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Julius von Kügelgen
Julius von Kügelgen
ETH Zürich
Bestätigte E-Mail-Adresse bei tuebingen.mpg.de - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style
J von Kügelgen*, Y Sharma*, L Gresele*, W Brendel, B Schölkopf, ...
NeurIPS, 2021
2422021
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach
AH Karimi*, J von Kügelgen*, B Schölkopf, I Valera
NeurIPS, 2020
1612020
Independent mechanism analysis, a new concept?
L Gresele*, J von Kügelgen*, V Stimper, B Schölkopf, M Besserve
NeurIPS, 2021
792021
On the Fairness of Causal Algorithmic Recourse
J von Kügelgen, AH Karimi, U Bhatt, I Valera, A Weller, B Schölkopf
AAAI, 2022
772022
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
59*2022
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
552022
From Statistical to Causal Learning
B Schölkopf*, J von Kügelgen*
Proceedings of the International Congress of Mathematicians 7, 5540–5593, 2022
422022
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
422022
Probable Domain Generalization via Quantile Risk Minimization
C Eastwood, A Robey, S Singh, J von Kügelgen, H Hassani, GJ Pappas, ...
NeurIPS, 2022
412022
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
392020
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
362021
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
35*2020
Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis
R Perry, J von Kügelgen*, B Schölkopf*
NeurIPS, 2022
312022
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
312022
Semi-supervised learning, causality and the conditional cluster assumption
J von Kügelgen, A Mey, M Loog, B Schölkopf
UAI, 2020
29*2020
Nonparametric Identifiability of Causal Representations from Unknown Interventions
J von Kügelgen, M Besserve, W Liang, L Gresele, A Kekić, E Bareinboim, ...
NeurIPS, 2023
282023
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
272021
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
262019
Causal Component Analysis
L Wendong, A Kekić, J von Kügelgen, S Buchholz, M Besserve, L Gresele, ...
NeurIPS, 2023
202023
Backtracking Counterfactuals
J von Kügelgen, A Mohamed, S Beckers
CLeaR, 2023
20*2023
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