17 citations · 34 across the 9 of their papers we have counts for
10 papers
MCMC Louvain for Online Community Detection
Yves Darmaillac, Sébastien Loustau
We introduce a novel algorithm of community detection that maintains dynamically a community structure of a large network that evolves with time. The algorithm maximizes the modula…
A Quasi-Bayesian Perspective to Online Clustering
Le Li, Benjamin Guedj, Sébastien Loustau
When faced with high frequency streams of data, clustering raises theoretical and algorithmic pitfalls. We introduce a new and adaptive online clustering algorithm relying on a qua…
Bandwidth selection in kernel empirical risk minimization via the gradient
Michaël Chichignoud, Sébastien Loustau
In this paper, we deal with the data-driven selection of multidimensional and possibly anisotropic bandwidths in the general framework of kernel empirical risk minimization. We pro…
The algorithm of noisy k-means
Camille Brunet, Sébastien Loustau
In this note, we introduce a new algorithm to deal with finite dimensional clustering with errors in variables. The design of this algorithm is based on recent theoretical advances…
Noisy classification with boundary assumptions
Sébastien Loustau, Clément Marteau
We address the problem of classification when data are collected from two samples with measurement errors. This problem turns to be an inverse problem and requires a specific treat…
Adaptive Noisy Clustering
Michael Chichignoud, Sébastien Loustau
The problem of adaptive noisy clustering is investigated. Given a set of noisy observations , , the goal is to design clusters associated with the law of $X…
Anisotropic oracle inequalities in noisy quantization
Sébastien Loustau
The effect of errors in variables in quantization is investigated. We prove general exact and non-exact oracle inequalities with fast rates for an empirical minimization based on a…
Fast rates for noisy clustering
Sébastien Loustau
The effect of errors in variables in empirical minimization is investigated. Given a loss and a set of decision rules , we prove a general upper bound for an empir…
Statistical learning with indirect observations
Sébastien Loustau
Let be a random couple with unknown distribution . Let $\GG$ be a class of measurable functions and a loss function. The problem o…
Minimax fast rates for discriminant analysis with errors in variables
Sébastien Loustau, Clément Marteau
The effect of measurement errors in discriminant analysis is investigated. Given observations , where denotes a random noise, the goal is to predict the density of a…