MIT Stochastics & Statistics Seminar Series: Robert Nowak
Title: Ranking and Embedding From Pairwise Comparisons
Abstract: Ranking, clustering, or metrically-embedding a set of items (e.g., images, documents, products) based on human judgments can shed light on preferences and human reasoning. Two common approaches to collecting data from people are rating and comparison-based systems. Ratings can be difficult to calibrate across people. Also, in certain applications, it may be far easier to compare items than to rate them (e.g., rating funniness of jokes is more difficult than deciding which of two jokes is more funny). For these reasons, pairwise comparisons are often used in practice. This talk focuses on ranking and metric embedding from pairwise comparisons, and theory and methods for adaptive data collection in particular. Adaptive data collection can reduce the number of comparisons required to learn an accurate ranking or embedding, but is challenging to mathematically analyze. The gap between theory and practice is relatively small in the case of ranking, but many difficult mathematical questions remain for embedding from pairwise comparisons. The talk will also illustrate progress and challenges through several ranking and embedding experiments carried out with a new open-source software system called NEXT.
Bio: Robert Nowak is the McFarland-Bascom Professor in Engineering at the University of Wisconsin-Madison, where his research focuses on signal processing, machine learning, optimization, and statistics. The BeerMapper and NEXT systems are recent applications of his research. Rob is a professor in Electrical and Computer Engineering, as well as being affiliated with the departments of Computer Sciences and Biomedical Engineering at the University of Wisconsin. He is also a Fellow of the IEEE and the Wisconsin Institute for Discovery, a member of the Wisconsin Optimization group, and organizer of the SILO seminar series.
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