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pro vyhledávání: '"Louart, P."'
Analysing Multi-Task Regression via Random Matrix Theory with Application to Time Series Forecasting
Autor:
Ilbert, Romain, Tiomoko, Malik, Louart, Cosme, Odonnat, Ambroise, Feofanov, Vasilii, Palpanas, Themis, Redko, Ievgen
In this paper, we introduce a novel theoretical framework for multi-task regression, applying random matrix theory to provide precise performance estimations, under high-dimensional, non-Gaussian data distributions. We formulate a multi-task optimiza
Externí odkaz:
http://arxiv.org/abs/2406.10327
Autor:
Louart, Cosme
Following the concentration of the measure theory formalism, we consider the transformation $\Phi(Z)$ of a random variable $Z$ having a general concentration function $\alpha$. If the transformation $\Phi$ is $\lambda$-Lipschitz with $\lambda>0$ dete
Externí odkaz:
http://arxiv.org/abs/2402.08206
Autor:
Louart, Cosme
Considering random matrix $X \in \mathcal M_{p,n}$ with independent columns satisfying the convex concentration properties issued from a famous theorem of Talagrand, we express the linear concentration of the resolvent $Q = (I_p - \frac{1}{n}XX^T) ^{
Externí odkaz:
http://arxiv.org/abs/2201.00284
Autor:
Louart, Cosme, Couillet, Romain
Given a random matrix $X= (x_1,\ldots, x_n)\in \mathcal M_{p,n}$ with independent columns and satisfying concentration of measure hypotheses and a parameter $z$ whose distance to the spectrum of $\frac{1}{n} XX^T$ should not depend on $p,n$, it was p
Externí odkaz:
http://arxiv.org/abs/2109.02644
Publikováno v:
BMC Health Services Research, Vol 23, Iss 1, Pp 1-15 (2023)
Abstract Acceptability is a key concept used to analyze the introduction of a health innovation in a specific setting. However, there seems to be a lack of clarity in this notion, both conceptually and practically. In low and middle-income countries,
Externí odkaz:
https://doaj.org/article/23caf40ea7254f38a49f9f202470e695
Autor:
Louart, Cosme, Couillet, Romain
Starting from concentration of measure hypotheses on $m$ random vectors $Z_1,\ldots, Z_m$, this article provides an expression of the concentration of functionals $\phi(Z_1,\ldots, Z_m)$ where the variations of $\phi$ on each variable depend on the p
Externí odkaz:
http://arxiv.org/abs/2102.08020
Autor:
Louart, Cosme
This paper provides a framework to show the concentration of solutions $Y^*$ to convex minimizing problem where the objective function $\phi(X)(Y)$ depends on some random vector $X$ satisfying concentration of measure hypotheses. More precisely, the
Externí odkaz:
http://arxiv.org/abs/2010.09877
Autor:
Louart, Cosme, Couillet, Romain
This article studies the \emph{robust covariance matrix estimation} of a data collection $X = (x_1,\ldots,x_n)$ with $x_i = \sqrt \tau_i z_i + m$, where $z_i \in \mathbb R^p$ is a \textit{concentrated vector} (e.g., an elliptical random vector), $m\i
Externí odkaz:
http://arxiv.org/abs/2006.09728
This paper shows that deep learning (DL) representations of data produced by generative adversarial nets (GANs) are random vectors which fall within the class of so-called \textit{concentrated} random vectors. Further exploiting the fact that Gram ma
Externí odkaz:
http://arxiv.org/abs/2001.08370
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