Learning Human Preferences and Perceptions From Data

Modeling human preferences and perceptions 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 that represent perceptions from datasets of human judgments, such as ratings or comparisons. Professor Nowak will briefly describe an ongoing large-scale experiment with the New Yorker magazine that deals with ranking cartoon captions using his nextml.org system. Then he will discuss 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. Professor Nowak will present 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. This theory also points to new algorithms that outperform previously proposed methods. He will also describe a few applications of ordinal embedding.
Rob 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. 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 @ Wisconsin. Rob is also an Adjoint Professor at the Toyota Technological Institute at Chicago.