Julius von Kügelgen
Julius von Kügelgen
Max Planck Institute for Intelligent Systems Tübingen & University of Cambridge
Verified email at tuebingen.mpg.de - Homepage
Title
Cited by
Cited by
Year
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach
AH Karimi*, J von Kügelgen*, B Schölkopf, I Valera
NeurIPS 2020, 2020
302020
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
17*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
102020
Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features
J von Kügelgen, A Mey, M Loog
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
82019
On the Fairness of Causal Algorithmic Recourse
J von Kügelgen, AH Karimi, U Bhatt, I Valera, A Weller, B Schölkopf
NeurIPS 2020 Workshop Algorithmic Fairness through the Lens of Causality and …, 2020
62020
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
52021
Semi-supervised learning, causality and the conditional cluster assumption
J von Kügelgen, A Mey, M Loog, B Schölkopf
Conference on Uncertainty in Artificial Intelligence, 2020, 1--10, 2020
52020
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
42019
On Artificial Spiking Neural Networks: Principles, Limitations and Potential
J von Kügelgen
June 18, 2017, 2017
12017
Backward-Compatible Prediction Updates: A Probabilistic Approach
F Träuble, J von Kügelgen, M Kleindessner, F Locatello, B Schölkopf, ...
arXiv preprint arXiv:2107.01057, 2021
2021
Algorithmic Recourse in Partially and Fully Confounded Settings Through Bounding Counterfactual Effects
J von Kügelgen, N Agarwal, J Zeitler, A Mastouri, B Schölkopf
arXiv preprint arXiv:2106.11849, 2021
2021
Independent mechanism analysis, a new concept?
L Gresele, J von Kügelgen, V Stimper, B Schölkopf, M Besserve
arXiv preprint arXiv:2106.05200, 2021
2021
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style
J von Kügelgen, Y Sharma, L Gresele, W Brendel, B Schölkopf, ...
arXiv preprint arXiv:2106.04619, 2021
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 Workshop - Generalization beyond the training distribution in brains …, 2021
2021
Complex Interlinkages, Key Objectives and Nexuses Amongst the Sustainable Development Goals and Climate Change
F Laumann, J von Kügelgen, TH Kanashiro Uehara, M Barahona
SSRN preprint, 2021
2021
Kernel Two-Sample and Independence Tests for Non-Stationary Random Processes
F Laumann, J von Kügelgen, M Barahona
Engineering Proceedings 5 (1), 31, 2021
2021
A Mathematical Model for the Kinetics of the MalFGK (2) Maltose Transporter (vol 82, 62, 2020)
RM Hiller, J von Kugelgen, H Bao, F Duong Van Hoa, EN Cytrynbaum
BULLETIN OF MATHEMATICAL BIOLOGY 82 (7), 2020
2020
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