When: May 6, 2015, 12:30 PM
Location: Researchers' Link, Discovery Building
Contact: 608-316-4401, firstname.lastname@example.org
Quantifying ad fraud – contamination estimation via convex relaxations
Identifying contamination in datasets is important in a wide variety of settings, including view and click fraud in online advertising. After a brief overview of digital ad fraud, I’ll describe a technique for estimating contamination in large, categorical datasets. The technique involves solving a series of convex programs, resulting in a bound on the minimum number of data points that must be discarded (i.e, the level of contamination) from an empirical data set in order to match a model to within a specified goodness-of-fit, controlled by a p-value. I’ll discuss convergence guarantees, provide geometric interpretations, and highlight practical aspects of solving over a million convex optimizations nightly.
SILO is a lecture series with speakers from the UW faculty, graduate students or invited researchers that discuss mathematical related topics. The seminars are organized by WID’s Optimization research group.
SILO’s purpose is to provide a forum that helps connect and recruit mathematically-minded graduate students. SILO is a lunch-and-listen format, where speakers present interesting math topics while the audience eats lunch.
Speaker: Matt Malloy, UW-Madison