Powerful statistical models that can be learned efficiently from large amounts of data are
currently revolutionizing computer vision. These models possess a rich internal structure …

Logarithms of determinants of large positive definite matrices appear ubiquitously in
machine learning applications including Gaussian graphical and Gaussian process models …

We describe a new optimization scheme for finding high-quality clusterings in planar graphs
that uses weighted perfect matching as a subroutine. Our method provides lower-bounds on …

We present a number of contributions to the LP relaxation approach to weighted constraint
satisfaction (= Gibbs energy minimization). We link this approach to many works from …

Semi-global matching, originally introduced in the context of dense stereo, is a very
successful heuristic to minimize the energy of a pairwise multi-label Markov Random Field …

Recently, unsupervised image segmentation has become increasingly popular. Starting
from a superpixel segmentation, an edge-weighted region adjacency graph is constructed …

This paper empirically compares four local search algorithms for correlation clustering by
applying these to a variety of instances of the correlation clustering problem for the tasks of …

Large-scale multiple testing tasks often exhibit dependence. Leveraging the dependence
between individual tests is still one challenging and important problem in statistics. With …

Discrete graphical models (also known as discrete Markov random fields) are a major
conceptual tool to model the structure of optimization problems in computer vision. While in …

We study the forrelation problem: given a pair of $ n $-bit Boolean functions $ f $ and $ g $,
estimate the correlation between $ f $ and the Fourier transform of $ g $. This problem is …