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NGO-GM: Natural Gradient Optimization for Graphical Models

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Date
2020
Publisher city
Paris
Collection title
Preprint Lamsade
Link to item file
https://hal.archives-ouvertes.fr/hal-02886514
Dewey
Intelligence artificielle
Sujet
Optimization
URI
https://basepub.dauphine.fr/handle/123456789/21206
Collections
  • LAMSADE : Publications
Metadata
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Author
Benhamou, Éric
Atif, Jamal
Laraki, Rida
Saltiel, David
Type
Document de travail / Working paper
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.

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