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 GreseleMax Planck Institute for Intelligent Systems, TübingenVerified email at tuebingen.mpg.de
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 GehlerZalandoVerified email at zalando.de
Amir-Hossein KarimiETH Zurich & MPI for Intelligent SystemsVerified email at tue.mpg.de
Isabel ValeraSaarland University and Max Planck Institute for Software SystemsVerified email at cs.uni-saarland.de
Yash SharmaUniversity of Tübingen, Max Planck Institute for Intelligent SystemsVerified email at bethgelab.org
Frederik TräubleMax Planck Institute for Intelligent SystemsVerified email at tuebingen.mpg.de
Matthias BethgeTübingen University & Maddox Co-FounderVerified email at bethgelab.org
Adrian WellerDirector of Research, Machine Learning, University of CambridgeVerified email at eng.cam.ac.uk
Cian EastwoodUniversity of EdinburghVerified email at ed.ac.uk
Marco LoogRadboud UniversityVerified email at ru.nl
Armin KekićMax Planck Institute for Intelligent SystemsVerified email at tuebingen.mpg.de
Thomas BroxUniversity of Freiburg and AmazonVerified email at cs.uni-freiburg.de
Dominik JanzingAmazon Development Center, TuebingenVerified email at amazon.com
Umang BhattUniversity of CambridgeVerified email at cam.ac.uk
Lukas SchottPhD Student International Max Planck Research School for Intelligent SystemsVerified email at bethgelab.org
Shashank SinghMax Planck Institute for Intelligent SystemsVerified email at tuebingen.mpg.de

Julius von Kügelgen

Max Planck Institute for Intelligent Systems Tübingen & University of Cambridge

Verified email at tuebingen.mpg.de - Homepage

Machine Learning Causal Inference Causality Causal Representation Learning Artificial Intelligence


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style J von Kügelgen, Y Sharma, L Gresele*, W Brendel, B Schölkopf, ... NeurIPS, 2021	162	2021
Algorithmic recourse under imperfect causal knowledge: a probabilistic approach AH Karimi, J von Kügelgen, B Schölkopf, I Valera NeurIPS, 2020	132	2020
On the Fairness of Causal Algorithmic Recourse J von Kügelgen, AH Karimi, U Bhatt, I Valera, A Weller, B Schölkopf AAAI, 2022	48	2022
Independent mechanism analysis, a new concept? L Gresele, J von Kügelgen, V Stimper, B Schölkopf, M Besserve NeurIPS, 2021	47	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	38	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	33*	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	32*	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	31	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	28	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	27*	2022
From Statistical to Causal Learning B Schölkopf, J von Kügelgen Proceedings of the International Congress of Mathematicians, 2022	26	2022
Semi-supervised learning, causality and the conditional cluster assumption J von Kügelgen, A Mey, M Loog, B Schölkopf UAI, 2020	21*	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	19	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	19	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	19	2019
Probable Domain Generalization via Quantile Risk Minimization C Eastwood, A Robey, S Singh, J von Kügelgen, H Hassani, GJ Pappas, ... NeurIPS, 2022	14	2022
Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features J von Kügelgen, A Mey, M Loog AISTATS, 2019	13*	2019
Unsupervised object learning via common fate M Tangemann, S Schneider, J Von Kügelgen, F Locatello, P Gehler, ... CLeaR, 2023	12	2023
Causal Discovery in Heterogeneous Environments Under the Sparse Mechanism Shift Hypothesis R Perry, J von Kügelgen, B Schölkopf NeurIPS, 2022	12	2022
Backtracking Counterfactuals J von Kügelgen, A Mohamed, S Beckers CLeaR, 2023	10*	2023

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