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07/05/2023
Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation
We tackle the problems of latent variables identification and "out-of-su...
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11/26/2022
Synergies Between Disentanglement and Sparsity: a Multi-Task Learning Perspective
Although disentangled representations are often said to be beneficial fo...
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07/15/2022
Partial Disentanglement via Mechanism Sparsity
Disentanglement via mechanism sparsity was introduced recently as a prin...
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07/22/2021
Typing assumptions improve identification in causal discovery
Causal discovery from observational data is a challenging task to which ...
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07/21/2021
Discovering Latent Causal Variables via Mechanism Sparsity: A New Principle for Nonlinear ICA
It can be argued that finding an interpretable low-dimensional represent...
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11/23/2020
On the Convergence of Continuous Constrained Optimization for Structure Learning
Structure learning of directed acyclic graphs (DAGs) is a fundamental pr...
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07/03/2020
Differentiable Causal Discovery from Interventional Data
Discovering causal relationships in data is a challenging task that invo...
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06/05/2019
Gradient-Based Neural DAG Learning
We propose a novel score-based approach to learning a directed acyclic g...
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01/30/2019
A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms
We propose to meta-learn causal structures based on how fast a learner a...
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01/22/2019
Predicting Tactical Solutions to Operational Planning Problems under Imperfect Information
This paper offers a methodological contribution at the intersection of m...
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07/31/2018