Sébastien Lachapelle

Publication Activity (10 Years)

Years Active: 2018-2024
Publications (10 Years): 24

Top Topics

Structural Svms

Imperfect Information

Causal Discovery

Conditionally Independent

Top Venues

Publications

Dingling Yao, Danru Xu, Sébastien Lachapelle, Sara Magliacane, Perouz Taslakian, Georg Martius, Julius von Kügelgen, Francesco Locatello
Multi-View Causal Representation Learning with Partial Observability. ICLR (2024)
Elliot Layne, Jason S. Hartford, Sébastien Lachapelle, Mathieu Blanchette, Dhanya Sridhar
Leveraging Structure Between Environments: Phylogenetic Regularization Incentivizes Disentangled Representations. CoRR (2024)
Sébastien Lachapelle, Pau Rodríguez López, Yash Sharma, Katie Everett, Rémi Le Priol, Alexandre Lacoste, Simon Lacoste-Julien
Nonparametric Partial Disentanglement via Mechanism Sparsity: Sparse Actions, Interventions and Sparse Temporal Dependencies. CoRR (2024)
Danru Xu, Dingling Yao, Sébastien Lachapelle, Perouz Taslakian, Julius von Kügelgen, Francesco Locatello, Sara Magliacane
A Sparsity Principle for Partially Observable Causal Representation Learning. CoRR (2024)
Dingling Yao, Danru Xu, Sébastien Lachapelle, Sara Magliacane, Perouz Taslakian, Georg Martius, Julius von Kügelgen, Francesco Locatello
Multi-View Causal Representation Learning with Partial Observability. CoRR (2023)
Sébastien Lachapelle, Divyat Mahajan, Ioannis Mitliagkas, Simon Lacoste-Julien
Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation. CoRR (2023)
Sébastien Lachapelle, Divyat Mahajan, Ioannis Mitliagkas, Simon Lacoste-Julien
Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation. NeurIPS (2023)
Sébastien Lachapelle, Tristan Deleu, Divyat Mahajan, Ioannis Mitliagkas, Yoshua Bengio, Simon Lacoste-Julien, Quentin Bertrand
Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning. ICML (2023)
Sébastien Lachapelle, Simon Lacoste-Julien
Partial Disentanglement via Mechanism Sparsity. CoRR (2022)
Sébastien Lachapelle, Pau Rodríguez, Yash Sharma, Katie Everett, Rémi Le Priol, Alexandre Lacoste, Simon Lacoste-Julien
Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA. CLeaR (2022)
Eric Larsen, Sébastien Lachapelle, Yoshua Bengio, Emma Frejinger, Simon Lacoste-Julien, Andrea Lodi
Predicting Tactical Solutions to Operational Planning Problems Under Imperfect Information. INFORMS J. Comput. 34 (1) (2022)
Philippe Brouillard, Perouz Taslakian, Alexandre Lacoste, Sébastien Lachapelle, Alexandre Drouin
Typing assumptions improve identification in causal discovery. CLeaR (2022)
Sébastien Lachapelle, Tristan Deleu, Divyat Mahajan, Ioannis Mitliagkas, Yoshua Bengio, Simon Lacoste-Julien, Quentin Bertrand
Synergies Between Disentanglement and Sparsity: a Multi-Task Learning Perspective. CoRR (2022)
Ignavier Ng, Sébastien Lachapelle, Nan Rosemary Ke, Simon Lacoste-Julien, Kun Zhang
On the Convergence of Continuous Constrained Optimization for Structure Learning. AISTATS (2022)
Philippe Brouillard, Perouz Taslakian, Alexandre Lacoste, Sébastien Lachapelle, Alexandre Drouin
Typing assumptions improve identification in causal discovery. CoRR (2021)
Sébastien Lachapelle, Pau Rodríguez López, Rémi Le Priol, Alexandre Lacoste, Simon Lacoste-Julien
Discovering Latent Causal Variables via Mechanism Sparsity: A New Principle for Nonlinear ICA. CoRR (2021)
Philippe Brouillard, Sébastien Lachapelle, Alexandre Lacoste, Simon Lacoste-Julien, Alexandre Drouin
Differentiable Causal Discovery from Interventional Data. NeurIPS (2020)
Sébastien Lachapelle, Philippe Brouillard, Tristan Deleu, Simon Lacoste-Julien
Gradient-Based Neural DAG Learning. ICLR (2020)
Philippe Brouillard, Sébastien Lachapelle, Alexandre Lacoste, Simon Lacoste-Julien, Alexandre Drouin
Differentiable Causal Discovery from Interventional Data. CoRR (2020)
Ignavier Ng, Sébastien Lachapelle, Nan Rosemary Ke, Simon Lacoste-Julien
On the Convergence of Continuous Constrained Optimization for Structure Learning. CoRR (2020)
Yoshua Bengio, Tristan Deleu, Nasim Rahaman, Nan Rosemary Ke, Sébastien Lachapelle, Olexa Bilaniuk, Anirudh Goyal, Christopher J. Pal
A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms. ICLR (2020)
Sébastien Lachapelle, Philippe Brouillard, Tristan Deleu, Simon Lacoste-Julien
Gradient-Based Neural DAG Learning. CoRR (2019)
Yoshua Bengio, Tristan Deleu, Nasim Rahaman, Nan Rosemary Ke, Sébastien Lachapelle, Olexa Bilaniuk, Anirudh Goyal, Christopher J. Pal
A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms. CoRR (2019)
Eric Larsen, Sébastien Lachapelle, Yoshua Bengio, Emma Frejinger, Simon Lacoste-Julien, Andrea Lodi
Predicting Solution Summaries to Integer Linear Programs under Imperfect Information with Machine Learning. CoRR (2018)