We advance both the theory and practice of robust $\ell_p $-quasinorm regression for $ p\in
(0, 1] $ by using novel variants of iteratively reweighted least-squares (IRLS) to solve the …

This paper introduces a robust unsupervised SE (3) point cloud registration method that
operates without requiring point correspondences. The method frames point clouds as …

Particle Imaging Velocimetry (PIV) is a classical method that estimates fluid flow by
analyzing the motion of injected particles. To reconstruct and track the swirling particles is a …

We prove that a generic matrix of bounded rank is uniquely recoverable—up to a
permutation of its rows and columns—from an arbitrary permutation of its entries. This can …

In data science, a number of applications have been emerging involving data recovery from
permutations. Here, we study this problem theoretically for data organized in a rank-deficient …

This paper studies the problem of data permutation recovery, where the goal is to estimate
the ordering of an-dimensional data vector given a noisy observation of it. The focus is on …

Applications such as unbalanced and fully shuffled regression can be approached by
optimizing regularized optimal transport (OT) distances, such as the entropic OT and …

We give unique recovery guarantees for matrices of bounded rank that have undergone
permutations of their entries. We even do this for a more general matrix structure that we call …

Analyzing the data collected from different sources requires unpaired data analysis to
account for the absence of correspondence between the random variable Y and the …

In the analysis of data sets consisting of (X, Y)-pairs, a tacit assumption is that each pair
corresponds to the same observational unit. If, however, such pairs are obtained via record …