Следене
Julius von Kügelgen
Julius von Kügelgen
ETH Zürich
Потвърден имейл адрес: stat.math.ethz.ch - Начална страница
Заглавие
Позовавания
Позовавания
Година
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style
J von Kügelgen*, Y Sharma*, L Gresele*, W Brendel, B Schölkopf, ...
NeurIPS, 2021
3292021
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach
AH Karimi*, J von Kügelgen*, B Schölkopf, I Valera
NeurIPS, 2020
1952020
Independent mechanism analysis, a new concept?
L Gresele*, J von Kügelgen*, V Stimper, B Schölkopf, M Besserve
NeurIPS, 2021
1022021
On the Fairness of Causal Algorithmic Recourse
J von Kügelgen, AH Karimi, U Bhatt, I Valera, A Weller, B Schölkopf
AAAI, 2022
942022
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
82*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
772022
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
702022
Probable Domain Generalization via Quantile Risk Minimization
C Eastwood, A Robey, S Singh, J von Kügelgen, H Hassani, GJ Pappas, ...
NeurIPS, 2022
682022
Nonparametric Identifiability of Causal Representations from Unknown Interventions
J von Kügelgen, M Besserve, W Liang, L Gresele, A Kekić, E Bareinboim, ...
NeurIPS, 2023
622023
From Statistical to Causal Learning
B Schölkopf*, J von Kügelgen*
Proceedings of the International Congress of Mathematicians 7, 5540–5593, 2022
58*2022
Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis
R Perry, J von Kügelgen*, B Schölkopf*
NeurIPS, 2022
522022
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
512021
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
432020
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
42*2020
Causal Component Analysis
L Wendong, A Kekić, J von Kügelgen, S Buchholz, M Besserve, L Gresele, ...
NeurIPS, 2023
412023
Provably Learning Object-Centric Representations
J Brady, RS Zimmermann, Y Sharma, B Schölkopf, J von Kügelgen, ...
ICML, 2023
402023
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
402022
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
382021
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
382019
Active Bayesian Causal Inference
C Toth, L Lorch, C Knoll, A Krause, F Pernkopf, R Peharz*, ...
NeurIPS, 2022
372022
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Статии 1–20