| Date |
Presenter |
Title |
Wednesday, January 20
Time: 12:10-1:10 pm |
Lihua Lei – Stanford University |
Distribution-Free Assessment of Population Overlap in Observational Studies |
Wednesday, January 27
Time: 12:10-1:10 pm |
Yuxin Chen – Princeton University |
Taming Nonconvexity in Statistical and Reinforcement Learning |
Wednesday, February 3
Time: 12:10-1:10 pm |
Liza Levina – University of Michigan |
Hierarchical Community Detection by Recursive Partitioning |
Wednesday, February 10
Time: 12:10-1:10 pm |
Yuting Wei – Carnegie Mellon University |
Breaking the Sample Size Barrier in Reinforcement Learning |
Friday, February 12
Time: 12:10-1:10 pm |
Elizabeth Ogburn – Johns Hopkins University |
Disentangling Confounding and Nonsense Associations Due to Dependence |
Wednesday, February 17
Time: 12:10-1:10 pm |
Gesine Reinert – University of Oxford |
Stein’s Method for Exponential Random Graph Models and Kernelized Goodness of Fit |
Wednesday, February 24
Time: 12:10-1:10 pm |
Jacob Steinhardt – University of California, Berkeley |
The Science of Measurement in Machine Learning |
Friday, February 26
Time: 12:10-1:10 pm |
Veronika Rockova – University of Chicago |
Metropolis-Hastings via Classification |
Wednesday, March 3
Time: 12:10-1:10 pm |
Will Fithian – University of California, Berkeley |
Conditional Calibration for False Discovery Rate Control under Dependence |
Wednesday, March 17
Time: 12:10-1:10 pm |
Tian Zheng – Columbia University |
Artificial Perceptual Learning: Image Categorization with Weak Supervision |
Wednesday, March 24
Time: 12:10-1:10 pm |
Hamsa Bastani – University of Pennsylvania |
Deploying an Artificial Intelligence System for COVID-19 Testing at the Greek Border |
Wednesday, March 31
Time: 12:10-1:10 pm |
Stephen Bates – University of California, Berkeley |
Distribution-Free, Risk-Controlling Prediction Sets |
Wednesday, April 7
Time: 12:10-1:10 pm |
Mikhail Belkin – University of California, San Diego |
Two Mathematical Lessons of Deep Learning |
Wednesday, April 14
Time: 12:10-1:10 pm |
Larry Wasserman – Carnegie Mellon University |
Causal Inference in the Time of Covid-19 |
Joint Seminar with the Wharton
Applied Economics Workshop
Wednesday, April 21
Time: 12:10-1:10 pm |
Isaiah Andrews – Harvard University |
Inference on Winners |
Joint Seminar with the
Penn CIS Department
Wednesday, April 28
Time: 12:10-1:10 pm |
Emmanuel Candès – Stanford University |
Reliable Predictions? Counterfactual Predictions? Equitable Treatment? Some Recent Progress In Predictive Inference |
Wednesday, May 5
Time: 12:10-1:10 pm |
Murat Erdogdu – University of Toronto |
On the Convergence of Langevin Monte Carlo: The Interplay between Tail Growth and Smoothness |
