Most of my publications are available on ArXiv or HAL.


C. Donnat, O. Klopp, and N. Verzelen. One-Bit Total Variation Denoising over Networks with Applications to Partially Observed Epidemics. [ArXiv]

B. Even, C. Giraud, and N. Verzelen. Computation-information gap in high-dimensional clustering. Accepted in COLT 2024 [ArXiv]

S. Thépaut and N. Verzelen. Optimal Estimation of Schatten Norms of a rectangular Matrix. Accepted in Annals of Statistics [ArXiv]


J. Mariel, I. Sanchez, N. Verzelen, F. Massol, S. Carrière, and V. Labeyrie. The role of farmers’ networks in sourcing planting material and information in a context of agroforestry transition in Madagascar. Agricultural Systems, 217, 2024. [Article]

E. Pilliat, A. Carpentier, and N. Verzelen. Optimal rates for ranking a permuted isotonic matrix in polynomial time. Symposium on Discrete Algorithms (SODA), 3236–3273, 2024. [Article] [ArXiv]

E. Saad, G. Blanchard, and N. Verzelen. Covariance Adaptive Best Arm Identification. Neur'IPS, 2023. [Article] [ArXiv]

E. Saad, A. Carpentier, and N. Verzelen. Active Ranking of Experts Based on their Performances in Many Tasks. ICML (oral presentation), 2023. [Article] [ArXiv]

E. Pilliat, A. Carpentier, and N. Verzelen. Optimal Permutation Estimation in CrowdSourcing problems. Annals of Statistics, 51(3):935–961, 2023. [Article] [ArXiv]

C. Giraud, Y. Issartel, and N. Verzelen Localization in 1D non-parametric latent space models from pairwise affinities Electronic journal of statistics, 17(1):1587–1662, 2023. [Article] [ArXiv]

E. Pilliat, A. Carpentier, and N. Verzelen. Optimal multiple change-point detection for high-dimensional data. Electronic journal of statistics, 17(1):1240–1315, 2023. [Article] [ArXiv]

N. Verzelen, M. Fromont, M. Lerasle, and P. Reynaud-Bouret. Optimal Change-Point Detection and Localization. Annals of Statistics, 51(4):1586–1610, 2023. [Article] [ArXiv]

E. Roquain and N. Verzelen. False discovery rate control with unknown null distribution: is it possible to mimic the oracle? Annals of Statistics, 50(2):1095–1123, 2022. [Article] [ArXiv] [Vignette]

A. Carpentier and N. Verzelen. Optimal sparsity testing in linear regression model. Bernoulli, 27(2):727–750, 2021. [Article] [ArXiv]

K. Bogerd, R. Castro, R. van der Hofstad, and N. Verzelen. Detecting a planted community in an inhomogeneous random graph. Bernoulli, 27(2):1159–1188, 2021. [Article] [ArXiv]

E. Arias-Castro, A. Channarond, B. Pelletier, and N. Verzelen. On the estimation of latent distances using graph distances. Electronic journal of statistics, 15(1):722–747, 2021. [Article] [ArXiv]

A. Carpentier, S. Delattre, E. Roquain, and N. Verzelen. Estimating minimum effect with outlier selection. Annals of Statistics, 49(1):272–294, 2021. [Article] [ArXiv]

E. Arias-Castro, R. Huang, and N. Verzelen. Detection of sparse positive dependence. Electronic journal of statistics, 14(1):702–730, 2020. [Article] [ArXiv]

F. Bunea, C. Giraud, X. Luo, M. Royer, and N. Verzelen. Model assisted variable clustering: minimax-optimal recovery and algorithms. Annals of Statistics, 48(1):111–137, 2020. [Article] [ArXiv] [preliminary version]

O. Klopp and N. Verzelen. Optimal graphon estimation in cut distance. Probability Theory and Related Fields, 174(3):1033–1090, 2019. [Article] [ArXiv]

A. Carpentier and N. Verzelen. Adaptive estimation of the sparsity in the Gaussian vector model. Annals of Statistics, 47(1):93–126, 2019. [Article] [ArXiv]

C. Giraud and N. Verzelen. Partial recovery bounds for clustering with the relaxed K-means. Mathematical Statistics and Learning, 1(3):317–374, 2018. [Article] [ArXiv]

O. Collier, L. Comminges, A. Tsybakov, and N. Verzelen. Optimal adaptive estimation of linear functionals under sparsity. Annals of Statistics, 46(6A):3130–3150, 2018. [Article] [ArXiv]

N. Verzelen and E. Gassiat. Adaptive estimation of high-dimensional signal-to-noise ratios. Bernoulli, 24(4B):3683–3710, 2018. [Article] [ArXiv]

E. Arias-Castro, S. Bubeck, G. Lugosi, and N. Verzelen. Detecting Markov random fields hidden in white noise. Bernoulli, 24(4B):3628–3656, 2018. [Article] [ArXiv]

