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2310.13434
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Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption
20 October 2023
Vasilii Feofanov
Malik Tiomoko
Aladin Virmaux
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Papers citing
"Random Matrix Analysis to Balance between Supervised and Unsupervised Learning under the Low Density Separation Assumption"
8 / 8 papers shown
Title
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
63
1
0
14 Jun 2024
MANO: Exploiting Matrix Norm for Unsupervised Accuracy Estimation Under Distribution Shifts
Renchunzi Xie
Ambroise Odonnat
Vasilii Feofanov
Weijian Deng
Jianfeng Zhang
Bo An
37
2
0
29 May 2024
"Lossless" Compression of Deep Neural Networks: A High-dimensional Neural Tangent Kernel Approach
Lingyu Gu
Yongqiang Du
Yuan Zhang
Di Xie
Shiliang Pu
Robert C. Qiu
Zhenyu Liao
28
6
0
01 Mar 2024
Leveraging Ensemble Diversity for Robust Self-Training in the Presence of Sample Selection Bias
Ambroise Odonnat
Vasilii Feofanov
I. Redko
19
5
0
23 Oct 2023
Random matrices in service of ML footprint: ternary random features with no performance loss
Hafiz Tiomoko Ali
Zhenyu Liao
Romain Couillet
36
7
0
05 Oct 2021
Multi-class Probabilistic Bounds for Self-learning
Vasilii Feofanov
Emilie Devijver
Massih-Reza Amini
17
3
0
29 Sep 2021
Graph-based Semi-supervised Learning: A Comprehensive Review
Zixing Song
Xiangli Yang
Zenglin Xu
Irwin King
79
190
0
26 Feb 2021
Double Trouble in Double Descent : Bias and Variance(s) in the Lazy Regime
Stéphane dÁscoli
Maria Refinetti
Giulio Biroli
Florent Krzakala
83
152
0
02 Mar 2020
1