no code implementations • 13 Mar 2024 • Danru Xu, Dingling Yao, Sébastien Lachapelle, Perouz Taslakian, Julius von Kügelgen, Francesco Locatello, Sara Magliacane
Causal representation learning aims at identifying high-level causal variables from perceptual data.
no code implementations • 10 Jan 2024 • Sébastien Lachapelle, Pau Rodríguez López, Yash Sharma, Katie Everett, Rémi Le Priol, Alexandre Lacoste, Simon Lacoste-Julien
We develop a nonparametric identifiability theory that formalizes this principle and shows that the latent factors can be recovered by regularizing the learned causal graph to be sparse.
1 code implementation • 7 Nov 2023 • Dingling Yao, Danru Xu, Sébastien Lachapelle, Sara Magliacane, Perouz Taslakian, Georg Martius, Julius von Kügelgen, Francesco Locatello
We present a unified framework for studying the identifiability of representations learned from simultaneously observed views, such as different data modalities.
1 code implementation • 26 Nov 2022 • Sébastien Lachapelle, Tristan Deleu, Divyat Mahajan, Ioannis Mitliagkas, Yoshua Bengio, Simon Lacoste-Julien, Quentin Bertrand
Although disentangled representations are often said to be beneficial for downstream tasks, current empirical and theoretical understanding is limited.
no code implementations • 15 Jul 2022 • Sébastien Lachapelle, Simon Lacoste-Julien
In this work, we introduce a generalization of this theory which applies to any ground-truth graph and specifies qualitatively how disentangled the learned representation is expected to be, via a new equivalence relation over models we call consistency.
1 code implementation • 21 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.
1 code implementation • 23 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.
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.
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.
2 code implementations • ICLR 2020 • Yoshua Bengio, Tristan Deleu, Nasim Rahaman, Rosemary Ke, Sébastien Lachapelle, Olexa Bilaniuk, Anirudh Goyal, Christopher Pal
We show that causal structures can be parameterized via continuous variables and learned end-to-end.
no code implementations • 22 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.
no code implementations • 31 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.