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Sébastien Lachapelle

lachaseb@mila.quebec
Visiteur de recherche indépendant - Samsung - SAIT
Superviseur⋅e principal⋅e
Simon Lacoste-Julien
Sujets de recherche
Apprentissage de représentations
Apprentissage profond
Causalité
Modèles génératifs
Modèles probabilistes
Site web
Google Scholar

Publications

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 Pal
We propose to meta-learn causal structures based on how fast a learner adapts to new distributions arising from sparse distributional change… (voir plus)s, e.g. due to interventions, actions of agents and other sources of non-stationarities. We show that under this assumption, the correct causal structural choices lead to faster adaptation to modified distributions because the changes are concentrated in one or just a few mechanisms when the learned knowledge is modularized appropriately. This leads to sparse expected gradients and a lower effective number of degrees of freedom needing to be relearned while adapting to the change. It motivates using the speed of adaptation to a modified distribution as a meta-learning objective. We demonstrate how this can be used to determine the cause-effect relationship between two observed variables. The distributional changes do not need to correspond to standard interventions (clamping a variable), and the learner has no direct knowledge of these interventions. We show that causal structures can be parameterized via continuous variables and learned end-to-end. We then explore how these ideas could be used to also learn an encoder that would map low-level observed variables to unobserved causal variables leading to faster adaptation out-of-distribution, learning a representation space where one can satisfy the assumptions of independent mechanisms and of small and sparse changes in these mechanisms due to actions and non-stationarities.
2019-12-31
ICLR (publié)
doi.org
openreview.net
G RADIENT -B ASED N EURAL DAG L EARNING WITH I NTERVENTIONS
Philippe Brouillard
Alexandre Drouin
Sébastien Lachapelle
Alexandre Lacoste
Simon Lacoste-Julien
Decision making based on statistical association alone can be a dangerous endeavor due to non-causal associations. Ideally, one would rely o… (voir plus)n causal relationships that enable reasoning about the effect of interventions. Several methods have been proposed to discover such relationships from observational and inter-ventional data. Among them, GraN-DAG, a method that relies on the constrained optimization of neural networks, was shown to produce state-of-the-art results among algorithms relying purely on observational data. However, it is limited to observational data and cannot make use of interventions. In this work, we extend GraN-DAG to support interventional data and show that this improves its ability to infer causal structures
2019-12-31
(publié)
www.semanticscholar.org
Unsupervised one-to-many image translation
Samuel Lavoie-Marchildon
Sébastien Lachapelle
Mikolaj Binkowski
Aaron Courville
Yoshua Bengio
R Devon Hjelm
2018-09-26
(publié)
openreview.net
openreview.net