Nicolai Meinshausen

According to our database, Nicolai Meinshausen authored at least 25 papers between 2006 and 2023.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2023

Engression: Extrapolation for Nonlinear Regression?

Xinwei Shen, Nicolai Meinshausen

CoRR, 2023

2022

Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression.

Domagoj Cevid, Loris Michel, Jeffrey Näf, Peter Bühlmann, Nicolai Meinshausen

J. Mach. Learn. Res., 2022

Robust detection and attribution of climate change under interventions.

Enikö Székely, Sebastian Sippel, Nicolai Meinshausen, Guillaume Obozinski, Reto Knutti

CoRR, 2022

Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions.

Andrew Jesson, Alyson Douglas, Peter Manshausen, Nicolai Meinshausen, Philip Stier, Yarin Gal, Uri Shalit

CoRR, 2022

Scalable Sensitivity and Uncertainty Analyses for Causal-Effect Estimates of Continuous-Valued Interventions.

Andrew Jesson, Alyson Douglas, Peter Manshausen, Maëlys Solal, Nicolai Meinshausen, Philip Stier, Yarin Gal, Uri Shalit

Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022

2021

Conditional variance penalties and domain shift robustness.

Christina Heinze-Deml, Nicolai Meinshausen

Mach. Learn., 2021

fairadapt: Causal Reasoning for Fair Data Pre-processing.

Drago Plecko, Nicolas Bennett, Nicolai Meinshausen

CoRR, 2021

Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning.

Michael Moor, Nicolas Bennett, Drago Plecko, Max Horn, Bastian Rieck, Nicolai Meinshausen, Peter Bühlmann, Karsten M. Borgwardt

CoRR, 2021

2020

Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression.

Domagoj Cevid, Loris Michel, Nicolai Meinshausen, Peter Bühlmann

CoRR, 2020

SPHN/PHRT: Forming a Swiss-Wide Infrastructure for Data-Driven Sepsis Research.

Adrian Egli, Manuel Battegay, Andrea C. Büchler, Peter Bühlmann, Thierry Calandra, Philippe Eckert, Hansjakob Furrer, Gilbert Greub, Stephan M. Jakob, Laurent Kaiser, Stephen L. Leib, Stephan Marsch, Nicolai Meinshausen, Jean-Luc Pagani, Jerome Pugin, Gunnar Rätsch, Jacques Schrenzel, Reto Schüpbach, Martin Siegemund, Nicola Zamboni, Reinhard Zbinden, Annelies Zinkernagel, Karsten M. Borgwardt

Proceedings of the Digital Personalized Health and Medicine - Proceedings of MIE 2020, Medical Informatics Europe, Geneva, Switzerland, April 28, 2020

2019

Fair Data Adaptation with Quantile Preservation.

Drago Plecko, Nicolai Meinshausen

CoRR, 2019

2018

The xyz algorithm for fast interaction search in high-dimensional data.

Gian-Andrea Thanei, Nicolai Meinshausen, Rajen Dinesh Shah

J. Mach. Learn. Res., 2018

Causality from a Distributional Robustness Point of View.

Nicolai Meinshausen

Proceedings of the 2018 IEEE Data Science Workshop, 2018

2017

On $b$-bit Min-wise Hashing for Large-scale Regression and Classification with Sparse Data.

Rajen Dinesh Shah, Nicolai Meinshausen

J. Mach. Learn. Res., 2017

Guarding Against Adversarial Domain Shifts with Counterfactual Regularization.

Christina Heinze-Deml, Nicolai Meinshausen

CoRR, 2017

Preserving Differential Privacy Between Features in Distributed Estimation.

Christina Heinze-Deml, Brian McWilliams, Nicolai Meinshausen

CoRR, 2017

2016

Magging: Maximin Aggregation for Inhomogeneous Large-Scale Data.

Peter Bühlmann, Nicolai Meinshausen

Proc. IEEE, 2016

Scalable Adaptive Stochastic Optimization Using Random Projections.

Gabriel Krummenacher, Brian McWilliams, Yannic Kilcher, Joachim M. Buhmann, Nicolai Meinshausen

Proceedings of the Advances in Neural Information Processing Systems 29: Annual Conference on Neural Information Processing Systems 2016, 2016

DUAL-LOCO: Distributing Statistical Estimation Using Random Projections.

Christina Heinze, Brian McWilliams, Nicolai Meinshausen

Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, 2016

2015

BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions.

Dominik Rothenhäusler, Christina Heinze, Jonas Peters, Nicolai Meinshausen

Proceedings of the Advances in Neural Information Processing Systems 28: Annual Conference on Neural Information Processing Systems 2015, 2015

2014

Random intersection trees.

Rajen Dinesh Shah, Nicolai Meinshausen

J. Mach. Learn. Res., 2014

Sparse distance metric learning.

Tze Choy, Nicolai Meinshausen

Comput. Stat., 2014

2012

Discussion: Latent variable graphical model selection via convex optimization

Steffen L. Lauritzen, Nicolai Meinshausen

CoRR, 2012

2007

Relaxed Lasso.

Nicolai Meinshausen

Comput. Stat. Data Anal., 2007

2006

Quantile Regression Forests.

Nicolai Meinshausen

J. Mach. Learn. Res., 2006