Luigi Gresele

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Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems; Professor at ETH ZürichVerified email at tuebingen.mpg.de
Julius von KügelgenMax Planck Institute for Intelligent Systems Tübingen & University of CambridgeVerified email at tuebingen.mpg.de
Michel BesserveMax Planck Institute for Intelligent SystemsVerified email at tuebingen.mpg.de
Francesco LocatelloSenior Applied Scientist, Amazon AWSVerified email at amazon.com
Wieland BrendelResearch Group Leader, Max Planck Institute for Intelligent SystemsVerified email at tuebingen.mpg.de
Yash SharmaUniversity of Tübingen, Max Planck Institute for Intelligent SystemsVerified email at bethgelab.org
Giambattista ParascandoloOpenAIVerified email at openai.com
Antonio OrvietoPhD student, ETH ZürichVerified email at ethz.ch
Alexander NeitzMax Planck Institute for Intelligent SystemVerified email at tuebingen.mpg.de
Aapo HyvarinenUniversity of HelsinkiVerified email at helsinki.fi
Vincent StimperUniversity of Cambridge, Max Planck Institute for Intelligent SystemsVerified email at cam.ac.uk
Paul K RubensteinGoogleVerified email at google.com
Arash MehrjouETH Zürich - Max Planck InstituteVerified email at tuebingen.mpg.de
Bertrand ThirionInriaVerified email at inria.fr
Alexandre GramfortMetaVerified email at meta.com
Pierre AblinApple Machine Learning ResearchVerified email at apple.com
Adrián JavaloyPhD student, University of SaarlandVerified email at uni-saarland.de
Krikamol MuandetCISPA - Helmholtz Center for Information SecurityVerified email at cispa.de
Matteo MarsiliSenior reserch scientist, Abdus Salam ICTP, TriesteVerified email at ictp.it
Elke KirschbaumAmazon Development Center, TuebingenVerified email at amazon.com

Luigi Gresele

Max Planck Institute for Intelligent Systems, Tübingen

Verified email at tuebingen.mpg.de - Homepage

Machine learning Causal Inference


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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, ... Advances in Neural Information Processing Systems 34, 2021	116	2021
Learning explanations that are hard to vary G Parascandolo, A Neitz, A Orvieto, L Gresele, B Schölkopf International Conference on Learning Representations (2021), 2020	88	2020
The Incomplete Rosetta Stone problem: identifiability results for multi-view nonlinear ICA L Gresele, PK Rubenstein, A Mehrjou, F Locatello, B Schölkopf Uncertainty in Artificial Intelligence, 217-227, 2019	50	2019
Independent mechanism analysis, a new concept? L Gresele, J von Kügelgen, V Stimper, B Schölkopf, M Besserve Advances in Neural Information Processing Systems 34, 2021	28	2021
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, 2021	22	2021
Modeling Shared Responses in Neuroimaging Studies through MultiView ICA H Richard, L Gresele, A Hyvärinen, B Thirion, A Gramfort, P Ablin Advances in Neural Information Processing Systems 33, 2020	17	2020
Relative gradient optimization of the Jacobian term in unsupervised deep learning L Gresele, G Fissore, A Javaloy, B Schölkopf, A Hyvärinen Advances in Neural Information Processing Systems 33, 2020	14	2020
On maximum entropy and inference L Gresele, M Marsili Entropy 19 (12), 642, 2017	10	2017
Causal Inference Through the Structural Causal Marginal Problem L Gresele, J Von Kügelgen, J Kübler, E Kirschbaum, B Schölkopf, ... International Conference on Machine Learning, 7793-7824, 2022	4	2022
Embrace the Gap: VAEs Perform Independent Mechanism Analysis P Reizinger, L Gresele, J Brady, J von Kügelgen, D Zietlow, B Schölkopf, ... Advances in Neural Information Processing Systems 35, 2022	3	2022
Privacy-Preserving Causal Inference via Inverse Probability Weighting SK Lee, L Gresele, M Park, K Muandet NeurIPS 2019 Workshop on Privacy in Machine Learning (PriML 2019), 2019	3*	2019
Evaluating vaccine allocation strategies using simulation-assisted causal modelling A Kekic, J Dehning, L Gresele, J von Kügelgen, V Priesemann, ...		2022
Probing the Robustness of Independent Mechanism Analysis for Representation Learning J Sliwa, S Ghosh, V Stimper, L Gresele, B Schölkopf UAI 2022 Workshop on Causal Representation Learning, 2022		2022
On Pitfalls of Identifiability in Unsupervised Learning. A Note on: "Desiderata for Representation Learning: A Causal Perspective" S Ghosh, L Gresele, J von Kügelgen, M Besserve, B Schölkopf arXiv preprint arXiv:2202.06844, 2022		2022
Model based fMRI denoising L Gresele, K Scheffler, B Schölkopf, G Lohmann 25th Annual Meeting of the Organization for Human Brain Mapping (OHBM 2019), 2019		2019

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