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A Meta-Transfer Objective for Learning to Disentangle Causal Mechanisms, , , , , , , and . (2019)cite arxiv:1901.10912.A Sparsity Principle for Partially Observable Causal Representation Learning., , , , , , and . CoRR, (2024)Multi-View Causal Representation Learning with Partial Observability., , , , , , , and . CoRR, (2023)Additive Decoders for Latent Variables Identification and Cartesian-Product Extrapolation., , , and . CoRR, (2023)Synergies Between Disentanglement and Sparsity: a Multi-Task Learning Perspective., , , , , , and . CoRR, (2022)Typing assumptions improve identification in causal discovery., , , , and . CLeaR, volume 177 of Proceedings of Machine Learning Research, page 162-177. PMLR, (2022)Predicting Solution Summaries to Integer Linear Programs under Imperfect Information with Machine Learning., , , , , and . CoRR, (2018)Gradient-Based Neural DAG Learning., , , and . ICLR, OpenReview.net, (2020)Disentanglement via Mechanism Sparsity Regularization: A New Principle for Nonlinear ICA., , , , , , and . CLeaR, volume 177 of Proceedings of Machine Learning Research, page 428-484. PMLR, (2022)Synergies between Disentanglement and Sparsity: Generalization and Identifiability in Multi-Task Learning., , , , , , and . ICML, volume 202 of Proceedings of Machine Learning Research, page 18171-18206. PMLR, (2023)