J. Banks, C. Moore, R. Vershynin, N. Verzelen, and J. Xu. Information-theoretic bounds and phase transitions in clustering, sparse PCA, and submatrix localization. IEEE Transactions on Information Theory, 64(7):4872–4894, 2018. [Article] [ArXiv] (presented at ISIT 2017)

O. Klopp, A. Tsybakov, and N. Verzelen. Oracle inequalities for network models and sparse graphon estimation. Annals of Statistics, 45(1):316–354, 2017. [Article] [ArXiv]

N. Verzelen and E. Arias-Castro. Detection and feature selection in sparse mixture models. Annals of Statistics, 45(5):1920–1950, 2017. [Article] [ArXiv]

E. Arias-Castro, G. Lugosi, and N. Verzelen. Detecting a path of correlations in a network. ALEA, 14(1):33–44, 2017. [Article] [ArXiv]

E. Arias-Castro and N. Verzelen. Discussion of Influential features PCA for high dimentional clustering. Annals of Statistics, 44(6):2360–2365, 2016. [Article] [ArXiv]

C. Charbonnier, N. Verzelen, and F. Villers. A global homogeneity test for high-dimensional linear regression. Electronic journal of statistics, 9(1):318–382, 2015. [Article] [ArXiv]

M. Thomas, N. Verzelen, P. Barbillon, Oliver T Coomes, S. Caillon, D. McKey, M. Elias, E. Garine, C. Raimond, E. Dounias, et al. A network-based method to detect patterns of local crop biodiversity: validation at the species and infra-species levels. Advances in Ecological Research, 53:259–320, 2015. [Article]

N. Verzelen and E. Arias-Castro. Community detection in sparse random networks. Annals of Applied Probability, 25(6):3465–3510, 2015. [Article] [ArXiv]

I. Vilmus, M. Ecarnot, N. Verzelen, and P. Roumet. Monitoring Nitrogen Leaf Resorption Kinetics by Near-Infrared Spectroscopy during Grain Filling in Durum Wheat in Different Nitrogen Availability Conditions. Crop Science, 54(1):284–296, 2014. [Article]

E. Arias-Castro and N. Verzelen. Community detection in dense random networks. Annals of Statistics, 42(3):940–969, 2014. [Article] [ArXiv]

N. Hilgert, A. Mas, and N. Verzelen. Minimax adaptive tests for the functional linear model. Annals of Statistics, 41(2):838–869, 2013. [Article] [ArXiv]

C Giraud, S Huet, and N Verzelen. Graph selection with GGMselect. Statistical Applications in Genetics and Molecular Biology, 11(3):Article–3, 2012. [Article] [ArXiv]

C. Giraud, S. Huet, and N. Verzelen. High-dimensional regression with unknown variance. Statistical Science, 27(4):500–518, 2012. [Article] [ArXiv]

N. Verzelen. Minimax risks for sparse regressions: ultra-high dimensional phenomenons. Electronic journal of statistics, 6:38–90, 2012. [Article] [ArXiv]

N. Verzelen, Wenwen Tao, and H.-G. Mueller. Inferring stochastic dynamics from functional data. Biometrika, 99(3):533–550, 2012. [Article]

Y. Ingster, A. Tsybakov, and N. Verzelen. Detection boundary in sparse regression. Electronic journal of statistics, 4:1476–1526, 2010. [Article] [ArXiv]

N. Verzelen. Adaptive estimation of covariance matrices via Cholesky decomposition. Electronic journal of statistics, 4:1113–1150, 2010. [Article] [ArXiv]

N. Verzelen. Adaptive estimation of stationary Gaussian fields. Annals of Statistics, 38(3):1363–1402, 2010. [Article] [ArXiv]

N. Verzelen. Data-driven neighborhood selection of a Gaussian field. Computational statistics & data analysis, 54(5):1355–1371, 2010. [Article] [ArXiv]

N. Verzelen and F. Villers. Goodness-of-fit tests for high-dimensional Gaussian linear models. Annals of Statistics, 38(2):704–752, 2010. [Article] [ArXiv]

N. Verzelen. High-dimensional Gaussian model selection on a Gaussian design. Annales de l’IHP Probabilités et statistiques, 46(2):480–524, 2010. [Article] [ArXiv]

N. Verzelen and F. Villers. Tests for Gaussian graphical models. Computational Statistics & Data Analysis, 53(5):1894–1905, 2009. [Article]

N. Cressie and N. Verzelen. Conditional-mean least-squares fitting of Gaussian Markov random fields to Gaussian fields. Computational Statistics & Data Analysis, 52(5):2794–2807, 2008. [Article]

N. Verzelen, N. Picard, and S. Gourlet-Fleury. Approximating spatial interactions in a model of forest dynamics as a means of understanding spatial patterns. Ecological Complexity, 3(3):209–218, 2006. [Article]


N. Verzelen. Contributions to Signal Detection, Network Analysis, and Clustering. Habilitation thesis, Université de Montpellier, 2022. [pdf]

N. Verzelen. Gaussian graphical models and Model selection. PhD thesis, Université Paris Sud-Paris XI, 2008. [pdf]