Julius von Kügelgen

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Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHVerified email at tuebingen.mpg.de
Luigi GreseleUniversity of CopenhagenVerified email at math.ku.dk
Michel BesserveMax Planck Institute for Intelligent SystemsVerified email at tuebingen.mpg.de
Francesco LocatelloAssistant Professor, ISTAVerified email at ista.ac.at
Wieland BrendelFellow at ELLIS Institut Tübingen, Group Leader, Max Planck Institute for Intelligent SystemsVerified email at tuebingen.mpg.de
Peter GehlerTübingen AI Center, University TübingenVerified email at uni-tuebingen.de
Armin KekićMax Planck Institute for Intelligent SystemsVerified email at tuebingen.mpg.de
Amir-Hossein KarimiUniversity of WaterlooVerified email at uwaterloo.ca
Isabel ValeraSaarland University and Max Planck Institute for Software SystemsVerified email at cs.uni-saarland.de
Frederik TräubleMax Planck Institute for Intelligent SystemsVerified email at tuebingen.mpg.de
Cian EastwoodUniversity of EdinburghVerified email at ed.ac.uk
Yash SharmaUniversity of Tübingen, Max Planck Institute for Intelligent SystemsVerified email at bethgelab.org
Matthias BethgeTübingen University & Maddox Co-FounderVerified email at bethgelab.org
Jack BradyPhD StudentVerified email at tuebingen.mpg.de
Adrian WellerDirector of Research, Machine Learning, University of CambridgeVerified email at eng.cam.ac.uk
Sébastien LachapelleResearch scientist, Samsung's SAIT AI Lab, MontrealVerified email at samsung.com
Andrei Liviu NicolicioiuMila and University of MontrealVerified email at mila.quebec
Liang WendongMax Planck Institute for Intelligent SystemsVerified email at tuebingen.mpg.de
Shashank SinghMax Planck Institute for Intelligent SystemsVerified email at tuebingen.mpg.de
Marco LoogRadboud UniversityVerified email at ru.nl

Julius von Kügelgen

ETH Zürich

Verified email at stat.math.ethz.ch - Homepage

Machine Learning Causal Inference Causal Representation Learning


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Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style J von Kügelgen, Y Sharma, L Gresele*, W Brendel, B Schölkopf, ... NeurIPS, 2021	342	2021
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach AH Karimi, J von Kügelgen, B Schölkopf, I Valera NeurIPS, 2020	199	2020
Independent mechanism analysis, a new concept? L Gresele, J von Kügelgen, V Stimper, B Schölkopf, M Besserve NeurIPS, 2021	103	2021
On the Fairness of Causal Algorithmic Recourse J von Kügelgen, AH Karimi, U Bhatt, I Valera, A Weller, B Schölkopf AAAI, 2022	97	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	86*	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	80	2022
Probable Domain Generalization via Quantile Risk Minimization C Eastwood, A Robey, S Singh, J von Kügelgen, H Hassani, GJ Pappas, ... NeurIPS, 2022	72	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	71	2022
Nonparametric Identifiability of Causal Representations from Unknown Interventions J von Kügelgen, M Besserve, W Liang, L Gresele, A Kekić, E Bareinboim, ... NeurIPS, 2023	65	2023
From Statistical to Causal Learning B Schölkopf, J von Kügelgen Proceedings of the International Congress of Mathematicians 7, 5540–5593, 2022	61*	2022
Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis R Perry, J von Kügelgen, B Schölkopf NeurIPS, 2022	53	2022
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	52	2021
Provably Learning Object-Centric Representations J Brady, RS Zimmermann, Y Sharma, B Schölkopf, J von Kügelgen, ... ICML, 2023	44*	2023
Causal Component Analysis L Wendong, A Kekić, J von Kügelgen, S Buchholz, M Besserve, L Gresele, ... NeurIPS, 2023	43	2023
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	43*	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	43	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	41	2022
Active Bayesian Causal Inference C Toth, L Lorch, C Knoll, A Krause, F Pernkopf, R Peharz*, ... NeurIPS, 2022	39	2022
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	39	2019
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	38	2021

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