. To submit your name for consideration, please register and await confirmation of your acceptance before booking your travel.