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03/03/2023
Asymptotic Bayes risk of semi-supervised multitask learning on Gaussian mixture
The article considers semi-supervised multitask learning on a Gaussian m...
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12/23/2021
When Random Tensors meet Random Matrices
Relying on random matrix theory (RMT), this paper studies asymmetric ord...
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11/01/2021
PCA-based Multi Task Learning: a Random Matrix Approach
The article proposes and theoretically analyses a computationally effici...
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10/09/2021
Multi-task learning on the edge: cost-efficiency and theoretical optimality
This article proposes a distributed multi-task learning (MTL) algorithm ...
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10/05/2021
Random matrices in service of ML footprint: ternary random features with no performance loss
In this article, we investigate the spectral behavior of random features...
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09/06/2021
Spectral properties of sample covariance matrices arising from random matrices with independent non identically distributed columns
Given a random matrix X= (x_1,…, x_n)∈ℳ_p,n with independent columns and...
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08/02/2021
A Random Matrix Perspective on Random Tensors
Tensor models play an increasingly prominent role in many fields, notabl...
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03/05/2021
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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02/24/2021
Two-way kernel matrix puncturing: towards resource-efficient PCA and spectral clustering
The article introduces an elementary cost and storage reduction method f...
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02/16/2021
Concentration of measure and generalized product of random vectors with an application to Hanson-Wright-like inequalities
Starting from concentration of measure hypotheses on m random vectors Z_...
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10/19/2020
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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10/03/2020
Sparse Quantized Spectral Clustering
Given a large data matrix, sparsifying, quantizing, and/or performing ot...
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09/03/2020
Large Dimensional Analysis and Improvement of Multi Task Learning
Multi Task Learning (MTL) efficiently leverages useful information conta...
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06/17/2020
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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06/13/2020
Consistent Semi-Supervised Graph Regularization for High Dimensional Data
Semi-supervised Laplacian regularization, a standard graph-based approac...
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06/09/2020
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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06/03/2020
Community detection in sparse time-evolving graphs with a dynamical Bethe-Hessian
This article considers the problem of community detection in sparse dyna...
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03/20/2020
A unified framework for spectral clustering in sparse graphs
This article considers spectral community detection in the regime of spa...
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01/21/2020
Random Matrix Theory Proves that Deep Learning Representations of GAN-data Behave as Gaussian Mixtures
This paper shows that deep learning (DL) representations of data produce...
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12/03/2019
Optimal Laplacian regularization for sparse spectral community detection
Regularization of the classical Laplacian matrices was empirically shown...
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09/15/2019
Inner-product Kernels are Asymptotically Equivalent to Binary Discrete Kernels
This article investigates the eigenspectrum of the inner product-type ke...
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03/08/2019
Random Matrix-Improved 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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02/07/2019
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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01/25/2019
Optimized Deformed Laplacian for Spectrum-based Community Detection in Sparse Heterogeneous Graphs
Spectral clustering is one of the most popular, yet still incompletely u...
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11/08/2018
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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10/10/2018
Random matrix-improved 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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06/16/2018
Latent heterogeneous multilayer community detection
We propose a method for simultaneously detecting shared and unshared com...
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05/30/2018
The Dynamics of Learning: A Random Matrix Approach
Understanding the learning dynamics of neural networks is one of the key...
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05/30/2018
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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04/26/2018
Large-dimensional behavior of regularized Maronna's M-estimators of covariance matrices
Robust estimators of large covariance matrices are considered, comprisin...
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11/23/2017
Gallager Bound for MIMO Channels: Large-N Asymptotics
The use of multiple antenna arrays in transmission and reception has bec...
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11/09/2017
A random matrix analysis and improvement of semi-supervised learning for large dimensional data
This article provides an original understanding of the behavior of a cla...
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11/01/2017
A Large Dimensional Analysis of Regularized Discriminant Analysis Classifiers
This article carries out a large dimensional analysis of standard regula...
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01/11/2017
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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11/03/2016
Spectral community detection in heterogeneous large networks
In this article, we study spectral methods for community detection based...
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09/07/2016
Random matrices meet machine learning: a large dimensional analysis of LS-SVM
This article proposes a performance analysis of kernel least squares sup...
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03/25/2016
The Asymptotic Performance of Linear Echo State Neural Networks
In this article, a study of the mean-square error (MSE) performance of l...
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03/04/2015