Julius von Kügelgen
Julius von Kügelgen
Max Planck Institute for Intelligent Systems Tübingen & University of Cambridge
Verified email at - Homepage
Cited by
Cited by
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach
AH Karimi*, J von Kügelgen*, B Schölkopf, I Valera
NeurIPS 2020, 2020
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style
J von Kügelgen*, Y Sharma*, L Gresele*, W Brendel, B Schölkopf, ...
NeurIPS 2021, 2021
A bacterial size law revealed by a coarse-grained model of cell physiology
F Bertaux, J von Kügelgen, S Marguerat, V Shahrezaei
PLoS Computational Biology 16 (9), e1008245, 2020
Towards causal generative scene models via competition of experts
J von Kügelgen*, I Ustyuzhaninov*, P Gehler, M Bethge, B Schölkopf
ICLR 2020 Workshop Causal Learning for Decision Making, 2020
On the Fairness of Causal Algorithmic Recourse
J von Kügelgen, AH Karimi, U Bhatt, I Valera, A Weller, B Schölkopf
AAAI 2022, 2020
Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features
J von Kügelgen, A Mey, M Loog
AISTATS 2019, 2019
Simpson's paradox in Covid-19 case fatality rates: a mediation analysis of age-related causal effects
J von Kügelgen*, L Gresele*, B Schölkopf
IEEE Transactions on Artificial Intelligence 2 (1), 18-27, 2021
Semi-supervised learning, causality and the conditional cluster assumption
J von Kügelgen, A Mey, M Loog, B Schölkopf
UAI 2020, 2020
Independent mechanism analysis, a new concept?
L Gresele*, J von Kügelgen*, V Stimper, B Schölkopf, M Besserve
NeurIPS 2021, 2021
Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks
J von Kügelgen, PK Rubenstein, B Schölkopf, A Weller
NeurIPS 2019 Workshop “Do the right thing”: machine learning and causal …, 2019
You Mostly Walk Alone: Analyzing Feature Attribution in Trajectory Prediction
O Makansi, J von Kügelgen, F Locatello, P Gehler, D Janzing, T Brox, ...
ICLR 2022, 2021
Causal Direction of Data Collection Matters: Implications of Causal and Anticausal Learning for NLP
Z Jin*, J von Kügelgen*, J Ni, T Vaidhya, A Kaushal, M Sachan, ...
EMNLP 2021, 2021
Backward-Compatible Prediction Updates: A Probabilistic Approach
F Träuble, J von Kügelgen, M Kleindessner, F Locatello, B Schölkopf, ...
NeurIPS 2021, 2021
On Artificial Spiking Neural Networks: Principles, Limitations and Potential
J von Kügelgen
Algorithmic Recourse in Partially and Fully Confounded Settings Through Bounding Counterfactual Effects
J von Kügelgen, N Agarwal, J Zeitler, A Mastouri, B Schölkopf
ICML 2021 Workshop Algorithmic Recourse, 2021
Visual representation learning does not generalize strongly within the same domain
L Schott, J von Kügelgen, F Träuble, P Gehler, C Russell, M Bethge, ...
ICLR 2022, 2021
Causal Inference Through the Structural Causal Marginal Problem
L Gresele*, J von Kügelgen*, JM Kübler*, E Kirschbaum, B Schölkopf, ...
ICML 2022, 2022
Complex interlinkages, key objectives, and nexuses among the Sustainable Development Goals and climate change: a network analysis
F Laumann, J von Kügelgen, TH Kanashiro Uehara, M Barahona
The Lancet Planetary Health 6 (5), e422-e430, 2022
Unsupervised Object Learning via Common Fate
M Tangemann, S Schneider, J von Kügelgen, F Locatello, P Gehler, ...
arXiv preprint arXiv:2110.06562, 2021
From Statistical to Causal Learning
B Schölkopf, J von Kügelgen
arXiv preprint arXiv:2204.00607, 2022
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