This paper introduces a simple principle for robust statistical inference via appropriate
shrinkage on the data. This widens the scope of high-dimensional techniques, reducing the …

Matrix completion has been well studied under the uniform sampling model and the trace-
norm regularized methods perform well both theoretically and numerically in such a setting …

We study the generalized trace regression with a near low-rank regression coefficient matrix,
which extends notion of sparsity for regression coefficient vectors. Specifically, given a …

In the matrix completion problem, we seek to estimate the missing entries of a matrix from a
small sample of the total number of entries in a matrix. While this task is hopeless in general …

With the emergence of various tensor data, tensor completion from one-bit measurements
has received widespread attention as a fundamental inverse problem. Since tensor rank is a …

We study the problem of estimating a low-rank tensor when we have noisy observations of a
subset of its entries. A rank-, order-, tensor, where, has free variables. On the other hand …

Modern surveys with large sample sizes and growing mixed-type questionnaires require
robust and scalable analysis methods. In this work, we consider recovering a mixed …

MicroRNAs (miRNAs) performs crucial roles in various human diseases, but miRNA-related
pathogenic mechanisms remain incompletely understood. Revealing the potential …

Minimization of the nuclear norm, NNM, is often used as a surrogate (convex relaxation) for
finding the minimum rank completion (recovery) of a partial matrix. The minimum nuclear …

We address a low-rank matrix recovery problem where each column of a rank-r matrix X of
size (d1, d2) is compressed beyond the point of recovery to size L with L<< d1. Leveraging …