Julius von Kügelgen
Julius von Kügelgen
Max Planck Institute for Intelligent Systems Tübingen & University of Cambridge
Verified email at - Homepage
Cited by
Cited by
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style
J von Kügelgen*, Y Sharma*, L Gresele*, W Brendel, B Schölkopf, ...
NeurIPS, 2021
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach
AH Karimi*, J von Kügelgen*, B Schölkopf, I Valera
NeurIPS, 2020
On the Fairness of Causal Algorithmic Recourse
J von Kügelgen, AH Karimi, U Bhatt, I Valera, A Weller, B Schölkopf
AAAI, 2022
Independent mechanism analysis, a new concept?
L Gresele*, J von Kügelgen*, V Stimper, B Schölkopf, M Besserve
NeurIPS, 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
Towards Causal Algorithmic Recourse
AH Karimi*, J von Kügelgen*, B Schölkopf, I Valera
Lecture Notes in Computer Science 13200 (xxAI - Beyond Explainable AI), 139–166, 2022
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 Workshop Causal Learning for Decision Making, 2020
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
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
From Statistical to Causal Learning
B Schölkopf, J von Kügelgen
Proceedings of the International Congress of Mathematicians, 2022
Semi-supervised learning, causality and the conditional cluster assumption
J von Kügelgen, A Mey, M Loog, B Schölkopf
UAI, 2020
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
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
Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks
J von Kügelgen, PK Rubenstein, B Schölkopf, A Weller
NeurIPS Workshop “Do the right thing”: machine learning and causal inference …, 2019
Probable Domain Generalization via Quantile Risk Minimization
C Eastwood, A Robey, S Singh, J von Kügelgen, H Hassani, GJ Pappas, ...
NeurIPS, 2022
Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features
J von Kügelgen, A Mey, M Loog
Unsupervised object learning via common fate
M Tangemann, S Schneider, J Von Kügelgen, F Locatello, P Gehler, ...
CLeaR, 2023
Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis
R Perry, J von Kügelgen*, B Schölkopf*
NeurIPS, 2022
Backtracking Counterfactuals
J von Kügelgen, A Mohamed, S Beckers
CLeaR, 2023
The system can't perform the operation now. Try again later.
Articles 1–20