Fall 2024

Sep 13 – Lihua Lei (Stanford University)
Model-agnostic covariate-assisted inference on partially identified causal effects

Sep 20 – No Seminar

Sep 27 – Allan Sly (Princeton University)
Large cycles for the interchange process

Oct 4 – Giles Hooker (University of Pennsylvania, Wharton School)
Trees and V’s:  Inference for Ensemble Models

Oct 11 – Tudor Manole (MIT)
Central Limit Theorems for Smooth Optimal Transport Maps

Oct 18 – Anna Korba (ENSAE/CREST)
Sampling through optimization of divergences on the space of measures

Oct 25 – Tijana Zrnic (Stanford University)
A Flexible Defense Against the Winner’s Curse

Nov 1 – Christopher Harshaw (Columbia University)
The Conflict Graph Design: Estimating Causal Effects Under Interference

Nov 8 – Boris Hanin (Princeton University)
Scaling Limits of Neural Networks

Nov 15 – Rina Foygel Barber (University of Chicago)
Evaluating a black-box algorithm: stability, risk, and model comparisons

Nov 22 – Ofer Shayevitz, (Tel Aviv University)
Statistical Inference with Limited Memory

Nov 29 – No Seminar

Dec 6 – Jing Lei (Carnegie Mellon University)
Winners with Confidence: Discrete Argmin Inference with an Application to Model Selection