Learning Human Preferences and Perceptions From Data

Speaker: Prof. Robert D. Nowak
Affiliation: University of Wisconsin-Madison

Abstract:  Modeling human perception has many applications in cognitive, social, and educational science, as well as in advertising and commerce.  This talk discusses theory and methods for learning rankings and embeddings representing perceptions from datasets of human judgments, such as ratings or comparisons. I will briefly describe an ongoing large-scale experiment with the New Yorker magazine that deals with ranking cartoon captions using on our nextml.org system.  Then I will discuss our recent work on ordinal embedding, also known as non-metric multidimensional scaling, which is the problem of representing items (e.g., images) as points in a low-dimensional Euclidean space given constraints of the form “item i is closer to item j than item k.” In other words, the goal is to find a geometric representation of data that is faithful to comparative similarity judgments. This classic problem is often used to gauge and visualize perceptual similarities. A variety of algorithms exist for learning metric embeddings from comparison data, but the accuracy and performance of these methods were poorly understood.  I will present a new theoretical framework that quantifies the accuracy of learned embeddings and indicates how many comparisons suffice as a function of the number of items and the dimension of the embedding.  Furthermore, the theory points to new algorithms that outperform previously proposed methods. I will also describe a few applications of ordinal embedding.

Biography: 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, Statistics, 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 Research Consortium and Machine Learning at Wisconsin, and organizer of the SILO seminar series.  Rob is also an Adjoint Professor at the Toyota Technological Institute at Chicago.

For more information contact Professors Suhas Diggavi & Mani Srivastava

Date(s) - Nov 07, 2016
12:30 pm - 1:30 pm

EE-IV Shannon Room #54-134
420 Westwood Plaza - 5th Flr., Los Angeles CA 90095