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PCA-based Multi Task Learning: a Random Matrix Approach

PCA-based Multi Task Learning: a Random Matrix Approach

1 November 2021
Malik Tiomoko
Romain Couillet
Frédéric Pascal
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Papers citing "PCA-based Multi Task Learning: a Random Matrix Approach"

4 / 4 papers shown
Title
Analysing Multi-Task Regression via Random Matrix Theory with
  Application to Time Series Forecasting
Analysing Multi-Task Regression via Random Matrix Theory with Application to Time Series Forecasting
Romain Ilbert
Malik Tiomoko
Cosme Louart
Ambroise Odonnat
Vasilii Feofanov
Themis Palpanas
I. Redko
AI4TS
69
1
0
14 Jun 2024
Random Matrix Analysis to Balance between Supervised and Unsupervised
  Learning under the Low Density Separation Assumption
Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption
Vasilii Feofanov
Malik Tiomoko
Aladin Virmaux
38
5
0
20 Oct 2023
Asymptotic Bayes risk of semi-supervised multitask learning on Gaussian
  mixture
Asymptotic Bayes risk of semi-supervised multitask learning on Gaussian mixture
Minh-Toan Nguyen
Romain Couillet
30
4
0
03 Mar 2023
Multi-task learning on the edge: cost-efficiency and theoretical
  optimality
Multi-task learning on the edge: cost-efficiency and theoretical optimality
Sami Fakhry
Romain Couillet
Malik Tiomoko
17
1
0
09 Oct 2021
1