Item recommendation is the task of predicting a personalized ranking on a set of items (eg
websites, movies, products). In this paper, we investigate the most common scenario with …

In this paper, we show that tracking multiple people whose paths may intersect can be
formulated as a multi-commodity network flow problem. Our proposed framework is …

This thesis explores applications of non-commutative harmonic analysis to machine
learning. In particular, we address learning on groups and learning in a way that is invariant …

A cycle in a brain network is a subset of a connected component with redundant additional
connections. If there are many cycles in a connected component, the connected component …

Statistical models on full and partial rankings of n items are often of limited prac-tical use for
large n due to computational consideration. We explore the use of non-parametric models …

In this paper, the partial-information-based (PIB) distributed filtering problem is addressed
for two-targets tracking sensor networks. Different from existing distributed filters, the …

Permutations are ubiquitous in many real-world problems, such as voting, ranking, and data
association. Representing uncertainty over permutations is challenging, since there are n …

We give an algorithm for the on-line learning of permutations. The algorithm maintains its
uncertainty about the target permutation as a doubly stochastic weight matrix, and makes …

Many matching, tracking, sorting, and ranking problems require probabilistic reasoning
about possible permutations, a set that grows factorially with dimension. Combinatorial …

We develop a cognitive modeling approach, motivated by classic theories of knowledge
representation and judgment from psychology, for combining people's rankings of items. The …