Julius von Kügelgen

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Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems; Professor at ETH Zürich, and DistinguishedVerified email at tuebingen.mpg.de
Isabel ValeraSaarland University and Max Planck Institute for Intelligent SystemsVerified email at tuebingen.mpg.de
Amir-Hossein KarimiMax Planck Institute for Intelligent SystemsVerified email at tue.mpg.de
Peter GehlerAmazon Web ServicesVerified email at amazon.com
Marco LoogDelft University of Technology & University of CopenhagenVerified email at tudelft.nl
Adrian WellerPrincipal Research Fellow, Machine Learning, University of CambridgeVerified email at eng.cam.ac.uk
Matthias BethgeTübingen University & Amazon ScholarVerified email at bethgelab.org
Ivan UstyuzhaninovPhD student, University of TübingenVerified email at bethgelab.org
Luigi GreselePh.D. student, Max Planck Institute for Intelligent SystemsVerified email at tuebingen.mpg.de
Umang BhattUniversity of CambridgeVerified email at cam.ac.uk
Paul K RubensteinApple Inc.Verified email at apple.com
Felix LaumannImperial College LondonVerified email at imperial.ac.uk
Francesco LocatelloSenior Applied Scientist, Amazon AWSVerified email at amazon.com
Wieland BrendelEmmy Noether Fellow, University of TübingenVerified email at uni-tuebingen.de
Frederik TräubleMax Planck Institute for Intelligent SystemsVerified email at tuebingen.mpg.de
Michel BesserveMax Planck Institute for Intelligent Systems/Max Planck Institute for Biological CyberneticsVerified email at tuebingen.mpg.de
Lukas SchottPhD Student International Max Planck Research School for Intelligent SystemsVerified email at bethgelab.org
Vincent StimperUniversity of Cambridge, Max Planck Institute for Intelligent SystemsVerified email at cam.ac.uk
Yash SharmaUniversity of Tübingen, Max Planck Institute for Intelligent SystemsVerified email at bethgelab.org
Matthäus KleindessnerAmazon AWS TübingenVerified email at amazon.com

Julius von Kügelgen

Max Planck Institute for Intelligent Systems Tübingen & University of Cambridge

Verified email at tuebingen.mpg.de - Homepage

Machine Learning Causal Inference


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Algorithmic recourse under imperfect causal knowledge: a probabilistic approach AH Karimi, J von Kügelgen, B Schölkopf, I Valera NeurIPS 2020, 2020	30	2020
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	17*	2020
Towards causal generative scene models via competition of experts J von Kügelgen, I Ustyuzhaninov, P Gehler, M Bethge, B Schölkopf ICLR 2020 Workshop Causal Learning for Decision Making, 2020	10	2020
Semi-Generative Modelling: Covariate-Shift Adaptation with Cause and Effect Features J von Kügelgen, A Mey, M Loog The 22nd International Conference on Artificial Intelligence and Statistics …, 2019	8	2019
On the Fairness of Causal Algorithmic Recourse J von Kügelgen, AH Karimi, U Bhatt, I Valera, A Weller, B Schölkopf NeurIPS 2020 Workshop Algorithmic Fairness through the Lens of Causality and …, 2020	6	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	5	2021
Semi-supervised learning, causality and the conditional cluster assumption J von Kügelgen, A Mey, M Loog, B Schölkopf Conference on Uncertainty in Artificial Intelligence, 2020, 1--10, 2020	5	2020
Optimal experimental design via Bayesian optimization: active causal structure learning for Gaussian process networks J von Kügelgen, PK Rubenstein, B Schölkopf, A Weller NeurIPS 2019 Workshop “Do the right thing”: machine learning and causal …, 2019	4	2019
On Artificial Spiking Neural Networks: Principles, Limitations and Potential J von Kügelgen June 18, 2017, 2017	1	2017
Backward-Compatible Prediction Updates: A Probabilistic Approach F Träuble, J von Kügelgen, M Kleindessner, F Locatello, B Schölkopf, ... arXiv preprint arXiv:2107.01057, 2021		2021
Algorithmic Recourse in Partially and Fully Confounded Settings Through Bounding Counterfactual Effects J von Kügelgen, N Agarwal, J Zeitler, A Mastouri, B Schölkopf arXiv preprint arXiv:2106.11849, 2021		2021
Independent mechanism analysis, a new concept? L Gresele, J von Kügelgen, V Stimper, B Schölkopf, M Besserve arXiv preprint arXiv:2106.05200, 2021		2021
Self-Supervised Learning with Data Augmentations Provably Isolates Content from Style J von Kügelgen, Y Sharma, L Gresele, W Brendel, B Schölkopf, ... arXiv preprint arXiv:2106.04619, 2021		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 Workshop - Generalization beyond the training distribution in brains …, 2021		2021
Complex Interlinkages, Key Objectives and Nexuses Amongst the Sustainable Development Goals and Climate Change F Laumann, J von Kügelgen, TH Kanashiro Uehara, M Barahona SSRN preprint, 2021		2021
Kernel Two-Sample and Independence Tests for Non-Stationary Random Processes F Laumann, J von Kügelgen, M Barahona Engineering Proceedings 5 (1), 31, 2021		2021
A Mathematical Model for the Kinetics of the MalFGK (2) Maltose Transporter (vol 82, 62, 2020) RM Hiller, J von Kugelgen, H Bao, F Duong Van Hoa, EN Cytrynbaum BULLETIN OF MATHEMATICAL BIOLOGY 82 (7), 2020		2020

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