Christina Heinze-Deml
Christina Heinze-Deml
Apple AI/ML Research
Verified email at - Homepage
Cited by
Cited by
Invariant causal prediction for nonlinear models
C Heinze-Deml, J Peters, N Meinshausen
Journal of Causal Inference 6 (2), 20170016, 2018
Causal structure learning
C Heinze-Deml, MH Maathuis, N Meinshausen
Annual Review of Statistics and Its Application 5, 2017
Conditional variance penalties and domain shift robustness
C Heinze-Deml, N Meinshausen
Machine Learning 110 (2), 303-348, 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
Random projections for large-scale regression
GA Thanei, C Heinze, N Meinshausen
Big and Complex Data Analysis: Methodologies and Applications, 51-68, 2017
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), 12724, 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
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
Active invariant causal prediction: Experiment selection through stability
JL Gamella, C Heinze-Deml
Advances in Neural Information Processing Systems (NeurIPS) 34, 2020, 2020
Loco: Distributing ridge regression with random projections
C Heinze, B McWilliams, N Meinshausen, G Krummenacher
arXiv preprint arXiv:1406.3469, 2014
Think before you act: A simple baseline for compositional generalization
C Heinze-Deml, D Bouchacourt
arXiv preprint arXiv:2009.13962, 2020
Preserving privacy between features in distributed estimation
C Heinze‐Deml, B McWilliams, N Meinshausen
stat 7 (1), e189, 2018
CompareCausalNetworks: interface to diverse estimation methods of causal networks
C Heinze-Deml, N Meinshausen
R package, 2017
Latent linear adjustment autoencoders 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 Discussions 2020, 1-39, 2020
Perturbations and Causality in Gaussian Latent Variable Models
A Taeb, JL Gamella, C Heinze-Deml, P Bühlmann
arXiv preprint arXiv:2101.06950v3, 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
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
Transfer learning for estimating dynamic precipitation across different climate models
J Kuettel, S Sippel, C Heinze-Deml, R Knutti, N Meinshausen
EGU22, 2022
Package ‘CondIndTests’
C Heinze-Deml, J Peters, AMS Munk, MC Heinze-Deml, T LazyData
Computational Causality and Learning from Partitioned Data
C Heinze-Deml
ETH Zurich, 2018
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