The problem of matching two sets of features appears in various tasks of computer vision
and can be often formalized as a problem of permutation estimation. We address this …

A recent line of research investigates how algorithms can be augmented with machine-
learned predictions to overcome worst case lower bounds. This area has revealed …

The problem of matching not just two, but m different sets of objects to each other arises in a
variety of contexts, including finding the correspondence between feature points across …

Bipartite matching problems characterize many situations, ranging from ranking in
information retrieval to correspondence in vision. Exact inference in real-world applications …

Matching between two sets of objects is typically approached by finding the object pairs that
collectively maximize the joint matching score. In this paper, we argue that this single …

We propose two statistical tests to determine if two samples are from different distributions.
Our test statistic is in both cases the distance between the means of the two samples …

We investigate the problem of finding the correspondence from multiple images, which is a
challenging combinatorial problem. In this work, we propose a robust solution by exploiting …

Score matching is an alternative to maximum likelihood (ML) for estimating a probability
distribution parametrized up to a constant of proportionality. By fitting the''score''of the …

Feature selection is central to modern data science. The 'stability'of a feature selection
algorithm refers to the sensitivity of its choices to small changes in training data. This is, in …

Affine transformations of the plane have been used in a number of model-based recognition
systems. Because the underlying mathematics are based on exact data, in practice various …