Diversity Maximization over Large Data Sets

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Event starts at this time 2:00 pm – 3:00 pm
Virtual & In Person (view details)
Featured Speaker(s): Sepideh Mahabadi
Cost: Free


The Fall 2022 SDS Seminar Series continues on Friday, October 28th from 2:00 p.m. to 3:00 p.m. with Dr. Sepideh Mahabadi (Senior Researcher at the Algorithms group of Microsoft Research). This event is in-person, but a virtual option will be available as well.

Title: Diversity Maximization over Large Data Sets


In this talk, we consider efficient construction of "composable core-sets" for the task of diversity maximization. A core-set is a subset of the data set that is sufficient for approximating the solution to the whole dataset. A composable core-set is a core-set with the composability property: given a collection of data sets, the union of the core-sets for all data sets in the collection, should be a core-set for the union of the data sets. Using composable core-sets one can obtain efficient solutions to a wide variety of massive data processing applications, including distributed computation (e.g. Map-Reduce model), streaming algorithms, and similarity search.
The notion of diversity can be captured using several measures such as "minimum pairwise distance" and "sum of pairwise distances". In this talk, I will focus on the "determinant maximization" problem which has recently gained a lot of interest for modeling diversity. We present algorithms that are simple to implement and achieve almost optimal approximation guarantee. We further show their effectiveness on standard datasets.


Please contact stat.admin@austin.utexas.edu for the zoom link.

POB 2.302



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