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SILO Seminar Series: Sanmi Koyejo
October 18, 2017 @ 12:30 pm - 1:30 pm
Learning with Aggregated Data; a Tale of Two Approaches
For many applications in healthcare, econometrics, financial forecasting and climate science, data can only be obtained as aggregates. This begs the question, can one construct accurate models using only aggregates? I will present two vignettes outlining recent work towards an answer.
First, consider a sparse linear model learned from IID data aggregated into groups, where only empirical moments of each group are observed. Despite this obfuscation of individual data values, we show that subject to standard conditions, the parameter is recoverable with high probability using standard algorithms. Second, consider learning with aggregated correlated data such as time series or spatial data. Here, standard techniques fail. Instead, we propose a simple procedure which exploits Fourier transforms and achieves strong generalization error guarantees. In both settings, empirical evaluation on datasets from healthcare, agricultural studies, ecological surveys and climate science are presented to demonstrate efficacy.
Joint work with Avradeep Bhowmik and Joydeep Ghosh.
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.