Constructing tractable dependent probability distributions over structured continuous random vectors is a central problem in statistics and machine learning. It has proven diffic...
The (decisional) learning with errors problem (LWE) asks to distinguish “noisy” inner products of a secret vector with random vectors from uniform. In recent years, the LWE pro...
Abstract. Independent Subspace Analysis (ISA) is an extension of Independent Component Analysis (ICA) that aims to linearly transform a random vector such as to render groups of it...
In blind source separation (BSS), two di erent separation techniques are mainly used: minimal mutual information (MMI), where minimization of the mutual output information yields ...
Fabian J. Theis, Christoph Bauer, Elmar Wolfgang L...
The assessment of multivariate association between two complex random vectors is considered. A number of correlation coefficients based on three popular correlation analysis techni...
We consider the problem minX{0,1}n {c x : aj x bj , j = 1, . . . , m}, where the aj are random vectors with unknown distributions. The only information we are given regarding the ...
We review chessboard distributions for modeling partially specified finite-dimensional random vectors. Chessboard distributions can match a given set of marginals, a given covaria...
Copulas are used in finance and insurance for modeling stochastic dependency. They comprehend the entire dependence structure, not only the correlations. Here they are estimated ...
— We propose a method that takes observations of a random vector as input, and learns to segment each observation into two disjoint parts. We show how to use the internal coheren...
Independent Subspace Analysis (ISA) is a generalization of ICA. It tries to find a basis in which a given random vector can be decomposed into groups of mutually independent rando...