Publication

Accelerated Stochastic Matrix Inversion: General Theory and Speeding up BFGS Rules for Faster Second-Order Optimization

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Message-Passing Algorithms for Quadratic Minimization

Nicholas Ruozzi

Gaussian belief propagation (GaBP) is an iterative algorithm for computing the mean (and variances) of a multivariate Gaussian distribution, or equivalently, the minimum of a multivariate positive definite quadratic function. Sufficient conditions, such as ...

Microtome Publ2013

treeKL: A distance between high dimension empirical distributions

François Fleuret

This paper offers a methodological contribution for computing the distance between two empirical distributions in an Euclidean space of very large dimension. We propose to use decision trees instead of relying on standard quantification of the feature spac ...

Elsevier Science Bv2013

Algorithmic aspects of sums of Hermitian squares of noncommutative polynomials

Sabine Burgdorf

This paper presents an algorithm and its implementation in the software package NCSOStools for finding sums of Hermitian squares and commutators decompositions for polynomials in noncommuting variables. The algorithm is based on noncommutative analogs of t ...

Springer2013

A survey and comparison of contemporary algorithms for computing the matrix geometric mean

In this paper we present a survey of various algorithms for computing matrix geometric means and derive new second-order optimization algorithms to compute the Karcher mean. These new algorithms are constructed using the standard definition of the Riemanni ...

2012

Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning

Jie Luo

Many state-of-the-art approaches for Multi Kernel Learning (MKL) struggle at finding a compromise between performance, sparsity of the solution and speed of the optimization process. In this paper we look at the MKL problem at the same time from a learning ...

2011

Ultra-Fast Optimization Algorithm for Sparse Multi Kernel Learning

Jie Luo

Idiap2011

Affine Processes On Positive Semidefinite Matrices

Damir Filipovic

This article provides the mathematical foundation for stochastically continuous affine processes on the cone of positive semidefinite symmetric matrices. This analysis has been motivated by a large and growing use of matrix-valued affine processes in finan ...

Institute of Mathematical Statistics2011

Reservoir optimization in recurrent neural networks using properties of Kronecker product

Martin Hasler, Ali Ajdari Rad, Mahdi Jalili

Recurrent neural networks based on reservoir computing are increasingly being used in many applications. Optimization of the topological structure of the reservoir and the internal connection weights for a given task is one of the most important problems i ...

2010

A representation formula for the voltage perturbations caused by diametrically small conductivity inhomogeneities. Proof of uniform validity

Hoài-Minh Nguyên

We revisit the asymptotic formulas originally derived in [D.J. Cedio-Fengya, S. Moskow, M.S. Vogelius, Identification of conductivity imperfections of small diameter by boundary measurements. Continuous dependence and computational reconstruction, Inverse ...

2009

A path integral formalism of DNA looping probability

Ludovica Cotta-Ramusino

In this thesis we describe a path integral formalism to evaluate approximations to the probability density function for the location and orientation of one end of a continuum polymer chain at thermodynamic equilibrium with a heat bath. We concentrate on th ...

EPFL2008