Solving sparse systems of linear equations is a crucial component in many science and
engineering problems, like simulating physical systems. Sparse matrix factorization …

We consider the well-studied problem of predicting the timevarying covariance matrix of a
vector of financial returns. Popular methods range from simple predictors like rolling window …

The field of Contextual Optimization (CO) integrates machine learning and optimization to
solve decision making problems under uncertainty. Recently, a risk sensitive variant of CO …

More than seventy years ago Harry Markowitz formulated portfolio construction as an
optimization problem that trades off expected return and risk, defined as the standard …

In covariance matrix estimation, one of the challenges lies in finding a suitable model and an
efficient estimation method. Two commonly used modelling approaches in the literature …

Feature description is a critical task in Augmented Reality Tracking. This article introduces a
Convex Based Feature Descriptor (CBFD) system designed to withstand rotation, lighting …

We consider the use of deep learning for covariance estimation. We propose to globally
learn a neural network that will then be applied locally at inference time. Leveraging recent …

CONVEX OPTIMIZATION AND IMPLICIT DIFFERENTIATION METHODS FOR CONTROL AND
ESTIMATION A DISSERTATION SUBMITTED TO THE DEPARTMENT O Page 1 CONVEX …

Forecasting covariance matrices across different investment horizons and asset classes is
central for portfolio construction and risk analysis. However, the industry-standard methods …

We consider the problem of managing a portfolio of moving-band statistical arbitrages
(MBSAs), inspired by the Markowitz optimization framework. We show how to manage a …