Blind Unwrapping of Modulo Reduced Gaussian Vectors: Recovering MSBs From LSBs

Autoregressive Gaussian Random Vectors of First Order

Recovering zeros of polynomials modulo a prime

Let p be a prime and Fp the finite field with p elements. We show how, when given an irreducible bivariate polynomial F ∈ Fp[X,Y ] and an approximation to a zero, one can recover the root efficiently, if the approximation is good enough. The strategy can be generalized to polynomials in the variables X1, . . . , Xm over the field Fp. These results have been motivated by the predictability probl...

Recovering Distributions from Gaussian RKHS Embeddings

Recent advances of kernel methods have yielded a framework for nonparametric statistical inference called RKHS embeddings, in which all probability distributions are represented as elements in a reproducing kernel Hilbert space, namely kernel means. In this paper, we consider the recovery of the information of a distribution from an estimate of the kernel mean, when a Gaussian kernel is used. T...

Blind Source Separation: Recovering Signals from Mixtures

autoregressive gaussian random vectors of first order