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Julius von Kügelgen
Note
: Julius von Kügelgen has transitioned from the institute (Alumni).
Empirische Inferenz
Doctoral Researcher
Alumni
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Overview
Publications
Empirical Inference
Conference Paper
From statistical to causal learning
Schölkopf*, B., von Kügelgen*, J.
Proceedings of the International Congress of Mathematicians (ICM)
, VII:5540-5593, EMS Press, July 2022, *equal contribution
(Published)
arXiv
DOI
URL
BibTeX
Empirical Inference
Article
Complex interlinkages, key objectives and nexuses amongst the Sustainable Development Goals and climate change: a network analysis
Laumann, F., von Kügelgen, J., Kanashiro Uehara, T. H., Barahona, M.
The Lancet Planetary Health
, 6(5):e422-e430, May 2022
(Published)
DOI
BibTeX
Empirical Inference
Conference Paper
Visual Representation Learning Does Not Generalize Strongly Within the Same Domain
Schott, L., von Kügelgen, J., Träuble, F., Gehler, P., Russell, C., Bethge, M., Schölkopf, B., Locatello, F., Brendel, W.
The Tenth International Conference on Learning Representations (ICLR 2022)
, 10th International Conference on Learning Representations (ICLR), April 2022
(Published)
URL
BibTeX
Empirical Inference
Conference Paper
You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction
Makansi, O., von Kügelgen, J., Locatello, F., Gehler, P., Janzing, D., Brox, T., Schölkopf, B.
10th International Conference on Learning Representations (ICLR)
, April 2022
(Published)
arXiv
URL
BibTeX
Empirical Inference
Probabilistic Learning Group
Conference Paper
On the Fairness of Causal Algorithmic Recourse
von Kügelgen, J., Karimi, A., Bhatt, U., Valera, I., Weller, A., Schölkopf, B.
Proceedings of the 36th AAAI Conference on Artificial Intelligence
, 9:9584-9594, AAAI Press, Palo Alto, CA, Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI 2022), February 2022, *also at ICML 2021 Workshop Algorithmic Recourse and NeurIPS 2020 Workshop Algorithmic Fairness through the Lens of Causality and Interpretability (AFCI)
(Published)
arXiv
DOI
URL
BibTeX
Empirical Inference
Probabilistic Learning Group
Book Chapter
Towards Causal Algorithmic Recourse
Karimi, A. H., von Kügelgen, J., Schölkopf, B., Valera, I.
In
xxAI - Beyond Explainable AI: International Workshop, Held in Conjunction with ICML 2020, July 18, 2020, Vienna, Austria, Revised and Extended Papers
, 139-166,
(Editors: Holzinger, Andreas and Goebel, Randy and Fong, Ruth and Moon, Taesup and Müller, Klaus-Robert and Samek, Wojciech)
, Springer International Publishing, 2022
(Published)
DOI
BibTeX
Empirical Inference
Conference Paper
Backward-Compatible Prediction Updates: A Probabilistic Approach
Träuble, F., von Kügelgen, J., Kleindessner, M., Locatello, F., Schölkopf, B., Gehler, P.
Advances in Neural Information Processing Systems 34 (NeurIPS 2021)
, 116-128,
(Editors: M. Ranzato and A. Beygelzimer and Y. Dauphin and P.S. Liang and J. Wortman Vaughan)
, Curran Associates, Inc., 35th Annual Conference on Neural Information Processing Systems (NeurIPS), December 2021
(Published)
arXiv
URL
BibTeX
Empirical Inference
Conference Paper
Independent mechanisms analysis, a new concept?
Gresele*, L., von Kügelgen*, J., Stimper, V., Schölkopf, B., Besserve, M.
Advances in Neural Information Processing Systems 34 (NeurIPS 2021)
, 28233-28248,
(Editors: M. Ranzato and A. Beygelzimer and Y. Dauphin and P.S. Liang and J. Wortman Vaughan)
, Curran Associates, Inc., 35th Annual Conference on Neural Information Processing Systems, December 2021, *equal contribution
(Published)
arXiv
URL
BibTeX
Empirical Inference
Conference Paper
Self-supervised learning with data augmentations provably isolates content from style
von Kügelgen*, J., Sharma*, Y., Gresele*, L., Brendel, W., Schölkopf, B., Besserve, M., Locatello, F.
Advances in Neural Information Processing Systems 34 (NeurIPS 2021)
, 16451-16467,
(Editors: M. Ranzato and A. Beygelzimer and Y. Dauphin and P.S. Liang and J. Wortman Vaughan)
, Curran Associates, Inc., 35th Annual Conference on Neural Information Processing Systems, December 2021, *equal contribution
(Published)
arXiv
URL
BibTeX
Empirical Inference
Conference Paper
Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP
Jin*, Z., von Kügelgen*, J., Ni, J., Vaidhya, T., Kaushal, A., Sachan, M., Schölkopf, B.
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP)
, 9499-9513,
(Editors: Marie-Francine Moens and Xuanjing Huang and Lucia Specia and Scott Wen-tau Yih)
, Association for Computational Linguistics, November 2021, *equal contribution
(Published)
arXiv
DOI
URL
BibTeX
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