Sebastian Weichwald

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Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems; Professor at ETH Zürich, and DistinguishedVerified email at tuebingen.mpg.de
Moritz Grosse-WentrupUniversity of ViennaVerified email at univie.ac.at
Tonio BallNeuromedical AI Lab Uni Freiburg, NeuroMentum AI (CEO)Verified email at uniklinik-freiburg.de
Niklas KoepRWTH Aachen UniversityVerified email at rwth-aachen.de
Ozan ÖzdenizciGraz University of TechnologyVerified email at tugraz.at
James TownsendUniversity of AmsterdamVerified email at uva.nl
Jonas PetersProfessor of Statistics, University of CopenhagenVerified email at math.ku.dk
Alexander ReisachUniversité Paris CitéVerified email at u-paris.fr
Christof SeilerMaastricht UniversityVerified email at maastrichtuniversity.nl
Niklas PfisterAssistant Professor, University of CopenhagenVerified email at math.ku.dk
Joris M. MooijProfessor in Mathematical Statistics, Korteweg-de Vries Institute, University of Amsterdam (NL)Verified email at uva.nl
Paul K RubensteinGoogleVerified email at google.com
Dominik JanzingAmazon Development Center, TuebingenVerified email at amazon.com
Stephan BongersPh.D. Candidate, University of AmsterdamVerified email at uva.nl
Peter BühlmannProfessor of Statistics and Mathematics, ETH ZurichVerified email at stat.math.ethz.ch
Nikolaj ThamsPhD studentVerified email at math.ku.dk
Arthur GrettonGatsby Computational Neuroscience Unit, UCLVerified email at gatsby.ucl.ac.uk
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	206	2016
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	97	2015
Causal Consistency of Structural Equation Models PK Rubenstein, S Weichwald, S Bongers, JM Mooij, D Janzing, ... Conference on Uncertainty in Artificial Intelligence (UAI), 2017	60	2017
Beware of the Simulated DAG! Causal Discovery Benchmarks May Be Easy to Game A Reisach, C Seiler, S Weichwald Advances in Neural Information Processing Systems 34, 2021	36*	2021
Causality in Cognitive Neuroscience: Concepts, Challenges, and Distributional Robustness S Weichwald, J Peters Journal of Cognitive Neuroscience, 1-21, 2020	18*	2020
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	17	2019
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	16	2020
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	13	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	12	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	9	2017
Compositional abstraction error and a category of causal models EF Rischel, S Weichwald Uncertainty in Artificial Intelligence, 1013-1023, 2021	8	2021
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	7	2014
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	5	2021
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	5	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
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, ... arXiv preprint arXiv:2202.06052, 2022	3	2022
A note on the expected minimum error probability in equientropic channels S Weichwald, T Fomina, B Schölkopf, M Grosse-Wentrup arXiv preprint arXiv:1605.07094, 2016	2*	2016
Identifying Causal Effects using Instrumental Time Series: Nuisance IV and Correcting for the Past N Thams, R Søndergaard, S Weichwald, J Peters arXiv preprint arXiv:2203.06056, 2022	1	2022
The right tool for the right question --- beyond the encoding versus decoding dichotomy S Weichwald, M Grosse-Wentrup arXiv preprint arXiv:1704.08851, 2017	1	2017

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