Date: 2023-02-10

Time: 15:00-16:00 (Montreal time)

Hybrid: In person / Zoom

Location: Burnside Hall 1205

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

Meeting ID: 834 3668 6293

Passcode: 12345

Abstract:

We study the domain adaptation problem with label shift in this work. Under the label shift context, the marginal distribution of the label varies across the training and testing datasets, while the conditional distribution of features given the label is the same. Traditional label shift adaptation methods either suffer from large estimation errors or require cumbersome post-prediction calibrations. To address these issues, we first propose a moment-matching framework for adapting the label shift based on the geometry of the influence function. Under such a framework, we propose a novel method named efficient label shift adaptation (ELSA), in which the adaptation weights can be estimated by solving linear systems. Theoretically, the ELSA estimator is root-n consistent (n is the sample size of the source data) and asymptotically normal. Empirically, we show that ELSA can achieve state-of-the-art estimation performances without post-prediction calibrations, thus, gaining computational efficiency.

Speaker

Dr. Qinglong Tian is an Assistant Professor in the Department of Statistics and Actuarial Science at the University of Waterloo. He obtain his PhD degree at Iowa State University with Professor William Meeker. His research focuses on engineering statistics, reliability and statistical computing.