Graph inference datasets. Replication Data for: "Learning Functional Causal Models with Generative Neural Networks"
By Olivier Goudet, Diviyan Kalainathan, Philippe Caillou, Isabelle M Guyon, David Lopez-Paz, Michèle Sebag. 2017
35 citations recorded by the source corpus.
Subfields: Artificial Intelligence, Graph Learning
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Artificial Intelligence
- Yoshua Bengio
- Simon Lacoste-Julien
- Bernhard Schölkopf
- Eric P. Xing
- Sergey Levine
- David Lopez-Paz
- Pradeep Ravikumar
- Percy Liang
In Graph Learning
- Simon Lacoste-Julien
- Sébastien Lachapelle
- Philippe Brouillard
- Tristan Deleu
- Christina Heinze-Deml
- David Lopez-Paz
- Eric P. Xing
- Pradeep Ravikumar
Citations in the corpus
- Gradient-Based Neural DAG Learning (2019, methodology): “CGNN (Goudet et al., 2018), when used for multivariate data, requires an initial skeleton to learn the different functional relationships.”
- Peeking Inside the Black-Box: A Survey on Explainable Artificial Intelligence (XAI) (2018, background): “as part of the loss function [77], or as part of the architecture structure, in terms of operations between layers [78], [79].”
- Learning Sparse Nonparametric DAGs (2019, methodology): “Our approach is based on a recent algebraic characterization of DAGs that led to a fully continuous program for scorebased learning of DAG models parametrized by a linear structural equation model (SEM).”
- Structural Agnostic Modeling: Adversarial Learning of Causal Graphs (2018, methodology): “CGNN starts from a given skeleton and explores the space of DAGs using a hill climbing algorithm aimed to optimize the global score of the network computed as the Maximum Mean Discrepancy (MMD) (Gretton et al., 2007) between the true empirical distribution P and the generated distribution P̂ .”
- Causal Confusion in Imitation Learning (2019, background): ““Causal discovery” approaches allow causal inference from pre-recorded observations under constraints [54, 17, 29, 15, 30, 31, 26, 14, 34, 57].”
- Guarding Against Adversarial Domain Shifts with Counterfactual Regularization (2017, background): “Goudet et al. (2017) use generative neural networks for cause-effect inference, to identify v-structures and to orient the edges of a given graph skeleton.”