
Nishimori meets Bethe: a spectral method for node classification in sparse weighted graphs
This article unveils a new relation between the Nishimori temperature pa...
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Twoway kernel matrix puncturing: towards resourceefficient PCA and spectral clustering
The article introduces an elementary cost and storage reduction method f...
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Concentration of measure and generalized product of random vectors with an application to HansonWrightlike inequalities
Starting from concentration of measure hypotheses on m random vectors Z_...
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Concentration of solutions to random equations with concentration of measure hypotheses
We propose here to study the concentration of random objects that are im...
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Sparse Quantized Spectral Clustering
Given a large data matrix, sparsifying, quantizing, and/or performing ot...
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Large Dimensional Analysis and Improvement of Multi Task Learning
Multi Task Learning (MTL) efficiently leverages useful information conta...
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A Concentration of Measure and Random Matrix Approach to Large Dimensional Robust Statistics
This article studies the robust covariance matrix estimation of a data c...
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Consistent SemiSupervised Graph Regularization for High Dimensional Data
Semisupervised Laplacian regularization, a standard graphbased approac...
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A random matrix analysis of random Fourier features: beyond the Gaussian kernel, a precise phase transition, and the corresponding double descent
This article characterizes the exact asymptotics of random Fourier featu...
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Community detection in sparse timeevolving graphs with a dynamical BetheHessian
This article considers the problem of community detection in sparse dyna...
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A unified framework for spectral clustering in sparse graphs
This article considers spectral community detection in the regime of spa...
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Random Matrix Theory Proves that Deep Learning Representations of GANdata Behave as Gaussian Mixtures
This paper shows that deep learning (DL) representations of data produce...
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Optimal Laplacian regularization for sparse spectral community detection
Regularization of the classical Laplacian matrices was empirically shown...
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Innerproduct Kernels are Asymptotically Equivalent to Binary Discrete Kernels
This article investigates the eigenspectrum of the inner producttype ke...
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Random MatrixImproved Estimation of the Wasserstein Distance between two Centered Gaussian Distributions
This article proposes a method to consistently estimate functionals 1/p∑...
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Random Matrix Improved Covariance Estimation for a Large Class of Metrics
Relying on recent advances in statistical estimation of covariance dista...
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Optimized Deformed Laplacian for Spectrumbased Community Detection in Sparse Heterogeneous Graphs
Spectral clustering is one of the most popular, yet still incompletely u...
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A Geometric Approach of Gradient Descent Algorithms in Neural Networks
In this article we present a geometric framework to analyze convergence ...
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Random matriximproved estimation of covariance matrix distances
Given two sets x_1^(1),...,x_n_1^(1) and x_1^(2),...,x_n_2^(2)∈R^p (or C...
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Latent heterogeneous multilayer community detection
We propose a method for simultaneously detecting shared and unshared com...
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The Dynamics of Learning: A Random Matrix Approach
Understanding the learning dynamics of neural networks is one of the key...
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On the Spectrum of Random Features Maps of High Dimensional Data
Random feature maps are ubiquitous in modern statistical machine learnin...
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Largedimensional behavior of regularized Maronna's Mestimators of covariance matrices
Robust estimators of large covariance matrices are considered, comprisin...
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Gallager Bound for MIMO Channels: LargeN Asymptotics
The use of multiple antenna arrays in transmission and reception has bec...
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A random matrix analysis and improvement of semisupervised learning for large dimensional data
This article provides an original understanding of the behavior of a cla...
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A Large Dimensional Analysis of Regularized Discriminant Analysis Classifiers
This article carries out a large dimensional analysis of standard regula...
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A Large Dimensional Analysis of Least Squares Support Vector Machines
In this article, a large dimensional performance analysis of kernel leas...
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Spectral community detection in heterogeneous large networks
In this article, we study spectral methods for community detection based...
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Random matrices meet machine learning: a large dimensional analysis of LSSVM
This article proposes a performance analysis of kernel least squares sup...
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The Asymptotic Performance of Linear Echo State Neural Networks
In this article, a study of the meansquare error (MSE) performance of l...
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Large Dimensional Analysis of Robust MEstimators of Covariance with Outliers
A large dimensional characterization of robust Mestimators of covarianc...
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Romain Couillet
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