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.
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.
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.
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.
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.