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This paper studies the causal representation learning problem when the latent causal variables are observed indirectly through an unknown linear transformation.
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Francesco Locatello, Stefan Bauer, Mario Lucic, Gunnar Raetsch, Sylvain Gelly, Bernhard Schölkopf, and Olivier Bachem · 2019
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Weakly-supervised disentanglement without compromises
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Weakly supervised causal representation learning
Johann Brehmer, Pim De Haan, Phillip Lippe, and Taco Cohen · 2022
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Disentanglement via mechanism sparsity regularization: A new principle for nonlinear ICA
Sébastien Lachapelle, Pau Rodriguez, Yash Sharma, Katie E Everett, Rémi Le Priol, Alexandre Lacoste, and Simon Lacoste-Julien · 2022
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iCITRIS: Causal representation learning for instantaneous temporal effects
Phillip Lippe, Sara Magliacane, Sindy Löwe, Yuki M Asano, Taco Cohen, and Efstratios Gavves · 2022
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Compositional perturbation autoencoder for single-cell response modeling
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Toward causal representation learning
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Score-based generative modeling through stochastic differential equations
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Identifying weight-variant latent causal models
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Scalable causal discovery with score matching
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Causal discovery in heterogeneous environments under the sparse mechanism shift hypothesis
Ronan Perry, Julius von Kügelgen, and Bernhard Schölkopf · 2022
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Score matching enables causal discovery of nonlinear additive noise models
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Causality for Machine Learning , page 765–804
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From statistical to causal learning
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Linear causal disentanglement via interventions
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