Events

Computationally Efficient Reductions Between Some Statistical Models

Speaker: Ashwin Pananjady (Georgia Tech) Gerald D. McInvale Assistant Professor, Georgia Tech Georgia Institute of Technology Monday, September 21, 2026 4:00PM - 5:00PM 3:30pm - Pre-talk meet and greet teatime - 219 Prospect Street, 13th floor, there will be light snacks and beverages in the kitchen area. Location: Yale Institute for Foundations of Data Science, Kline Tower 13th Floor, Room 1327, New Haven, CT 06511 and via Webcast: https://yale.hosted.panopto.com/Panopto/Pages/Viewer.aspx?id=92b0159c-6a07-4ed4-bbf1-b4a400f552b1

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 computationally efficient algorithms. I will present several such reductions and discuss their applications to the aforementioned problems.

This is joint work with Mengqi Lou and Guy Bresler.

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