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Philippe Brouillard

3 accepted papers

2023

ClimateSet: A Large-Scale Climate Model Dataset for Machine Learning

NeurIPS 2023poster

Climate models have been key for assessing the impact of climate change and simulating future climate scenarios. The machine learning (ML) community has taken an increased interest in supporting climate scientists’ efforts on various tasks such as climate model emulation, downscaling, and prediction…

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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