[Statlist] Reminder: ETH Young Data Science Researcher Seminar Zurich - virtual seminar with Yuling Yan, Princeton University - Thursday, 8 December 2022

[Maurer Letizia]

We are glad to announce the following virtual talk in the ETH Young Data Science Researcher Seminar Zurich

"Inference and Uncertainty Quantification for Low-Rank Models“  
by Yuling Yan, Princeton University

Date and Time: Thursday, 8 December 2022, 16:00-17:00 (Zurich time)
Place:  Zoom at https://ethz.zoom.us/j/68998616059

Abstract: Many high-dimensional problems involve reconstruction of a low-rank matrix from highly incomplete and noisy observations. Despite substantial progress in designing efficient estimation algorithms, it remains largely unclear how to assess the uncertainty of the obtained low-rank estimates, and how to construct valid yet short confidence intervals for the unknown low-rank matrix.  

In this talk, I will discuss how to perform inference and uncertainty quantification for two widely encountered low-rank models: (1) noisy matrix completion, and (2) heteroskedastic PCA with missing data. For both problems, we identify statistically efficient estimators that admit non-asymptotic distributional characterizations, which in turn enable optimal construction of confidence intervals for, say, the unseen entries of the low-rank matrix of interest.  Our inferential procedures do not rely on sample splitting, thus avoiding unnecessary loss of data efficiency. All this is accomplished by a powerful leave-one-out analysis framework that originated from probability and random matrix theory. 

This is based on joint work with Yuxin Chen, Jianqing Fan and Cong Ma.


Seminar website: https://math.ethz.ch/sfs/news-and-events/young-data-science.html

Young Data Science Researcher Seminar Zurich – Seminar for Statistics | ETH Zurich
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