Webcast Option: https://yale.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=15c1fa53-9633-44d6-b74b-b3e10105872c
Title: Computationally efficient reductions between some statistical models
Abstract: Can a sample from one parametric statistical model (the source) be transformed into a sample from a different (target) model? Versions of this question were asked as far back as 1950, and a beautiful asymptotic theory of equivalence between experiments emerged in the latter half of the 20th century. Motivated by problems spanning information-computation gaps and differentially private data analysis, we address the analogous non-asymptotic question in high-dimensional problems and with algorithmic considerations. We show how a single observation from some source models can be approximately transformed to a single observation from a large class of target models by a computationally efficient algorithm. I will present several such reductions and discuss their applications to the aforementioned problems.
This is joint work with Mengqi Lou and Guy Bresler and based on the following papers:
https://arxiv.org/abs/2402.07717
https://arxiv.org/abs/2510.07250
3:30pm - Pre-talk meet and greet teatime - 219 Prospect Street, 13 floor, there will be light snacks and beverages in the kitchen area. For more details and upcoming events visit our website at https://statistics.yale.edu/calendar.