Sébastien Lachapelle

According to our database, Sébastien Lachapelle authored at least 20 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of three.

2024

All or None: Identifiable Linear Properties of Next-token Predictors in Language Modeling.

Emanuele Marconato, Sébastien Lachapelle, Sebastian Weichwald, Luigi Gresele

CoRR, 2024

Causal Representation Learning in Temporal Data via Single-Parent Decoding.

Philippe Brouillard, Sébastien Lachapelle, Julia Kaltenborn, Yaniv Gurwicz, Dhanya Sridhar, Alexandre Drouin, Peer Nowack, Jakob Runge, David Rolnick

CoRR, 2024

Leveraging Structure Between Environments: Phylogenetic Regularization Incentivizes Disentangled Representations.

Elliot Layne, Jason S. Hartford, Sébastien Lachapelle, Mathieu Blanchette, Dhanya Sridhar

CoRR, 2024

Nonparametric Partial Disentanglement via Mechanism Sparsity: Sparse Actions, Interventions and Sparse Temporal Dependencies.

Sébastien Lachapelle, Pau Rodríguez López, Yash Sharma, Katie Everett, Rémi Le Priol, Alexandre Lacoste, Simon Lacoste-Julien

CoRR, 2024

A Sparsity Principle for Partially Observable Causal Representation Learning.

Danru Xu, Dingling Yao, Sébastien Lachapelle, Perouz Taslakian, Julius von Kügelgen, Francesco Locatello, Sara Magliacane

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Multi-View Causal Representation Learning with Partial Observability.

Dingling Yao, Danru Xu, Sébastien Lachapelle, Sara Magliacane, Perouz Taslakian, Georg Martius, Julius von Kügelgen, Francesco Locatello

Proceedings of the Twelfth International Conference on Learning Representations, 2024

2023

Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation.

Sébastien Lachapelle, Divyat Mahajan, Ioannis Mitliagkas, Simon Lacoste-Julien

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning.

Sébastien Lachapelle, Tristan Deleu, Divyat Mahajan, Ioannis Mitliagkas, Yoshua Bengio, Simon Lacoste-Julien, Quentin Bertrand

Proceedings of the International Conference on Machine Learning, 2023

2022

Predicting Tactical Solutions to Operational Planning Problems Under Imperfect Information.

Eric Larsen, Sébastien Lachapelle, Yoshua Bengio, Emma Frejinger, Simon Lacoste-Julien, Andrea Lodi

INFORMS J. Comput., 2022

Synergies Between Disentanglement and Sparsity: a Multi-Task Learning Perspective.

Sébastien Lachapelle, Tristan Deleu, Divyat Mahajan, Ioannis Mitliagkas, Yoshua Bengio, Simon Lacoste-Julien, Quentin Bertrand

CoRR, 2022

Partial Disentanglement via Mechanism Sparsity.

Sébastien Lachapelle, Simon Lacoste-Julien

CoRR, 2022

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

Sébastien Lachapelle, Pau Rodríguez, Yash Sharma, Katie Everett, Rémi Le Priol, Alexandre Lacoste, Simon Lacoste-Julien

Proceedings of the 1st Conference on Causal Learning and Reasoning, 2022

Typing assumptions improve identification in causal discovery.

Philippe Brouillard, Perouz Taslakian, Alexandre Lacoste, Sébastien Lachapelle, Alexandre Drouin

Proceedings of the 1st Conference on Causal Learning and Reasoning, 2022

On the Convergence of Continuous Constrained Optimization for Structure Learning.

Ignavier Ng, Sébastien Lachapelle, Nan Rosemary Ke, Simon Lacoste-Julien, Kun Zhang

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2022

2021

Discovering Latent Causal Variables via Mechanism Sparsity: A New Principle for Nonlinear ICA.

Sébastien Lachapelle, Pau Rodríguez López, Rémi Le Priol, Alexandre Lacoste, Simon Lacoste-Julien

CoRR, 2021

2020

On the Convergence of Continuous Constrained Optimization for Structure Learning.

Ignavier Ng, Sébastien Lachapelle, Nan Rosemary Ke, Simon Lacoste-Julien

CoRR, 2020

Differentiable Causal Discovery from Interventional Data.

Philippe Brouillard, Sébastien Lachapelle, Alexandre Lacoste, Simon Lacoste-Julien, Alexandre Drouin

Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020

Gradient-Based Neural DAG Learning.

Sébastien Lachapelle, Philippe Brouillard, Tristan Deleu, Simon Lacoste-Julien

Proceedings of the 8th International Conference on Learning Representations, 2020

A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms.

Yoshua Bengio, Tristan Deleu, Nasim Rahaman, Nan Rosemary Ke, Sébastien Lachapelle, Olexa Bilaniuk, Anirudh Goyal, Christopher J. Pal

Proceedings of the 8th International Conference on Learning Representations, 2020

2018

Predicting Solution Summaries to Integer Linear Programs under Imperfect Information with Machine Learning.

Eric Larsen, Sébastien Lachapelle, Yoshua Bengio, Emma Frejinger, Simon Lacoste-Julien, Andrea Lodi

CoRR, 2018