Search Results for author: Sébastien Lachapelle

Found 7 papers, 5 papers with code

Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA

1 code implementation21 Jul 2021 Sébastien Lachapelle, Pau Rodríguez López, Yash Sharma, Katie Everett, Rémi Le Priol, Alexandre Lacoste, Simon Lacoste-Julien

This work introduces a novel principle we call disentanglement via mechanism sparsity regularization, which can be applied when the latent factors of interest depend sparsely on past latent factors and/or observed auxiliary variables.

Disentanglement

On the Convergence of Continuous Constrained Optimization for Structure Learning

1 code implementation23 Nov 2020 Ignavier Ng, Sébastien Lachapelle, Nan Rosemary Ke, Simon Lacoste-Julien, Kun Zhang

Recently, structure learning of directed acyclic graphs (DAGs) has been formulated as a continuous optimization problem by leveraging an algebraic characterization of acyclicity.

Differentiable Causal Discovery from Interventional Data

1 code implementation NeurIPS 2020 Philippe Brouillard, Sébastien Lachapelle, Alexandre Lacoste, Simon Lacoste-Julien, Alexandre Drouin

This work constitutes a new step in this direction by proposing a theoretically-grounded method based on neural networks that can leverage interventional data.

Causal Discovery

Gradient-Based Neural DAG Learning

1 code implementation ICLR 2020 Sébastien Lachapelle, Philippe Brouillard, Tristan Deleu, Simon Lacoste-Julien

We propose a novel score-based approach to learning a directed acyclic graph (DAG) from observational data.

Causal Inference

Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information

no code implementations22 Jan 2019 Eric Larsen, Sébastien Lachapelle, Yoshua Bengio, Emma Frejinger, Simon Lacoste-Julien, Andrea Lodi

We formulate the problem as a two-stage optimal prediction stochastic program whose solution we predict with a supervised machine learning algorithm.

Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information

no code implementations31 Jul 2018 Eric Larsen, Sébastien Lachapelle, Yoshua Bengio, Emma Frejinger, Simon Lacoste-Julien, Andrea Lodi

We aim to predict at a high speed the expected TDOS associated with the second stage problem, conditionally on the first stage variables.

Stochastic Optimization

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