Title: Influence-Guided Subsampling under Measurement Constraints

Presented by Lin Wang, Assistant Professor, Department of Statistics, Purdue University

DATE:  Wednesday, October 15, 2025, 3:30 PM, AUST 434

Webex link: Link 

Coffee will be available at 3:00 in the Noether Lounge (AUST 326)

Abstract: Collecting labels for every point in a large dataset is often impractical due to measurement and budget constraints. A common remedy is to label only a carefully chosen subset of design points. In this talk, I will present a suite of computationally efficient subsampling methods that select small, informative subsets from large candidate pools. Unlike most existing approaches, which are tailored to low-dimensional settings, these methods explicitly accommodate high-dimensional regimes where the number of relevant predictors can be comparable to, or even exceed, the full sample size. I will describe theoretical guarantees and demonstrate performance through extensive numerical experiments. Across a wide range of scenarios, the methods consistently outperform existing subsampling techniques, resulting in substantial savings in labeling costs.

Bio: Lin Wang is an Assistant Professor of Statistics at Purdue University. Before joining Purdue, she was an Assistant Professor at George Washington University. She received her Ph.D. in Statistics from the University of California, Los Angeles, in 2019. Her research focuses on sampling and subsampling, experimental design, and causal inference, with applications to electronic health record data, large-scale genetic data, and computer experiments.