Search Results for author: Zaïd Harchaoui

Found 5 papers, 0 papers with code

A Fast, Consistent Kernel Two-Sample Test

no code implementations NeurIPS 2009 Arthur Gretton, Kenji Fukumizu, Zaïd Harchaoui, Bharath K. Sriperumbudur

A kernel embedding of probability distributions into reproducing kernel Hilbert spaces (RKHS) has recently been proposed, which allows the comparison of two probability measures P and Q based on the distance between their respective embeddings: for a sufficiently rich RKHS, this distance is zero if and only if P and Q coincide.

Kernel Change-point Analysis

no code implementations NeurIPS 2008 Zaïd Harchaoui, Eric Moulines, Francis R. Bach

Change-point analysis of an (unlabelled) sample of observations consists in, first, testing whether a change in the distribution occurs within the sample, and second, if a change occurs, estimating the change-point instant after which the distribution of the observations switches from one distribution to another different distribution.

Two-sample testing

Catching Change-points with Lasso

no code implementations NeurIPS 2007 Céline Levy-Leduc, Zaïd Harchaoui

We propose a new approach for dealing with the estimation of the location of change-points in one-dimensional piecewise constant signals observed in white noise.

Variable Selection

Testing for Homogeneity with Kernel Fisher Discriminant Analysis

no code implementations NeurIPS 2007 Moulines Eric, Francis R. Bach, Zaïd Harchaoui

This provides us with a consistent nonparametric test statistic, for which we derive the asymptotic distribution under the null hypothesis.

DIFFRAC: a discriminative and flexible framework for clustering

no code implementations NeurIPS 2007 Francis R. Bach, Zaïd Harchaoui

We present a novel linear clustering framework (Diffrac) which relies on a linear discriminative cost function and a convex relaxation of a combinatorial optimization problem.

Combinatorial Optimization General Classification

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