Minimizing the rank of a matrix subject to affine constraints is a fundamental problem with
many important applications in machine learning and statistics. In this paper we propose a …

This paper presents a new framework of recovering missing synchrophasor measurements
(erasures). Leveraging the approximate low-rank property of phasor measurement unit …

Differentially private collaborative filtering is a challenging task, both in terms of accuracy
and speed. We present a simple algorithm that is provably differentially private, while …

Embedding representations power machine intelligence in many applications, including
recommendation systems, but they are space intensive-potentially occupying hundreds of …

We show that matrix completion with trace-norm regularization can be significantly hurt
when entries of the matrix are sampled non-uniformly, but that a properly weighted version …

To support complex data-intensive applications such as personalized recommendations,
targeted advertising, and intelligent services, the data management community has focused …

Motivated by the problem of learning a linear regression model whose parameter is a large
fixed-rank non-symmetric matrix, we consider the optimization of a smooth cost function …

The problem of low-rank matrix completion has recently generated a lot of interest leading to
several results that offer exact solutions to the problem. However, in order to do so, these …

Influenza viruses have been responsible for large losses of lives around the world and
continue to present a great public health challenge. Antigenic characterization based on …

Matrix completion is the problem of recovering a low rank matrix by observing a small
fraction of its entries. A series of recent works (Keshavan 2012),(Jain et al. 2013) and (Hardt …