Nicolai Meinshausen

33 papers A* 1A 1Journal 27Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Anti-causal domain generalization: Leveraging unlabeled data. CoRR Sorawit Saengkyongam, Juan L. Gamella, Andrew C. Miller, Jonas Peters, Nicolai Meinshausen, Christina Heinze-Deml
2025	J	`jnl`	Representation Learning for Distributional Perturbation Extrapolation. CoRR Julius von Kügelgen, Jakob Ketterer, Xinwei Shen, Nicolai Meinshausen, Jonas Peters
2025	J	`jnl`	Reverse Markov Learning: Multi-Step Generative Models for Complex Distributions. CoRR Xinwei Shen, Nicolai Meinshausen, Tong Zhang
2024	J	`jnl`	Distributional Principal Autoencoders. CoRR Xinwei Shen, Nicolai Meinshausen
2024	J	`jnl`	fairadapt: Causal Reasoning for Fair Data Preprocessing. J. Stat. Softw. Drago Plecko, Nicolas Bennett, Nicolai Meinshausen
2023	J	`jnl`	Confidence and Uncertainty Assessment for Distributional Random Forests. J. Mach. Learn. Res. Jeffrey Näf, Corinne Emmenegger, Peter Bühlmann, Nicolai Meinshausen
2023	J	`jnl`	Engression: Extrapolation for Nonlinear Regression? CoRR Xinwei Shen, Nicolai Meinshausen
2022	J	`jnl`	Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression. J. Mach. Learn. Res. Domagoj Cevid, Loris Michel, Jeffrey Näf, Peter Bühlmann, Nicolai Meinshausen
2022	J	`jnl`	Robust detection and attribution of climate change under interventions. CoRR Enikö Székely, Sebastian Sippel, Nicolai Meinshausen, Guillaume Obozinski, Reto Knutti
2022	A*	`conf`	Scalable Sensitivity and Uncertainty Analyses for Causal-Effect Estimates of Continuous-Valued Interventions. NeurIPS Andrew Jesson, Alyson Douglas, Peter Manshausen, Maëlys Solal, Nicolai Meinshausen, Philip Stier, Yarin Gal, Uri Shalit
2022	J	`jnl`	Scalable Sensitivity and Uncertainty Analysis for Causal-Effect Estimates of Continuous-Valued Interventions. CoRR Andrew Jesson, Alyson Douglas, Peter Manshausen, Nicolai Meinshausen, Philip Stier, Yarin Gal, Uri Shalit
2021	J	`jnl`	Conditional variance penalties and domain shift robustness. Mach. Learn. Christina Heinze-Deml, Nicolai Meinshausen
2021	J	`jnl`	Predicting sepsis in multi-site, multi-national intensive care cohorts using deep learning. CoRR Michael Moor, Nicolas Bennett, Drago Plecko, Max Horn, Bastian Rieck, Nicolai Meinshausen, Peter Bühlmann, Karsten M. Borgwardt
2021	J	`jnl`	fairadapt: Causal Reasoning for Fair Data Pre-processing. CoRR Drago Plecko, Nicolas Bennett, Nicolai Meinshausen
2020	J	`jnl`	Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression. CoRR Domagoj Cevid, Loris Michel, Nicolai Meinshausen, Peter Bühlmann
2020	—	`conf`	SPHN/PHRT: Forming a Swiss-Wide Infrastructure for Data-Driven Sepsis Research. MIE 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
2019	J	`jnl`	Fair Data Adaptation with Quantile Preservation. CoRR Drago Plecko, Nicolai Meinshausen
2018	—	`conf`	Causality from a Distributional Robustness Point of View. DSW Nicolai Meinshausen
2018	J	`jnl`	The xyz algorithm for fast interaction search in high-dimensional data. J. Mach. Learn. Res. Gian-Andrea Thanei, Nicolai Meinshausen, Rajen Dinesh Shah
2017	J	`jnl`	Guarding Against Adversarial Domain Shifts with Counterfactual Regularization. CoRR Christina Heinze-Deml, Nicolai Meinshausen
2017	J	`jnl`	On $b$-bit Min-wise Hashing for Large-scale Regression and Classification with Sparse Data. J. Mach. Learn. Res. Rajen Dinesh Shah, Nicolai Meinshausen
2017	J	`jnl`	Preserving Differential Privacy Between Features in Distributed Estimation. CoRR Christina Heinze-Deml, Brian McWilliams, Nicolai Meinshausen
2016	A	`conf`	DUAL-LOCO: Distributing Statistical Estimation Using Random Projections. AISTATS Christina Heinze, Brian McWilliams, Nicolai Meinshausen
2016	J	`jnl`	Magging: Maximin Aggregation for Inhomogeneous Large-Scale Data. Proc. IEEE Peter Bühlmann, Nicolai Meinshausen
2016	—	`conf`	Scalable Adaptive Stochastic Optimization Using Random Projections. NIPS Gabriel Krummenacher, Brian McWilliams, Yannic Kilcher, Joachim M. Buhmann, Nicolai Meinshausen
2016	J	`jnl`	Scalable Adaptive Stochastic Optimization Using Random Projections. CoRR Gabriel Krummenacher, Brian McWilliams, Yannic Kilcher, Joachim M. Buhmann, Nicolai Meinshausen
2015	—	`conf`	BACKSHIFT: Learning causal cyclic graphs from unknown shift interventions. NIPS Dominik Rothenhäusler, Christina Heinze, Jonas Peters, Nicolai Meinshausen
2015	J	`jnl`	DUAL-LOCO: Distributing Statistical Estimation Using Random Projections. CoRR Christina Heinze, Brian McWilliams, Nicolai Meinshausen
2014	J	`jnl`	Random intersection trees. J. Mach. Learn. Res. Rajen Dinesh Shah, Nicolai Meinshausen
2014	J	`jnl`	Sparse distance metric learning. Comput. Stat. Tze Choy, Nicolai Meinshausen
2012	J	`jnl`	Discussion: Latent variable graphical model selection via convex optimization CoRR Steffen L. Lauritzen, Nicolai Meinshausen
2007	J	`jnl`	Relaxed Lasso. Comput. Stat. Data Anal. Nicolai Meinshausen
2006	J	`jnl`	Quantile Regression Forests. J. Mach. Learn. Res. Nicolai Meinshausen