Systems | Information | Learning | Optimization
 

A Topic Modeling Approach to Learning Preference-Behavior from Pairwise Comparisons

The recent explosion of web analytics tools has enabled us to collect an immense amount of partial preferences for large sets of items such as products from Amazon, movies from Netflix, or restaurants from Yelp, from a large and diverse population of users through transactions, clicks, etc. Modeling, learning, and …

High-dimensional low-rank matrix recovery

High-dimensional low-rank structure arises in many applications including genomics, signal processing, and social science. In this talk, we discuss some recent results on high-dimensional low-rank matrix recovery, including low-rank matrix recovery via rank-one projections and structured matrix completion. We provide theoretical justifications for the proposed methods and derive lower bounds …

How a helix is bigger than a plane (but not as big as a ball), and why it matters.

In harmonic analysis we work to understand signals and operators by breaking them up into simpler pieces. We will discuss several problems for which these pieces live on lower dimensional sets whose curvature makes them seem bigger than they are. Surprising connections to some geometric and physical problems will also …

Learning (from) networks: fundamental limits, algorithms, and applications

Network models provide a unifying framework for understanding dependencies among variables in medical, biological, and other sciences. Networks can be used to reveal underlying data structures, infer functional modules, and facilitate experiment design. In practice, however, size, uncertainty and complexity of the underlying associations render these applications challenging. In this …