activity
20172025
most citedRisk of the Least Squares Minimum Norm Estimator under the Spike Covariance Model

6 citations · 6 across the 4 of their papers we have counts for

collaborators

6 papers

stat.ML2025

SubSearch: Robust Estimation and Outlier Detection for Stochastic Block Models via Subgraph Search

Leonardo Martins Bianco, Christine Keribin, Zacharie Naulet

Community detection is a fundamental task in graph analysis, with methods often relying on fitting models like the Stochastic Block Model (SBM) to observed networks. While many alg…

math.ST2024

On the impossibility of detecting a late change-point in the preferential attachment random graph model

Ibrahim Kaddouri, Zacharie Naulet, Élisabeth Gassiat

We consider the problem of late change-point detection under the preferential attachment random graph model with time dependent attachment function. This can be formulated as a hyp…

math.ST2023

Clustering risk in Non-parametric Hidden Markov and I.I.D. Models

Elisabeth Gassiat, Ibrahim Kaddouri, Zacharie Naulet

We conduct an in-depth analysis of the Bayes risk of clustering in the context of Hidden Markov and i.i.d. models. In both settings, we identify the situations where this risk is c…

stat.ML20196 cited

Risk of the Least Squares Minimum Norm Estimator under the Spike Covariance Model

Yasaman Mahdaviyeh, Zacharie Naulet

We study risk of the minimum norm linear least squares estimator in when the number of parameters depends on , and . We assume that data has…

math.ST2019

Optimal disclosure risk assessment

Federico Camerlenghi, Stefano Favaro, Zacharie Naulet +1

Protection against disclosure is a legal and ethical obligation for agencies releasing microdata files for public use. Consider a microdata sample of size from a finite populat…

stat.ML2017

Exchangeable modelling of relational data: checking sparsity, train-test splitting, and sparse exchangeable Poisson matrix factorization

Victor Veitch, Ekansh Sharma, Zacharie Naulet +1

A variety of machine learning tasks---e.g., matrix factorization, topic modelling, and feature allocation---can be viewed as learning the parameters of a probability distribution o…