Quantile Regression Forests. - Nicolai Meinshausen (J. Mach. Learn. Res., 2006)

Relaxed Lasso. - Nicolai Meinshausen (Comput. Stat. Data Anal., 2007)

Discussion: Latent variable graphical model selection via convex optimization - Steffen L. Lauritzen; Nicolai Meinshausen (CoRR, 2012)

Sparse distance metric learning. - Tze Choy; Nicolai Meinshausen (Comput. Stat., 2014)

Random intersection trees. - Rajen Dinesh Shah; Nicolai Meinshausen (J. Mach. Learn. Res., 2014)

BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions. - Dominik Rothenhäusler; Christina Heinze; Jonas Peters; Nicolai Meinshausen (NIPS, 2015)

DUAL-LOCO: Distributing Statistical Estimation Using Random Projections. - Christina Heinze; Brian McWilliams; Nicolai Meinshausen (AISTATS, 2016)

Scalable Adaptive Stochastic Optimization Using Random Projections. - Gabriel Krummenacher; Brian McWilliams; Yannic Kilcher; Joachim M. Buhmann; Nicolai Meinshausen (NIPS, 2016)

Magging: Maximin Aggregation for Inhomogeneous Large-Scale Data. - Peter Bühlmann; Nicolai Meinshausen (Proc. IEEE, 2016)

Preserving Differential Privacy Between Features in Distributed Estimation. - Christina Heinze-Deml; Brian McWilliams; Nicolai Meinshausen (CoRR, 2017)

Guarding Against Adversarial Domain Shifts with Counterfactual Regularization. - Christina Heinze-Deml; Nicolai Meinshausen (CoRR, 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)

Causality from a Distributional Robustness Point of View. - Nicolai Meinshausen (DSW, 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)

Fair Data Adaptation with Quantile Preservation. - Drago Plecko; Nicolai Meinshausen (CoRR, 2019)

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; et al. (MIE, 2020)

Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression. - Domagoj Cevid; Loris Michel; Nicolai Meinshausen; Peter Bühlmann (CoRR, 2020)

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)

fairadapt: Causal Reasoning for Fair Data Pre-processing. - Drago Plecko; Nicolas Bennett; Nicolai Meinshausen (CoRR, 2021)

Conditional variance penalties and domain shift robustness. - Christina Heinze-Deml; Nicolai Meinshausen (Mach. Learn., 2021)

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 (NeurIPS, 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)

Robust detection and attribution of climate change under interventions. - Enikö Székely; Sebastian Sippel; Nicolai Meinshausen; Guillaume Obozinski; Reto Knutti (CoRR, 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)

Engression: Extrapolation for Nonlinear Regression? - Xinwei Shen 002; Nicolai Meinshausen (CoRR, 2023)

Confidence and Uncertainty Assessment for Distributional Random Forests. - Jeffrey Näf; Corinne Emmenegger; Peter Bühlmann; Nicolai Meinshausen (J. Mach. Learn. Res., 2023)

Distributional Principal Autoencoders. - Xinwei Shen 002; Nicolai Meinshausen (CoRR, 2024)

fairadapt: Causal Reasoning for Fair Data Preprocessing. - Drago Plecko; Nicolas Bennett; Nicolai Meinshausen (J. Stat. Softw., 2024)

Reverse Markov Learning: Multi-Step Generative Models for Complex Distributions. - Xinwei Shen; Nicolai Meinshausen; Tong Zhang (CoRR, 2025)

Representation Learning for Distributional Perturbation Extrapolation. - Julius von Kügelgen; Jakob Ketterer; Xinwei Shen; Nicolai Meinshausen; Jonas Peters (CoRR, 2025)