Sebastian Weichwald

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Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems & ELLIS Institute Tübingen; Professor at ETHVerified email at tuebingen.mpg.de
Moritz Grosse-WentrupUniversity of ViennaVerified email at univie.ac.at
Alexander G. ReisachUniversité Paris CitéVerified email at u-paris.fr
Christof SeilerMaastricht University / University Hospital Zurich / University of ZurichVerified email at usz.ch
Niklas KoepRWTH Aachen UniversityVerified email at rwth-aachen.de
Jonas PetersProfessor of Statistics, ETH ZurichVerified email at stat.math.ethz.ch
James TownsendUniversity of AmsterdamVerified email at uva.nl
Ozan ÖzdenizciMontanuniversität LeobenVerified email at unileoben.ac.at
Joris M. MooijProfessor in Mathematical Statistics, Korteweg-de Vries Institute, University of Amsterdam (NL)Verified email at uva.nl
Paul K RubensteinGoogle DeepMindVerified email at google.com
Dominik JanzingAmazon Development Center, TuebingenVerified email at amazon.com
Stephan BongersPostdoctoral Researcher, Delft University of TechnologyVerified email at uva.nl
Niklas PfisterAssociate Professor, University of CopenhagenVerified email at math.ku.dk
Nikolaj ThamsPhD studentVerified email at math.ku.dk
Antoine ChambazProfessor of Statistics, MAP5 (UMR CNRS 8145), Université de ParisVerified email at u-paris.fr
Peter BühlmannProfessor of Statistics and Mathematics, ETH ZurichVerified email at stat.math.ethz.ch
Tami Myriamenseignante-chercheuse en statistical learning, Université Paris Saclay, CentraleSupélecVerified email at centralesupelec.fr
Gherardo VarandoUniversitat de ValènciaVerified email at uv.es
Martin Emil JakobsenUniversity of CopenhagenVerified email at math.ku.dk
Phillip B MogensenPh.D. student in statisticsVerified email at math.ku.dk

Sebastian Weichwald

University of Copenhagen / formerly Max Planck Institute for Intelligent Systems, ETH Zurich

Verified email at math.ku.dk - Homepage


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Pymanopt: A Python Toolbox for Optimization on Manifolds using Automatic Differentiation J Townsend, N Koep, S Weichwald Journal of Machine Learning Research 17 (137), 1-5, 2016	343	2016
Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy to Game AG Reisach, C Seiler, S Weichwald Advances in Neural Information Processing Systems 34, 2021	194*	2021
Causal Consistency of Structural Equation Models PK Rubenstein, S Weichwald, S Bongers, JM Mooij, D Janzing, ... Conference on Uncertainty in Artificial Intelligence (UAI), 2017	144	2017
Causal interpretation rules for encoding and decoding models in neuroimaging S Weichwald, T Meyer, O Özdenizci, B Schölkopf, T Ball, ... NeuroImage 110, 48-59, 2015	125	2015
Causality in Cognitive Neuroscience: Concepts, Challenges, and Distributional Robustness S Weichwald, J Peters Journal of Cognitive Neuroscience, 1-21, 2020	55	2020
Causal structure learning from time series: Large regression coefficients may predict causal links better in practice than small p-values S Weichwald, ME Jakobsen, PB Mogensen, L Petersen, N Thams, ... Proceedings of the NeurIPS 2019 Competition and Demonstration Track …, 2020	41	2020
Compositional abstraction error and a category of causal models EF Rischel, S Weichwald Uncertainty in Artificial Intelligence, 1013-1023, 2021	34	2021
A Scale-Invariant Sorting Criterion to Find a Causal Order in Additive Noise Models AG Reisach, M Tami, C Seiler, A Chambaz, S Weichwald Advances in Neural Information Processing Systems 36, 2023	25*	2023
Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise N Pfister, S Weichwald, P Bühlmann, B Schölkopf Journal of Machine Learning Research 20 (147), 1-50, 2019	25	2019
Improving 1-year mortality prediction in ACS patients using machine learning S Weichwald, A Candreva, R Burkholz, R Klingenberg, L Räber, D Heg, ... European Heart Journal Acute Cardiovascular Care 10 (8), 855-865, 2021	20	2021
Identifying Causal Eﬀects using Instrumental Time Series: Nuisance IV and Correcting for the Past N Thams, R Søndergaard, S Weichwald, J Peters Journal of Machine Learning Research 25 (302), 1-51, 2024	17	2024
Absence of EEG correlates of self-referential processing depth in ALS. T Fomina, S Weichwald, M Synofzik, J Just, L Schöls, B Schölkopf, ... PloS one 12 (6), e0180136, 2017	15	2017
Causal and anti-causal learning in pattern recognition for neuroimaging S Weichwald, B Schölkopf, T Ball, M Grosse-Wentrup Pattern Recognition in Neuroimaging, 2014 International Workshop on, 1-4, 2014	15	2014
Personalized Brain-Computer Interface Models for Motor Rehabilitation AA Mastakouri, S Weichwald, O Özdenizci, T Meyer, B Schölkopf, ... Proceedings of the IEEE International Conference on Systems, Man, and …, 2017	11	2017
Learning by Doing: Controlling a Dynamical System using Causality, Control, and Reinforcement Learning S Weichwald, SW Mogensen, TE Lee, D Baumann, O Kroemer, I Guyon, ... NeurIPS 2021 Competitions and Demonstrations Track, 246-258, 2022	10	2022
Decoding index finger position from EEG using random forests S Weichwald, T Meyer, B Schölkopf, T Ball, M Grosse-Wentrup Cognitive Information Processing (CIP), 2014 4th International Workshop on, 1-6, 2014	8	2014
Adjustment Identification Distance: A gadjid for Causal Structure Learning L Henckel, T Würtzen, S Weichwald Uncertainty in Artificial Intelligence, 1569-1598, 2024	6	2024
MERLiN: Mixture Effect Recovery in Linear Networks S Weichwald, M Grosse-Wentrup, A Gretton IEEE Journal of Selected Topics in Signal Processing 10 (7), 1254-1266, 2016	6	2016
Recovery of non-linear cause-effect relationships from linearly mixed neuroimaging data S Weichwald, A Gretton, B Scholkopf, M Grosse-Wentrup 2016 International Workshop on Pattern Recognition in Neuroimaging (PRNI), 1-4, 2016	5	2016
Pragmatism and Variable Transformations in Causal Modelling S Weichwald ETH Zurich, 2019	4	2019

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