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 001 (CoRR, 2018)

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 (ICLR, 2020)

Gradient-Based Neural DAG Learning. - Sébastien Lachapelle; Philippe Brouillard; Tristan Deleu; Simon Lacoste-Julien (ICLR, 2020)

Differentiable Causal Discovery from Interventional Data. - Philippe Brouillard; Sébastien Lachapelle; Alexandre Lacoste; Simon Lacoste-Julien; Alexandre Drouin (NeurIPS, 2020)

On the Convergence of Continuous Constrained Optimization for Structure Learning. - Ignavier Ng; Sébastien Lachapelle; Nan Rosemary Ke; Simon Lacoste-Julien (CoRR, 2020)

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)

On the Convergence of Continuous Constrained Optimization for Structure Learning. - Ignavier Ng; Sébastien Lachapelle; Nan Rosemary Ke; Simon Lacoste-Julien; Kun Zhang 001 (AISTATS, 2022)

Typing assumptions improve identification in causal discovery. - Philippe Brouillard; Perouz Taslakian; Alexandre Lacoste; Sébastien Lachapelle; Alexandre Drouin (CLeaR, 2022)

Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA. - Sébastien Lachapelle; Pau Rodríguez; Yash Sharma 001; Katie Everett; Rémi Le Priol; Alexandre Lacoste; Simon Lacoste-Julien (CLeaR, 2022)

Partial Disentanglement via Mechanism Sparsity. - Sébastien Lachapelle; Simon Lacoste-Julien (CoRR, 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)

Predicting Tactical Solutions to Operational Planning Problems Under Imperfect Information. - Eric Larsen; Sébastien Lachapelle; Yoshua Bengio; Emma Frejinger; Simon Lacoste-Julien; Andrea Lodi 001 (INFORMS J. Comput., 2022)

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 (ICML, 2023)

Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation. - Sébastien Lachapelle; Divyat Mahajan; Ioannis Mitliagkas; Simon Lacoste-Julien (NeurIPS, 2023)

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 (ICLR, 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 (ICML, 2024)

Nonparametric Partial Disentanglement via Mechanism Sparsity: Sparse Actions, Interventions and Sparse Temporal Dependencies. - Sébastien Lachapelle; Pau Rodríguez López; Yash Sharma 001; Katie Everett; Rémi Le Priol; Alexandre Lacoste; Simon Lacoste-Julien (CoRR, 2024)

Leveraging Structure Between Environments: Phylogenetic Regularization Incentivizes Disentangled Representations. - Elliot Layne; Jason S. Hartford; Sébastien Lachapelle; Mathieu Blanchette; Dhanya Sridhar (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)

All or None: Identifiable Linear Properties of Next-Token Predictors in Language Modeling. - Emanuele Marconato; Sébastien Lachapelle; Sebastian Weichwald; Luigi Gresele (AISTATS, 2025)

Interaction Asymmetry: A General Principle for Learning Composable Abstractions. - Jack Brady; Julius von Kügelgen; Sébastien Lachapelle; Simon Buchholz; Thomas Kipf; Wieland Brendel (ICLR, 2025)

Sparsity regularization via tree-structured environments for disentangled representations. - Elliot Layne; Jason S. Hartford; Sébastien Lachapelle; Mathieu Blanchette; Dhanya Sridhar (Trans. Mach. Learn. Res., 2025)

Identifiable Steering via Sparse Autoencoding of Multi-Concept Shifts. - Shruti Joshi; Andrea Dittadi; Sébastien Lachapelle; Dhanya Sridhar (CoRR, 2025)