output
20002026
most citedInformation theory explanation of the fluctuation theorem, maximum entropy production and self-organized criticality in non-equilibrium stationary states

479 citations

researchers with a paper here

Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.

collaborating institutions
Showing math.STShow all

20 papers · 1 filter

math.ST2025

Computational barriers for permutation-based problems, and cumulants of weakly dependent random variables

Bertrand Even, Christophe Giraud, Nicolas Verzelen

In many high-dimensional problems,polynomial-time algorithms fall short of achieving the statistical limits attainable without computational constraints. A powerful approach to pro…

math.ST2025

Estimation and variable selection in high dimension in nonlinear mixed-effects models

Antoine Caillebotte, Estelle Kuhn, Sarah Lemler

We consider nonlinear mixed effects models including high-dimensional covariates to model individual parameters variability. The objective is to identify relevant covariates among…

math.ST2024

Seriation of Toeplitz and latent position matrices: optimal rates and computational trade-offs

Clément Berenfeld, Alexandra Carpentier, Nicolas Verzelen

In this paper, we consider the problem of seriation of a permuted structured matrix based on noisy observations. The entries of the matrix relate to an expected quantification of i…

math.ST2024

Computation-information gap in high-dimensional clustering

Bertrand Even, Christophe Giraud, Nicolas Verzelen

We investigate the existence of a fundamental computation-information gap for the problem of clustering a mixture of isotropic Gaussian in the high-dimensional regime, where the am…

math.ST2023

Optimal rates for ranking a permuted isotonic matrix in polynomial time

Emmanuel Pilliat, Alexandra Carpentier, Nicolas Verzelen

We consider a ranking problem where we have noisy observations from a matrix with isotonic columns whose rows have been permuted by some permutation *. This encompasses many mo…

math.ST2022★ 1 cited

Optimal Permutation Estimation in Crowd-Sourcing problems

Emmanuel Pilliat, Alexandra Carpentier, Nicolas Verzelen

Motivated by crowd-sourcing applications, we consider a model where we have partial observations from a bivariate isotonic n x d matrix with an unknown permutation * acting on…

math.ST2022★ 4 cited

Bayesian high-dimensional covariate selection in non-linear mixed-effects models using the SAEM algorithm

Marion Naveau, Guillaume Kon Kam King, Renaud Rincent +2

High-dimensional variable selection, with many more covariates than observations, is widely documented in standard regression models, but there are still few tools to address it in…

math.ST2021

Note on the approximation of the conditional intensity of non-stationary cluster point processes

Edith Gabriel, Joël Chadoeuf

In this note we consider non-stationary cluster point processes and we derive their conditional intensity, i.e. the intensity of the process given the locations of one or more even…

math.ST2021

Optimal Estimation of Schatten Norms of a rectangular Matrix

Solène Thépaut, Nicolas Verzelen

We consider the twin problems of estimating the effective rank and the Schatten norms of a rectangular matrix from noisy observations. When…

math.ST2020★ 4 cited

Anomaly Detection for Bivariate Signals

Marie Cottrell, Cynthia Faure, Jérôme Lacaille +1

The anomaly detection problem for univariate or multivariate time series is a critical question in many practical applications as industrial processes control, biological measures,…

math.ST2020

A review on asymptotic inference in stochastic differential equations with mixed-effects

Maud Delattre

This paper is a survey of recent contributions on estimation in stochastic differential equations with mixed-effects. These models involve N stochastic differential equations with…

math.ST2020★ 1 cited

Spectral estimation of Hawkes processes from count data

Felix Cheysson, Gabriel Lang

This paper presents a parametric estimation method for ill-observed linear stationary Hawkes processes. When the exact locations of points are not observed, but only counts over ti…

math.ST2019★ 3 cited

Detecting a planted community in an inhomogeneous random graph

Kay Bogerd, Rui M. Castro, Remco van der Hofstad +1

We study the problem of detecting whether an inhomogeneous random graph contains a planted community. Specifically, we observe a single realization of a graph. Under the null hypot…

math.ST2019★ 9 cited

Consistency and Asymptotic Normality of Stochastic Block Models Estimators from Sampled Data

Mahendra Mariadassou, Timothée Tabouy

Statistical analysis of network is an active research area and the literature counts a lot of papers concerned with network models and statistical analysis of networks. However, ve…

math.ST2018

A MOM-based ensemble method for robustness, subsampling and hyperparameter tuning

Joon Kwon, Guillaume Lecué, Matthieu Lerasle

Hyperparameters tuning and model selection are important steps in machine learning. Unfortunately, classical hyperparameter calibration and model selection procedures are sensitive…

math.ST2013★ 4 cited

Estimation of the parameters of a stochastic logistic growth model

Fabien Campillo, Marc Joannides, Irène Larramendy-Valverde

We consider a stochastic logistic growth model involving both birth and death rates in the drift and diffusion coefficients for which extinction eventually occurs almost surely. Th…

math.ST2012

Non-asymptotic Oracle Inequalities for the Lasso and Group Lasso in high dimensional logistic model

Marius Kwemou

We consider the problem of estimating a function in logistic regression model. We propose to estimate this function by a sparse approximation build as a linear co…

math.ST2012★ 10 cited

Localizing the Latent Structure Canonical Uncertainty: Entropy Profiles for Hidden Markov Models

Jean-Baptiste Durand, Y. Guédon

This report addresses state inference for hidden Markov models. These models rely on unobserved states, which often have a meaningful interpretation. This makes it necessary to dev…

math.ST2011★ 8 cited

Generating French virtual commuting network at municipality level

Maxime Lenormand, Sylvie Huet, Floriana Gargiulo

We aim to generate virtual commuting networks in the French rural regions in order to study the dynamics of their municipalities. Since we have to model small commuting flows betwe…

math.ST2004★ 12 cited

Asymptotic global robustness in bayesian decision theory

Christophe Abraham, Benoit Cadre

In Bayesian decision theory, it is known that robustness with respect to the loss and the prior can be improved by adding new observations. In this article we study the rate of rob…