Date: 2022-10-21

Time: 15:30-16:30 (Montreal time)

https://mcgill.zoom.us/j/83436686293?pwd=b0RmWmlXRXE3OWR6NlNIcWF5d0dJQT09

Meeting ID: 834 3668 6293

Passcode: 12345

Abstract:

We consider the problem of testing the equality of the conditional distribution of a response variable given a set of covariates between two populations. Such a testing problem is related to transfer learning and causal inference. We develop a nonparametric procedure by combining recent advances in conformal prediction with some new ingredients such as a novel choice of conformity score and data-driven choices of weight and score functions. To our knowledge, this is the first successful attempt of using conformal prediction for testing statistical hypotheses beyond exchangeability. The final test statistic reveals a natural connection between conformal inference and the classical rank-sum test. Our method is suitable for modern machine learning scenarios where the data has high dimensionality and the sample size is large, and can be effectively combined with existing classification algorithms to find good weight and score functions. The performance of the proposed method is demonstrated in synthetic and real data examples.

Speaker

Dr. Jing Lei is a Professor in the Department of Statistics and Data Science at Carnegie Mellon University. His research focuses on conformal prediction, cross-validation, generalization error, networks, sparse PCA and optimal transport. He is also interested in applications such as the analysis of single cell RNA-sequencing and multi-omics data.

McGill Statistics Seminar schedule: https://mcgillstat.github.io/