Marc Schoenauer

Centre National de la Recherche Scientifique

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Co-Authors

Michèle Sébag

Université Paris-Saclay

147

ÉL

Évelyne Lutton

Laboratoire Matière et Systèmes Complexes

Pierre Savéant

Thales (France)

Pierre Collet

École Polytechnique

Ilya Loshchilov

École Polytechnique Fédérale de Lausanne

Olivier Teytaud

Université Paris-Saclay

Nicolas Monmarché

Laboratoire d'Informatique Fondamentale et Appliquée de Tours

Nicolas Bredèche

Sorbonne Université

Vincent Vidal

Assistance Publique Hôpitaux de Marseille

Jean‐Marc Alliot

Institut de Recherche en Informatique de Toulouse

Cooperative Institutions

Centre National de la Recherche Scientifique

214

Institut national de recherche en informatique et en automatique

143

Université Paris-Sud

Laboratoire de Recherche en Informatique

Université Paris-Saclay

Université Paris Cité

École Polytechnique

Sorbonne Université

Laboratoire Matière et Systèmes Complexes

Inria Saclay - Île de France

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Learning Structural Causal Models through Deep Generative Models: Methods, Guarantees, and Challenges

Audrey Poinsot Alessandro Leite Nicolas Chesneau Michèle Sébag Marc Schoenauer

This paper provides a comprehensive review of deep structural causal models (DSCMs), particularly focusing on their ability to answer counterfactual queries using observational data within known causal structures. It delves into the characteristics of DSCMs by analyzing the hypotheses, guarantees, and applications inherent to the underlying deep learning components and structural causal models, fostering a finer understanding of their capabilities and limitations in addressing different counterfactual queries. Furthermore, it highlights the challenges and open questions in the field of deep structural causal modeling. It sets the stages for researchers to identify future work directions and for practitioners to get an overview in order to find out the most appropriate methods for their needs.

Causal model

10.24963/ijcai.2024/907

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Incremental Learning of Rules and Meta–rules

Elsevier eBooks (1990)

Marc Schoenauer Michèle Sébag

Operator (biology)

10.1016/b978-1-55860-141-3.50010-9

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Citations (17)

Artificial gene regulatory networks and spatial computation: A case study

Sylvain Cussat‐Blanc Nicolas Bredèche Hervé Luga Yves Duthen Marc Schoenauer

This paper explores temporal and spatial dynamics of a population of Genetic Regulatory Networks (GRN). In order to so, a GRN model is spatially distributed to solve a multi-cellular Artiﬁcial Embryogeny problem, and Evolutionary Computation is used to optimize the developmental sequences. An in-depth analysis is provided and show that such a population of GRN display strong spatial synchronization as well as various kind of behavioral patterns, ranging from smooth diffusion to abrupt transition patterns.

Gene regulatory network

10.7551/978-0-262-29714-1-ch032

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Citations (17)

Contrôle et induction.

HAL (Le Centre pour la Communication Scientifique Directe) (1996)

Caroline Ravisé Michèle Sébag Marc Schoenauer

Source

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Contrôle et induction

HAL (Le Centre pour la Communication Scientifique Directe) (1996)