Nicolai Meinshausen

Publication Activity (10 Years)

Years Active: 2006-2024
Publications (10 Years): 21

Top Topics

Distance Metric Learning

Random Projections

Linear Regression

Top Venues

J. Mach. Learn. Res.

Publications

Xinwei Shen, Nicolai Meinshausen
Distributional Principal Autoencoders. CoRR (2024)
Xinwei Shen, Nicolai Meinshausen
Engression: Extrapolation for Nonlinear Regression? CoRR (2023)
Domagoj Cevid, Loris Michel, Jeffrey Näf, Peter Bühlmann, Nicolai Meinshausen
Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression. J. Mach. Learn. Res. 23 (2022)
Andrew Jesson, Alyson Douglas, Peter Manshausen, Nicolai Meinshausen, Philip Stier, Yarin Gal, Uri Shalit
Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions. CoRR (2022)
Andrew Jesson, Alyson Douglas, Peter Manshausen, Maëlys Solal, Nicolai Meinshausen, Philip Stier, Yarin Gal, Uri Shalit
Scalable Sensitivity and Uncertainty Analyses for Causal-Effect Estimates of Continuous-Valued Interventions. NeurIPS (2022)
Enikö Székely, Sebastian Sippel, Nicolai Meinshausen, Guillaume Obozinski, Reto Knutti
Robust detection and attribution of climate change under interventions. CoRR (2022)
Michael Moor, Nicolas Bennett, Drago Plecko, Max Horn, Bastian Rieck, Nicolai Meinshausen, Peter Bühlmann, Karsten M. Borgwardt
Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning. CoRR (2021)
Drago Plecko, Nicolas Bennett, Nicolai Meinshausen
fairadapt: Causal Reasoning for Fair Data Pre-processing. CoRR (2021)
Christina Heinze-Deml, Nicolai Meinshausen
Conditional variance penalties and domain shift robustness. Mach. Learn. 110 (2) (2021)
Domagoj Cevid, Loris Michel, Nicolai Meinshausen, Peter Bühlmann
Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression. CoRR (2020)
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
SPHN/PHRT: Forming a Swiss-Wide Infrastructure for Data-Driven Sepsis Research. MIE (2020)
Drago Plecko, Nicolai Meinshausen
Fair Data Adaptation with Quantile Preservation. CoRR (2019)
Nicolai Meinshausen
Causality from a Distributional Robustness Point of View. DSW (2018)
Gian-Andrea Thanei, Nicolai Meinshausen, Rajen Dinesh Shah
The xyz algorithm for fast interaction search in high-dimensional data. J. Mach. Learn. Res. 19 (2018)
Christina Heinze-Deml, Nicolai Meinshausen
Guarding Against Adversarial Domain Shifts with Counterfactual Regularization. CoRR (2017)
Christina Heinze-Deml, Brian McWilliams, Nicolai Meinshausen
Preserving Differential Privacy Between Features in Distributed Estimation. CoRR (2017)
Rajen Dinesh Shah, Nicolai Meinshausen
On $b$-bit Min-wise Hashing for Large-scale Regression and Classification with Sparse Data. J. Mach. Learn. Res. 18 (2017)
Christina Heinze, Brian McWilliams, Nicolai Meinshausen
DUAL-LOCO: Distributing Statistical Estimation Using Random Projections. AISTATS (2016)
Gabriel Krummenacher, Brian McWilliams, Yannic Kilcher, Joachim M. Buhmann, Nicolai Meinshausen
Scalable Adaptive Stochastic Optimization Using Random Projections. NIPS (2016)
Gabriel Krummenacher, Brian McWilliams, Yannic Kilcher, Joachim M. Buhmann, Nicolai Meinshausen
Scalable Adaptive Stochastic Optimization Using Random Projections. CoRR (2016)
Peter Bühlmann, Nicolai Meinshausen
Magging: Maximin Aggregation for Inhomogeneous Large-Scale Data. Proc. IEEE 104 (1) (2016)
Dominik Rothenhäusler, Christina Heinze, Jonas Peters, Nicolai Meinshausen
BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions. NIPS (2015)
Christina Heinze, Brian McWilliams, Nicolai Meinshausen
DUAL-LOCO: Distributing Statistical Estimation Using Random Projections. CoRR (2015)
Rajen Dinesh Shah, Nicolai Meinshausen
Random intersection trees. J. Mach. Learn. Res. 15 (1) (2014)
Tze Choy, Nicolai Meinshausen
Sparse distance metric learning. Comput. Stat. 29 (3) (2014)
Steffen L. Lauritzen, Nicolai Meinshausen
Discussion: Latent variable graphical model selection via convex optimization CoRR (2012)
Nicolai Meinshausen
Relaxed Lasso. Comput. Stat. Data Anal. 52 (1) (2007)
Nicolai Meinshausen
Quantile Regression Forests. J. Mach. Learn. Res. 7 (2006)