Efficient Simulation of High Dimensional Gaussian Vectors

Autoregressive Gaussian Random Vectors of First Order

Efficient Sparse Clustering of High-Dimensional Non-spherical Gaussian Mixtures

We consider the problem of clustering data points in high dimensions, i.e., when the number of data points may be much smaller than the number of dimensions. Specifically, we consider a Gaussian mixture model (GMM) with two non-spherical Gaussian components, where the clusters are distinguished by only a few relevant dimensions. The method we propose is a combination of a recent approach for le...

High dimensional gaussian classification

High dimensional data analysis is known to be as a challenging problem (see [10]). In this article, we give a theoretical analysis of high dimensional classification of Gaussian data which relies on a geometrical analysis of the error measure. It links a problem of classification with a problem of nonparametric regression. We give an algorithm designed for high dimensional data which appears st...

Efficient Rare Events Simulation of Gaussian Processes

A key feature of modern broadband networks is their ability to provide stringent Quality of Service (QoS) guarantees to different classes of users. A primary QoS parameter is the Cell Loss Probability (CLP), whose typical values can be very small and therefore difficult to estimate through standard Monte Carlo (MC) simulation, since long run times are required to achieve accurate results. In th...

High-Dimensional Gaussian Process Bandits

Many applications in machine learning require optimizing unknown functions defined over a high-dimensional space from noisy samples that are expensive to obtain. We address this notoriously hard challenge, under the assumptions that the function varies only along some low-dimensional subspace and is smooth (i.e., it has a low norm in a Reproducible Kernel Hilbert Space). In particular, we prese...