In this paper, we present an information-theoretic approach to learning a Mahalanobis distance
function. We formulate the problem as that of minimizing the differential relative entropy …

Alternating minimization represents a widely applicable and empirically successful approach
for finding low-rank matrices that best fit the given data. For example, for the problem of low-…

PennyLane is a Python 3 software framework for differentiable programming of quantum
computers. The library provides a unified architecture for near-term quantum computing devices…

Phase retrieval problems involve solving linear equations, but with missing sign (or phase,
for complex numbers) information. Over the last two decades, a popular generic empirical …

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 …

The multi-label classification problem has generated significant interest in recent years.
However, existing approaches do not adequately address two key challenges:(a) scaling up to …

A vast majority of machine learning algorithms train their models and perform inference by
solving optimization problems. In order to capture the learning and prediction problems …

The objective in extreme multi-label learning is to train a classifier that can automatically tag
a novel data point with the most relevant subset of labels from an extremely large label set. …

Several works have proposed Simplicity Bias (SB)---the tendency of standard training
procedures such as Stochastic Gradient Descent (SGD) to find simple models---to justify why …

Momentum based stochastic gradient methods such as heavy ball (HB) and Nesterov's
accelerated gradient descent (NAG) method are widely used in practice for training deep …