I am a fifth year Ph.D student supervised by Simon Lacoste-Julien.
I have graduated from Université de Montréal with a Bachelor's
degree in Mathematics and Economics.
I now work on causal structure learning and causal representation learning.
More details can be found in my resume.
9
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Synergies Between Disentanglement and Sparsity: a Multi-Task Learning Perspective By Sébastien Lachapelle, Tristan Deleu, Divyat Mahajan, Ioannis Mitliagkas, Yoshua Bengio, Simon Lacoste-Julien and Quentin Bertrand. Under review (2022).
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8
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Partial Disentanglement via Mechanism Sparsity By Sébastien Lachapelle and Simon Lacoste-Julien. UAI 2022 First Workshop on Causal Representation Learning (Workshop oral + Best paper award!).
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7
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Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA By Sébastien Lachapelle, Pau Rodríguez López , Yash Sharma , Katie Everett , Rémi Le Priol , Alexandre Lacoste and Simon Lacoste-Julien. CLeaR 2022.
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6
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Typing assumptions improve identification in causal discovery By Philippe Brouillard, Perouz Taslakian, Alexandre Lacoste, Sébastien Lachapelle and Alexandre Drouin. CLeaR 2022 (Oral!).
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5
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Differentiable Causal Discovery from Interventional Data By Philippe Brouillard*, Sébastien Lachapelle*, Alexandre Lacoste, Simon Lacoste-Julien and Alexandre Drouin. *Equal contribution. NeurIPS 2020 (Spotlight!).
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4
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On the Convergence of Continuous Constrained Optimization for Structure Learning By Ignavier Ng, Sébastien Lachapelle, Nan Rosemary Ke and Simon Lacoste-Julien. AISTATS 2022. ICML 2021 Workshop on The Neglected Assumptions in Causal Inference 2022 (Workshop oral!).
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3
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Gradient-Based Neural DAG Learning By Sébastien Lachapelle, Philippe Brouillard, Tristan Deleu and Simon Lacoste-Julien. ICLR 2020.
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2
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A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms By Yoshua Bengio, Tristan Deleu, Nasim Rahaman, Rosemary Ke, Sébastien Lachapelle, Olexa Bilaniuk, Anirudh Goyal and Christopher Pal. ICLR 2020.
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1
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Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information By Eric Larsen, Sébastien Lachapelle, Yoshua Bengio, Emma Frejinger, Simon Lacoste-Julien and Andrea Lodi. INFORMS Journal on Computing 2021.
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Science Talk at Element AI (2020)
“Learning Causal Structures via Gradient-Based Optimization”, Montreal, Quebec, Canada.
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NeurIPS 2019 Workshop on Solving Inverse Problems with Deep Networks
“Gradient-Based Neural DAG Learning”, Vancouver, British-Columbia, Canada.
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Montreal AI Symposium 2019
“Gradient-Based Neural DAG Learning”, Montreal, Quebec, Canada.
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Deep Learning and Reinforcement Learning Summer School 2019
“Gradient-Based Neural DAG Learning”, Edmonton, Alberta, Canada.
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Optimization Days 2018
“Predicting solution summaries to integer linear programs under imperfect information with machine learning”, Montreal, Quebec, Canada.
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DIMACS 2018
“Predicting solution summaries to integer linear programs under imperfect information with machine learning", Bethlehem, Pennsylvania, United-States.
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