Abstract:
In studies ranging from clinical medicine to policy research, complete data are usually available from a population P, but the quantity of interest is often sought for a related but different population Q. In this talk, we consider the unsupervised domain adaptation setting under the label shift assumption. In the first part, we estimate a parameter of interest in population Q by leveraging information from P, where three ingredients are essential: (a) the common conditional distribution of X given Y, (b) the regression model of Y given X in P, and (c) the density ratio of the outcome Y between the two populations. We propose an estimation procedure that only needs some standard nonparametric technique to approximate the conditional expectations with respect to (a), while by no means needs an estimate or model for (b) or (c); i.e., doubly flexible to the model misspecifications of both (b) and (c). In the second part, we pay special attention to the case that the outcome Y is categorical. In this scenario, traditional label shift adaptation methods either suffer from large estimation errors or require cumbersome post-prediction calibrations. To address these issues, we propose a moment-matching framework for adapting the label shift, and an efficient label shift adaptation method where the adaptation weights can be estimated by solving linear systems. We rigorously study the theoretical properties of our proposed methods. Empirically, we illustrate our proposed methods in the MIMIC-III database as well as in some benchmark datasets including MNIST, CIFAR-10, and CIFAR-100.
About the Speaker:
Jiwei Zhao is currently an Associate Professor at the University of Wisconsin-Madison, jointly appointed by the Departments of Statistics & of Biostatistics and Medical Informatics. He earned his Bachelor’s degree in Mathematics from the Chern Honored Class at Nankai University, and his PhD in Statistics from UW-Madison in 2012. Before returning to Madison in 2020, he was an Assistant and then Associate Professor at the State University of New York at Buffalo. Dr. Zhao's research interests include semiparametric statistics, the tradeoff between efficiency and robustness, missing data analysis and causal inference, high-dimensional statistical inference, domain adaptation and transfer learning. His applied research focuses on patient-reported outcomes, clinical trials, real-world evidence and real-world data, health disparity, and health equity. His work has been published in top-tier statistical journals as well as in leading machine learning conferences. His research has been consistently supported by the US National Science Foundation and the National Institutes of Health.
