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 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.
3:30pm - Pre-talk meet and greet teatime - 219 Prospect Street, 13th floor, there will be light snacks and beverages in the kitchen area.
