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NGO-GM: Natural Gradient Optimization for Graphical Models

Benhamou, Éric; Atif, Jamal; Laraki, Rida; Saltiel, David (2020), NGO-GM: Natural Gradient Optimization for Graphical Models. https://basepub.dauphine.fr/handle/123456789/21206

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Type
Document de travail / Working paper
External document link
https://hal.archives-ouvertes.fr/hal-02886514
Date
2020
Series title
Preprint Lamsade
Published in
Paris
Metadata
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Author(s)
Benhamou, Éric
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Atif, Jamal
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Laraki, Rida cc
Laboratoire d'analyse et modélisation de systèmes pour l'aide à la décision [LAMSADE]
Saltiel, David
Abstract (EN)
This paper deals with estimating model parameters in graphical models. We reformulate it as an information geometric optimization problem and introduce a natural gradient descent strategy that incorporates additional meta parameters. We show that our approach is a strong alternative to the celebrated EM approach for learning in graphical models. Actually, our natural gradient based strategy leads to learning optimal parameters for the final objective function without artificially trying to fit a distribution that may not correspond to the real one. We support our theoretical findings with the question of trend detection in financial markets and show that the learned model performs better than traditional practitioner methods and is less prone to overfitting.
Subjects / Keywords
Optimization

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