Christina Heinze-Deml 个人学术档案

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Nicolai MeinshausenProfessor of Statistics, ETH Zurich在 stat.math.ethz.ch 的电子邮件经过验证
Jonas PetersProfessor of Statistics, ETH Zurich在 stat.math.ethz.ch 的电子邮件经过验证
Brian McWilliamsGoogle DeepMind在 google.com 的电子邮件经过验证
Juan L. GamellaETH Zürich在 stat.math.ethz.ch 的电子邮件经过验证
Dominik RothenhäuslerStanford University在 stanford.edu 的电子邮件经过验证
Gabriel KrummenacherZühlke Engineering AG在 zuehlke.com 的电子邮件经过验证
Ioannis TsamardinosProfessor, Computer Science Department, University of Crete在 csd.uoc.gr 的电子邮件经过验证
Sofia TriantafillouUniversity of Crete, University of Pittsburgh, Foundation for Research and Technology - Hellas在 uoc.gr 的电子邮件经过验证
Vincenzo LaganiResearch Scientist, King Abdullah University of Science and Technology在 kaust.edu.sa 的电子邮件经过验证
Angelika SchmidtMerck KGaA, Neuroscience & Immunology在 merckgroup.com 的电子邮件经过验证
Jesper TegnérProfessor. Bio, CS, BioE, STAT,KAUST, Strategic Chaired Professor, Director, Karolinska Institutet在 kaust.edu.sa 的电子邮件经过验证
Fanny YangETH Zurich在 inf.ethz.ch 的电子邮件经过验证
Zuowen WangInstitute of Neuroinformatics, University of Zurich and ETH Zurich在 ethz.ch 的电子邮件经过验证
Peter BühlmannProfessor of Statistics and Mathematics, ETH Zurich在 stat.math.ethz.ch 的电子邮件经过验证
hastagiri vanchinathanAmazon Inc.在 amazon.com 的电子邮件经过验证
Angeline G PendergrassCornell University and National Center for Atmospheric Research在 ucar.edu 的电子邮件经过验证
Armeen TaebUniversity of Washington在 uw.edu 的电子邮件经过验证
Flavio LehnerCornell University and National Center for Atmospheric Research在 cornell.edu 的电子邮件经过验证
Sebastian SippelLeipzig University在 uni-leipzig.de 的电子邮件经过验证
Andrew C. MillerResearch Scientist, Apple在 apple.com 的电子邮件经过验证

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Christina Heinze-Deml

Apple AI/ML Research

在 apple.com 的电子邮件经过验证 - 首页

Causality Machine Learning Computational Statistics


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
Invariant causal prediction for nonlinear models C Heinze-Deml, J Peters, N Meinshausen Journal of Causal Inference 6 (2), 20170016, 2018	277	2018
Causal structure learning C Heinze-Deml, MH Maathuis, N Meinshausen Annual Review of Statistics and Its Application 5, 2017	233	2017
Conditional variance penalties and domain shift robustness C Heinze-Deml, N Meinshausen Machine Learning 110 (2), 303-348, 2021	166	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	81	2015
Random projections for large-scale regression GA Thanei, C Heinze, N Meinshausen Big and Complex Data Analysis: Methodologies and Applications, 51-68, 2017	57	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	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	48	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	41	2019
Active invariant causal prediction: Experiment selection through stability JL Gamella, C Heinze-Deml Advances in Neural Information Processing Systems (NeurIPS) 34, 2020, 2020	38	2020
Loco: Distributing ridge regression with random projections C Heinze, B McWilliams, N Meinshausen, G Krummenacher arXiv preprint arXiv:1406.3469, 2014	35*	2014
Think before you act: A simple baseline for compositional generalization C Heinze-Deml, D Bouchacourt arXiv preprint arXiv:2009.13962, 2020	15	2020
Preserving privacy between features in distributed estimation C Heinze‐Deml, B McWilliams, N Meinshausen stat 7 (1), e189, 2018	11	2018
CompareCausalNetworks: interface to diverse estimation methods of causal networks C Heinze-Deml, N Meinshausen R package, 2017	9*	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	8	2020
Perturbations and Causality in Gaussian Latent Variable Models A Taeb, JL Gamella, C Heinze-Deml, P Bühlmann arXiv preprint arXiv:2101.06950v3, 2021	5	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	3	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	3	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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