Christina Heinze-Deml

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Nicolai MeinshausenProfessor of Statistics, ETH ZurichVerified email at stat.math.ethz.ch
Jonas PetersProfessor of Statistics, ETH ZurichVerified email at stat.math.ethz.ch
Brian McWilliamsResearch Scientist, GoogleVerified email at google.com
Marloes MaathuisETH ZurichVerified email at stat.math.ethz.ch
Juan L. GamellaETH ZürichVerified email at stat.math.ethz.ch
Gabriel KrummenacherZühlke Engineering AGVerified email at zuehlke.com
Dominik RothenhäuslerStanford UniversityVerified email at stanford.edu
Sebastian SippelETH ZurichVerified email at env.ethz.ch
Ioannis TsamardinosProfessor, Computer Science Department, University of CreteVerified email at csd.uoc.gr
Sofia TriantafillouUniversity of Crete, University of PittsburghVerified email at pitt.edu
Vincenzo LaganiResearch Scientist, King Abdullah University of Science and TechnologyVerified email at kaust.edu.sa
Angelika SchmidtBiomedical Center Munich, Institute for ImmunologyVerified email at bmc.med.lmu.de
Jesper TegnérProfessor. Bio, CS, BioE, STAT,KAUST, Strategic Chaired Professor, Karolinska InstitutetVerified email at kaust.edu.sa
Angeline G PendergrassCornell University and National Center for Atmospheric ResearchVerified email at ucar.edu
Fanny YangETH ZurichVerified email at inf.ethz.ch
Zuowen WangInstitute of Neuroinformatics, University of Zurich and ETH ZurichVerified email at ethz.ch
hastagiri vanchinathanThousandEyesVerified email at thousandeyes.com
Flavio LehnerCornell University and National Center for Atmospheric ResearchVerified email at cornell.edu
Diane BouchacourtFacebook AI ResearchVerified email at fb.com
Peter BühlmannProfessor of Statistics and Mathematics, ETH ZurichVerified email at stat.math.ethz.ch

Christina Heinze-Deml

Apple AI/ML Research

Verified email at apple.com - Homepage

Causality Machine Learning Computational Statistics


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Invariant causal prediction for nonlinear models C Heinze-Deml, J Peters, N Meinshausen Journal of Causal Inference 6 (2), 2018	190	2018
Causal structure learning C Heinze-Deml, MH Maathuis, N Meinshausen Annual Review of Statistics and Its Application 5, 2017	159	2017
Conditional variance penalties and domain shift robustness C Heinze-Deml, N Meinshausen Machine Learning 110 (2), 303-348, 2021	141*	2021
BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions D Rothenhäusler, C Heinze, J Peters, N Meinshausen Advances in Neural Information Processing Systems (NIPS) 29, 2015, 2015	59	2015
Random projections for large-scale regression GA Thanei, C Heinze, N Meinshausen Big and Complex Data Analysis: Methodologies and Applications, 51-68, 2017	48	2017
DUAL-LOCO: Distributing Statistical Estimation Using Random Projections C Heinze, B McWilliams, N Meinshausen Proceedings of the 19th International Conference on Artificial Intelligence …, 2016	42	2016
Predicting causal relationships from biological data: Applying automated causal discovery on mass cytometry data of human immune cells S Triantafillou, V Lagani, C Heinze-Deml, A Schmidt, J Tegner, ... Scientific reports 7 (1), 1-11, 2017	36	2017
Invariance-inducing regularization using worst-case transformations suffices to boost accuracy and spatial robustness F Yang, Z Wang, C Heinze-Deml Advances in Neural Information Processing Systems (NeurIPS) 33, 2019, 2019	32	2019
Loco: Distributing ridge regression with random projections C Heinze, B McWilliams, N Meinshausen, G Krummenacher arXiv preprint arXiv:1406.3469, 2014	32*	2014
Active invariant causal prediction: Experiment selection through stability JL Gamella, C Heinze-Deml Advances in Neural Information Processing Systems (NeurIPS) 34, 2020, 2020	24	2020
Think before you act: A simple baseline for compositional generalization C Heinze-Deml, D Bouchacourt arXiv preprint arXiv:2009.13962, 2020	12	2020
Preserving privacy between features in distributed estimation C Heinze‐Deml, B McWilliams, N Meinshausen stat 7 (1), e189, 2018	11	2018
Latent Linear Adjustment Autoencoder v1. 0: a novel method for estimating and emulating dynamic precipitation at high resolution C Heinze-Deml, S Sippel, AG Pendergrass, F Lehner, N Meinshausen Geoscientific Model Development 14 (8), 4977-4999, 2021	8	2021
CompareCausalNetworks: interface to diverse estimation methods of causal networks C Heinze-Deml, N Meinshausen R package, 2017	7*	2017
Perturbations and Causality in Gaussian Latent Variable Models A Taeb, JL Gamella, C Heinze-Deml, P Bühlmann arXiv preprint arXiv:2101.06950v3, 2021	4	2021
Considerations for Distribution Shift Robustness in Health A Blaas, A Miller, L Zappella, JH Jacobsen, C Heinze-Deml ICLR 2023 Workshop on Trustworthy Machine Learning for Healthcare, 2023		2023
Characterization and Greedy Learning of Gaussian Structural Causal Models under Unknown Interventions JL Gamella, A Taeb, C Heinze-Deml, P Bühlmann arXiv preprint arXiv:2211.14897, 2022		2022
Transfer learning for estimating dynamic precipitation across different climate models J Kuettel, S Sippel, C Heinze-Deml, R Knutti, N Meinshausen EGU22, 2022		2022
Package ‘CondIndTests’ C Heinze-Deml, J Peters, AMS Munk, MC Heinze-Deml, T LazyData		2019
Computational Causality and Learning from Partitioned Data C Heinze-Deml ETH Zurich, 2018		2018

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