Calendar

All Topics (February 1 through April 30)

Thursday, February 2

• 4:00 PM Shrinkage Estimation for Causal Inference and Experimental Design (Evan Rosenman -- Harvard)

Monday, February 6

• 4:00 PM Machine Learning Meets PDEs (Yulong Lu -- UMass Amherst)

Tuesday, February 7

• 4:00 PM Causal Reasoning in Complex Data Settings (AmirEmad Ghassami -- Johns Hopkins)

Thursday, February 9

• 4:00 PM Universality of regularized estimators in high dimensions (Yandi Shen -- U Chicago)

Thursday, February 23

• 4:00 PM Layered variable selection for multivariate Bayesian regression with density function-based features: a case study in imaging-genomics (Shariq Mohammed -- BU Biostats)

Thursday, March 2

• 4:00 PM Statistical and Computational Trade-offs in Variational Inference: A Case Study in Inferential Model Selection (Yixin Wang -- University of Michigan)

Thursday, March 23

• 4:00 PM Kernel Thinning and Stein Thinning (Lester Mackey -- Microsoft Research New England)

Thursday, March 30

• 4:00 PM A new perspective on quasi-stationary distributions (Paul Dupuis -- Brown University)

Thursday, April 6

• 4:00 PM Quantifying similarity and variability in artificial and biological neural networks (Alex Williams -- NYU, Flatiron)

Thursday, April 13

• 4:00 PM Self-Avoiding Models: Polymers and Surfaces (Carl Mueller -- University of Rochester)

Thursday, April 20

• 4:00 PM TBA (James Robins -- Harvard)

Thursday, April 27

• 4:00 PM Function-space regularized divergences and optimal transport for enhanced generative modeling (Markos Katsoulakis -- UMass Amherst)