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Julius von Kügelgen
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: Julius von Kügelgen has transitioned from the institute (Alumni).
Empirische Inferenz
Doctoral Researcher
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Overview
Publications
Empirical Inference
Conference Paper
Algorithmic recourse in partially and fully confounded settings through bounding counterfactual effects
von Kügelgen, J., Agarwal, N., Zeitler, J., Mastouri, A., Schölkopf, B.
ICML 2021 Workshop on Algorithmic Recourse
, July 2021
(Published)
arXiv
URL
BibTeX
Empirical Inference
Conference Paper
Kernel Two-Sample and Independence Tests for Non-Stationary Random Processes
Laumann, F., von Kügelgen, J., Barahona, M.
7th International Conference on Time Series and Forecasting (ITISE 2021)
, July 2021
(Published)
arXiv
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.
ICLR 2021 - Workshop on Generalization beyond the training distribution in brains and machines
, May 2021
(Published)
URL
BibTeX
Empirical Inference
Article
Simpson’s paradox in Covid-19 case fatality rates: a mediation analysis of age-related causal effects
von Kügelgen*, J., Gresele*, L., Schölkopf, B.
IEEE Transactions on Artificial Intelligence
, 2(1):18-27, IEEE Computer Society, 2021, *equal contribution
(Published)
arXiv
DOI
URL
BibTeX
Empirical Inference
Probabilistic Learning Group
Conference Paper
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach
Karimi*, A., von Kügelgen*, J., Schölkopf, B., Valera, I.
Advances in Neural Information Processing Systems 33 (NeurIPS 2020)
, 265-277,
(Editors: H. Larochelle and M. Ranzato and R. Hadsell and M. F. Balcan and H. Lin)
, Curran Associates, Inc., 34th Annual Conference on Neural Information Processing Systems, December 2020, *equal contribution
(Published)
arXiv
URL
BibTeX
Empirical Inference
Conference Paper
Semi-supervised learning, causality, and the conditional cluster assumption
von Kügelgen, J., Mey, A., Loog, M., Schölkopf, B.
Proceedings of the 36th International Conference on Uncertainty in Artificial Intelligence (UAI)
, 124:1-10, Proceedings of Machine Learning Research,
(Editors: Jonas Peters and David Sontag)
, PMLR, August 2020, *also at NeurIPS 2019 Workshop Do the right thing: machine learning and causal inference for improved decision making
(Published)
arXiv
URL
BibTeX
Empirical Inference
Conference Paper
Non-linear interlinkages and key objectives amongst the Paris Agreement and the Sustainable Development Goals
Laumann, F., von Kügelgen, J., Barahona, M.
ICLR 2020 Workshop "Tackling Climate Change with Machine Learning"
, April 2020
(Published)
arXiv
PDF
BibTeX
Empirical Inference
Conference Paper
Towards causal generative scene models via competition of experts
von Kügelgen*, J., Ustyuzhaninov*, I., Gehler, P., Bethge, M., Schölkopf, B.
ICLR 2020 Workshop "Causal Learning for Decision Making"
, April 2020, *equal contribution
(Published)
arXiv
PDF
BibTeX
Empirical Inference
Conference Paper
Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks
von Kügelgen, J., Rubenstein, P. K., Schölkopf, B., Weller, A.
NeurIPS 2019 Workshop Do the right thing: machine learning and causal inference for improved decision making
, December 2019
(Published)
arXiv
Poster
URL
BibTeX
Empirical Inference
Conference Paper
Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features
von Kügelgen, J., Mey, A., Loog, M.
In
Proceedings of the 22nd International Conference on Artificial Intelligence and Statistics (AISTATS)
, 89:1361-1369,
(Editors: Kamalika Chaudhuri and Masashi Sugiyama)
, PMLR, April 2019
(Published)
PDF
Poster
URL
BibTeX
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