125 citations · 166 across the 17 of their papers we have counts for
25 papers
Node-Private Community Detection in Stochastic Block Models
Olga Klopp, Ilias Zadik
We study community detection in stochastic block models under pure node-level differential privacy, a stringent notion that protects the participation of an individual together wit…
Change point detection in low-rank VAR processes
Farida Enikeeva, Olga Klopp, Mathilde Rousselot
Vector autoregressive (VAR) models are widely used in multivariate time series analysis for describing the short-time dynamics of the data. The reduced-rank VAR models are of parti…
Optimality of variational inference for stochastic block model with missing links
Solenne Gaucher, Olga Klopp
Variational methods are extremely popular in the analysis of network data. Statistical guarantees obtained for these methods typically provide asymptotic normality for the problem…
Assigning Topics to Documents by Successive Projections
Olga Klopp, Maxim Panov, Suzanne Sigalla +1
Topic models provide a useful tool to organize and understand the structure of large corpora of text documents, in particular, to discover hidden thematic structure. Clustering doc…
Change-Point Detection in Dynamic Networks with Missing Links
Farida Enikeeva, Olga Klopp
Structural changes occur in dynamic networks quite frequently and its detection is an important question in many situations such as fraud detection or cybersecurity. Real-life netw…
Outliers Detection in Networks with Missing Links
Solenne Gaucher, Olga Klopp, Geneviève Robin
Outliers arise in networks due to different reasons such as fraudulent behavior of malicious users or default in measurement instruments and can significantly impair network analys…
Maximum Likelihood Estimation of Sparse Networks with Missing Observations
Solenne Gaucher, Olga Klopp
Estimating the matrix of connections probabilities is one of the key questions when studying sparse networks. In this work, we consider networks generated under the sparse graphon…
Low-rank Interaction with Sparse Additive Effects Model for Large Data Frames
Geneviève Robin, Hoi-To Wai, Julie Josse +2
Many applications of machine learning involve the analysis of large data frames-matrices collecting heterogeneous measurements (binary, numerical, counts, etc.) across samples-with…
Collective Matrix Completion
Mokhtar Z. Alaya, Olga Klopp
Matrix completion aims to reconstruct a data matrix based on observations of a small number of its entries. Usually in matrix completion a single matrix is considered, which can be…
Main effects and interactions in mixed and incomplete data frames
Geneviève Robin, Olga Klopp, Julie Josse +2
A mixed data frame (MDF) is a table collecting categorical, numerical and count observations. The use of MDF is widespread in statistics and the applications are numerous from abun…
Structured Matrix Estimation and Completion
Olga Klopp, Yu Lu, Alexandre B. Tsybakov +1
We study the problem of matrix estimation and matrix completion under a general framework. This framework includes several important models as special cases such as the gaussian mi…
Constructing confidence sets for the matrix completion problem
Alexandra Carpentier, Olga Klopp, Matthias Löffler
In the present note we consider the problem of constructing honest and adaptive confidence sets for the matrix completion problem. For the Bernoulli model with known variance of th…
Optimal graphon estimation in cut distance
Olga Klopp, Nicolas Verzelen
Consider the twin problems of estimating the connection probability matrix of an inhomogeneous random graph and the graphon of a W-random graph. We establish the minimax estimation…
Adaptive confidence sets for matrix completion
Alexandra Carpentier, Olga Klopp, Matthias Löffler +1
In the present paper we study the problem of existence of honest and adaptive confidence sets for matrix completion. We consider two statistical models: the trace regression model…
Estimation of matrices with row sparsity
O. Klopp, A. B. Tsybakov
An increasing number of applications is concerned with recovering a sparse matrix from noisy observations. In this paper, we consider the setting where each row of the unknown matr…
Oracle inequalities for network models and sparse graphon estimation
Olga Klopp, Alexandre B. Tsybakov, Nicolas Verzelen
Inhomogeneous random graph models encompass many network models such as stochastic block models and latent position models. We consider the problem of statistical estimation of the…
Matrix completion by singular value thresholding: sharp bounds
Olga Klopp
We consider the matrix completion problem where the aim is to esti-mate a large data matrix for which only a relatively small random subset of its entries is observed. Quite popula…
Probabilistic low-rank matrix completion on finite alphabets
Jean Lafond, Olga Klopp, Eric Moulines +1
The task of reconstructing a matrix given a sample of observedentries is known as the matrix completion problem. It arises ina wide range of problems, including recommender systems…
Robust Matrix Completion
Olga Klopp, Karim Lounici, Alexandre B. Tsybakov
This paper considers the problem of recovery of a low-rank matrix in the situation when most of its entries are not observed and a fraction of observed entries are corrupted. The o…
Adaptive Multinomial Matrix Completion
Olga Klopp, Jean Lafond, Eric Moulines +1
The task of estimating a matrix given a sample of observed entries is known as the \emph{matrix completion problem}. Most works on matrix completion have focused on recovering an u…
Sparse high-dimensional varying coefficient model: non-asymptotic minimax study
Olga Klopp, Marianna Pensky
The objective of the present paper is to develop a minimax theory for the varying coefficient model in a non-asymptotic setting. We consider a high-dimensional sparse varying coeff…
Non-asymptotic approach to varying coefficient model
Olga Klopp, Marianna Pensky
In the present paper we consider the varying coefficient model which represents a useful tool for exploring dynamic patterns in many applications. Existing methods typically provid…
Noisy low-rank matrix completion with general sampling distribution
Olga Klopp
In the present paper, we consider the problem of matrix completion with noise. Unlike previous works, we consider quite general sampling distribution and we do not need to know or…
High dimensional matrix estimation with unknown variance of the noise
Olga Klopp, Stéphane Gaiffas
We propose a new pivotal method for estimating high-dimensional matrices. Assume that we observe a small set of entries or linear combinations of entries of an unknown matrix $A\_0…