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Sébastien Lachapelle

2 accepted papers

2020

Differentiable Causal Discovery from Interventional Data

NeurIPS 2020spotlight

Learning a causal directed acyclic graph from data is a challenging task that involves solving a combinatorial problem for which the solution is not always identifiable. A new line of work reformulates this problem as a continuous constrained optimization one, which is solved via the augmented Lagra…

2020

Gradient-Based Neural DAG Learning

ICLR 2020poster

We propose a novel score-based approach to learning a directed acyclic graph (DAG) from observational data. We adapt a recently proposed continuous constrained optimization formulation to allow for nonlinear relationships between variables using neural networks. This extension allows to model comple…

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