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A Sparsity Principle for Partially Observable Causal Representation Learning.

D. Xu, D. Yao, S. Lachapelle, P. Taslakian, J. von Kügelgen, F. Locatello, and S. Magliacane. ICML, OpenReview.net, (2024)

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Sebastian Kügelgen

Kurt Kügelgen

Benedikt Kügelgen

Anke Kügelgen

Normann Kügelgen

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Multi-View Causal Representation Learning with Partial Observability.D. Yao, D. Xu, S. Lachapelle, S. Magliacane, P. Taslakian, G. Martius, J. von Kügelgen, and F. Locatello. CoRR, (2023)Visual Representation Learning Does Not Generalize Strongly Within the Same Domain.L. Schott, J. von Kügelgen, F. Träuble, P. Gehler, C. Russell, M. Bethge, B. Schölkopf, F. Locatello, and W. Brendel. ICLR, OpenReview.net, (2022)On the Fairness of Causal Algorithmic Recourse.J. von Kügelgen, A. Karimi, U. Bhatt, I. Valera, A. Weller, and B. Schölkopf. AAAI, page 9584-9594. AAAI Press, (2022)Age-stratified Covid-19 case fatality rates (CFRs): different countries and longitudinal.J. von Kügelgen, L. Gresele, and B. Schölkopf. (May 2022)A Sparsity Principle for Partially Observable Causal Representation Learning.D. Xu, D. Yao, S. Lachapelle, P. Taslakian, J. von Kügelgen, F. Locatello, and S. Magliacane. ICML, OpenReview.net, (2024)Active Bayesian Causal Inference.C. Toth, L. Lorch, C. Knoll, A. Krause, F. Pernkopf, R. Peharz, and J. von Kügelgen. NeurIPS, (2022)Backward-Compatible Prediction Updates: A Probabilistic Approach.F. Träuble, J. von Kügelgen, M. Kleindessner, F. Locatello, B. Schölkopf, and P. Gehler. NeurIPS, page 116-128. (2021)Provably Learning Object-Centric Representations.J. Brady, R. Zimmermann, Y. Sharma, B. Schölkopf, J. von Kügelgen, and W. Brendel. ICML, volume 202 of Proceedings of Machine Learning Research, page 3038-3062. PMLR, (2023)Towards Causal Algorithmic Recourse.A. Karimi, J. von Kügelgen, B. Schölkopf, and I. Valera. xxAI@ICML, volume 13200 of Lecture Notes in Computer Science, page 139-166. Springer, (2020)Semi-supervised learning, causality, and the conditional cluster assumption.J. von Kügelgen, A. Mey, M. Loog, and B. Schölkopf. UAI, volume 124 of Proceedings of Machine Learning Research, page 1-10. AUAI Press, (2020)

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Disambiguation of "von Kügelgen, Julius"

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A Sparsity Principle for Partially Observable Causal Representation Learning.

Please choose a person to relate this publication to

Sebastian Kügelgen

Kurt Kügelgen

Benedikt Kügelgen

Anke Kügelgen

Normann Kügelgen

Other publications of authors with the same name

Disambiguation

BibSonomy

Disambiguation of "von Kügelgen, Julius"

copydeleteadd this publication to your clipboardcommunity posthistory of this postURLDOIBibTeXEndNoteAPAChicagoDIN 1505HarvardMSOffice XML A Sparsity Principle for Partially Observable Causal Representation Learning.

Please choose a person to relate this publication to

Sebastian Kügelgen

Kurt Kügelgen

Benedikt Kügelgen

Anke Kügelgen

Normann Kügelgen

Other publications of authors with the same name

Disambiguation

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URL
DOI
BibTeX
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APA
Chicago
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A Sparsity Principle for Partially Observable Causal Representation Learning.