Events: Statistics Colloquium

Statistics Colloquium: Jing Lei

11:30 am–12:30 pm Jones 303

Jing Lei
Department of Statistics & Data Science
Carnegie Mellon University

Title: Evaluating Black-Box Classifiers via Stable Adaptive Two-Sample Inference

Abstract: Black-box machine learning methods predict well but are hard to validate. This talk develops adaptive, sample-efficient inference that uses flexible machine learning tools to test black-box predictions, with conditional distribution testing as the unifying problem. First, a weighted conformal rank-sum statistic turns aggregated conformal p-values into a test whose target, the Neyman-Pearson-Gini divergence, connects rank-sum statistics, ROC curves, and total variation. Second, two-sample U-statistic orthogonalization and cross-fitting remove the bias from estimating density-ratio weights, so the data can be reused without sample splitting. Third, for validating a fitted classifier, a cross-fitted distinguisher with a “safe-side bias” yields valid inference even though the statistic targets a data-dependent lower bound of the divergence. Applications show that classifiers with similar accuracy can differ substantially in goodness of fit.

Oct 26

Statistics Colloquium: Michael Sobel

11:30 am–12:30 pm Jones 303

Michael Sobel
Department of Statistics
Columbia University

Title: TBA

Abstract: TBA

Nov 2

Statistics Colloquium: Bin Yu

11:30 am–12:30 pm Jones 303

Bin Yu
Department of Statistics, Electrical Engineering and Computer Sciences, and Center for Computational Biology
University of California, Berkeley

Title: TBA

Abstract: TBA

Nov 9