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