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A Random Block-Coordinate Douglas-Rachford Splitting Method with Low
  Computational Complexity for Binary Logistic Regression

A Random Block-Coordinate Douglas-Rachford Splitting Method with Low Computational Complexity for Binary Logistic Regression

25 December 2017
L. Briceño-Arias
Giovanni Chierchia
Émilie Chouzenoux
J. Pesquet
ArXiv (abs)PDFHTML

Papers citing "A Random Block-Coordinate Douglas-Rachford Splitting Method with Low Computational Complexity for Binary Logistic Regression"

3 / 3 papers shown
Title
A High Dimensional Statistical Model for Adversarial Training: Geometry and Trade-Offs
A High Dimensional Statistical Model for Adversarial Training: Geometry and Trade-Offs
Kasimir Tanner
Matteo Vilucchio
Bruno Loureiro
Florent Krzakala
AAML
90
1
0
31 Dec 2024
ConFuse: Convolutional Transform Learning Fusion Framework For
  Multi-Channel Data Analysis
ConFuse: Convolutional Transform Learning Fusion Framework For Multi-Channel Data Analysis
Pooja Gupta
Jyoti Maggu
A. Majumdar
Émilie Chouzenoux
Giovanni Chierchia
AI4TS
122
2
0
09 Nov 2020
General risk measures for robust machine learning
General risk measures for robust machine learning
Émilie Chouzenoux
Henri Gérard
J. Pesquet
OOD
40
7
0
26 Apr 2019
1