[gov3009-l] Applied Statistics - 3/9 - Stefanie Jegelka

Aaron Kaufman aaronkaufman at fas.harvard.edu
Mon Mar 7 10:09:22 EST 2016


Hi everyone!

This week at the Applied Statistics Workshop we will be welcoming *Stefanie
Jegelka*, Assistant Professor of Electrical Engineering and Computer
Science at MIT. She will be presenting work entitled *Algorithms and new
applications for determinantal point processes**.*  Please find the
abstract below and on the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/presentations/392016-stefanie-jegelka-mit-time-coming-soon>
.

As usual, we will meet in CGIS Knafel Room 354 from noon to 1:30pm, and
lunch will be provided.  See you all there! To view previous Applied
Statistics presentations, please visit the website
<http://projects.iq.harvard.edu/applied.stats.workshop-gov3009/videos>.

-- Aaron Kaufman

Title: Algorithms and new applications for determinantal point processes

Abstract: Many real-world inference problems are, at their core, subset
selection problems. Probabilistic models for such scenarios rely on having
distributions over discrete sets that are sufficiently accurate yet
computationally efficient to work with. We focus on sub-families of such
distributions whose special mathematical properties are the basis for fast
algorithms. As a specific example, Determinantal Point Processes (DPPs)
have recently become popular in machine learning, as elegant and tractable
probabilistic models of diversity. We explore new applications of DPPs for
variational inference over combinatorial objects, such as coupled cascades
in a collection of networks, where we are able to leverage combinatorial
and convex structure in the problem. In the second part of the talk, I will
outline ideas for speeding up sampling from DPPs. These ideas build on new
insights for algorithms that compute bilinear inverse forms. These results
have applications beyond DPPs, including sensing with Gaussian Processes
and submodular maximization.
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