7 citations · 17 across the 12 of their papers we have counts for
17 papers
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…
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…
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…
Frontiers to the learning of nonparametric hidden Markov models
Kweku Abraham, Elisabeth Gassiat, Zacharie Naulet
Hidden Markov models (HMMs) are flexible tools for clustering dependent data coming from unknown populations, allowing nonparametric modelling of the population densities. Identifi…
Optimal estimation of high-order missing masses, and the rare-type match problem
Stefano Favaro, Zacharie Naulet
Consider a random sample from an unknown discrete distribution on a countable alphabet , and let $(Y_{n,j})_{j\ge…
Bayesian Nonparametric Inference for "Species-sampling" Problems
Cecilia Balocchi, Stefano Favaro, Zacharie Naulet
Given an observed sample from a population of individuals belonging to species, "species-sampling" problems (SSPs) call for estimating some features of the unknown species composit…
Asymptotic Analysis of Statistical Estimators related to MultiGraphex Processes under Misspecification
Zacharie Naulet, Judith Rousseau, François Caron
This article studies the asymptotic properties of Bayesian or frequentist estimators of a vector of parameters related to structural properties of sequences of graphs. The estimato…
Fundamental limits for learning hidden Markov model parameters
Kweku Abraham, Zacharie Naulet, Elisabeth Gassiat
We study the frontier between learnable and unlearnable hidden Markov models (HMMs). HMMs are flexible tools for clustering dependent data coming from unknown populations. The mode…
Near-optimal estimation of the unseen under regularly varying tail populations
Stefano Favaro, Zacharie Naulet
Given samples from a population of individuals belonging to different species, what is the number of hitherto unseen species that would be observed if new samples were…
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…
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…
Adaptive Bayesian density estimation in sup-norm
Zacharie Naulet
We investigate the problem of deriving adaptive posterior rates of contraction on balls in density estimation. Although it is known that log-density priors ca…
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…
Bootstrap estimators for the tail-index and for the count statistics of graphex processes
Zacharie Naulet, Daniel M. Roy, Ekansh Sharma +1
Graphex processes resolve some pathologies in traditional random graph models, notably, providing models that are both projective and allow sparsity. Most of the literature on grap…
Bayesian nonparametric estimation for Quantum Homodyne Tomography
Zacharie Naulet, Eric Barat
We estimate the quantum state of a light beam from results of quantum homodyne tomography noisy measurements performed on identically prepared quantum systems. We propose two Bayes…
Tails assumptions and posterior concentration rates for mixtures of Gaussians
Zacharie Naulet, Judith Rousseau
Nowadays in density estimation, posterior rates of convergence for location and location-scale mixtures of Gaussians are only known under light-tail assumptions; with better rates…
Some aspects of symmetric Gamma process mixtures
Zacharie Naulet, Eric Barat
In this article, we present some specific aspects of symmetric Gamma process mixtures for use in regression models. We propose a new Gibbs sampler for simulating the posterior and…