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- Laboratoire de Probabilités et Modèles AléatoiresFR15 papers
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5 papers · 1 filter
Graph inference with clustering and false discovery rate control
Tabea Rebafka, Etienne Roquain, Fanny Villers
In this paper, a noisy version of the stochastic block model (NSBM) is introduced and we investigate the three following statistical inferences in this model: estimation of the mod…
Central limit theorem for a partially observed interacting system of Hawkes processes
Chenguang Liu
We observe the actions of a sub-sample of individuals up to time for some large . We model the relationships of individuals by i.i.d. Bernoulli()-random vari…
The k-PDTM : a coreset for robust geometric inference
Claire Brécheteau, Clément Levrard
Analyzing the sub-level sets of the distance to a compact sub-manifold of R d is a common method in TDA to understand its topology. The distance to measure (DTM) was introduced by…
Quantization/clustering: when and why does k-means work?
Clément Levrard
Though mostly used as a clustering algorithm, k-means are originally designed as a quantization algorithm. Namely, it aims at providing a compression of a probability distribution…
Dimension-free PAC-Bayesian bounds for matrices, vectors, and linear least squares regression
Olivier Catoni, Ilaria Giulini
This paper is focused on dimension-free PAC-Bayesian bounds, under weak polynomial moment assumptions, allowing for heavy tailed sample distributions. It covers the estimation of t…