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Peter Bühlmann
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Publications
- 2023
- [c4]Alexander Immer, Christoph Schultheiss, Julia E. Vogt, Bernhard Schölkopf, Peter Bühlmann, Alexander Marx:
On the Identifiability and Estimation of Causal Location-Scale Noise Models. ICML 2023: 14316-14332 - 2022
- [j36]Kim Philipp Jablonski, Martin Pirkl, Domagoj Cevid, Peter Bühlmann, Niko Beerenwinkel:
Identifying cancer pathway dysregulations using differential causal effects. Bioinform. 38(6): 1550-1559 (2022) - [j35]Martin Emil Jakobsen, Rajen Dinesh Shah, Peter Bühlmann, Jonas Peters:
Structure Learning for Directed Trees. J. Mach. Learn. Res. 23: 159:1-159:97 (2022) - [j33]Domagoj Cevid, Loris Michel, Jeffrey Näf, Peter Bühlmann, Nicolai Meinshausen:
Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression. J. Mach. Learn. Res. 23: 333:1-333:79 (2022) - [i11]Alexander Immer, Christoph Schultheiss, Julia E. Vogt, Bernhard Schölkopf, Peter Bühlmann, Alexander Marx:
On the Identifiability and Estimation of Causal Location-Scale Noise Models. CoRR abs/2210.09054 (2022) - 2021
- [i10]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 abs/2107.05230 (2021) - [i9]Martin Emil Jakobsen, Rajen Dinesh Shah, Peter Bühlmann, Jonas Peters:
Structure Learning for Directed Trees. CoRR abs/2108.08871 (2021) - 2020
- [j29]Claude Renaux, Laura Buzdugan, Markus Kalisch, Peter Bühlmann:
Hierarchical inference for genome-wide association studies: a view on methodology with software. Comput. Stat. 35(1): 1-40 (2020) - [j28]Claude Renaux, Laura Buzdugan, Markus Kalisch, Peter Bühlmann:
Rejoinder on: Hierarchical inference for genome-wide association studies: a view on methodology with software. Comput. Stat. 35(1): 59-67 (2020) - [c3]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: 1163-1167 - [i8]Domagoj Cevid, Loris Michel, Nicolai Meinshausen, Peter Bühlmann:
Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression. CoRR abs/2005.14458 (2020) - 2019
- [j27]Niklas Pfister, Sebastian Weichwald, Peter Bühlmann, Bernhard Schölkopf:
Robustifying Independent Component Analysis by Adjusting for Group-Wise Stationary Noise. J. Mach. Learn. Res. 20: 147:1-147:50 (2019) - 2018
- [i4]Niklas Pfister, Sebastian Weichwald, Peter Bühlmann, Bernhard Schölkopf:
groupICA: Independent component analysis for grouped data. CoRR abs/1806.01094 (2018) - 2016
- [j26]Laura Buzdugan, Markus Kalisch, Arcadi Navarro, Daniel Schunk, Ernst Fehr, Peter Bühlmann:
Assessing statistical significance in multivariable genome wide association analysis. Bioinform. 32(13): 1990-2000 (2016) - [j25]Peter Bühlmann, Nicolai Meinshausen:
Magging: Maximin Aggregation for Inhomogeneous Large-Scale Data. Proc. IEEE 104(1): 126-135 (2016) - 2015
- [j24]Jonas Peters, Peter Bühlmann:
Structural Intervention Distance for Evaluating Causal Graphs. Neural Comput. 27(3): 771-799 (2015) - 2014
- [j22]Shaowei Lin, Caroline Uhler, Bernd Sturmfels, Peter Bühlmann:
Hypersurfaces and Their Singularities in Partial Correlation Testing. Found. Comput. Math. 14(5): 1079-1116 (2014) - [j20]Nicolas Städler, Daniel J. Stekhoven, Peter Bühlmann:
Pattern alternating maximization algorithm for missing data in high-dimensional problems. J. Mach. Learn. Res. 15(1): 1903-1928 (2014) - [j19]Po-Ling Loh, Peter Bühlmann:
High-dimensional learning of linear causal networks via inverse covariance estimation. J. Mach. Learn. Res. 15(1): 3065-3105 (2014) - 2013
- [i3]Peter Bühlmann, Jonas Peters, Jan Ernest:
CAM: Causal Additive Models, high-dimensional order search and penalized regression. CoRR abs/1310.1533 (2013) - 2012
- [j16]Daniel J. Stekhoven, Peter Bühlmann:
MissForest - non-parametric missing value imputation for mixed-type data. Bioinform. 28(1): 112-118 (2012) - [j15]Daniel J. Stekhoven, Izabel Moraes, Gardar Sveinbjörnsson, Lars Hennig, Marloes H. Maathuis, Peter Bühlmann:
Causal stability ranking. Bioinform. 28(21): 2819-2823 (2012) - [j13]Nicolas Städler, Peter Bühlmann:
Missing values: sparse inverse covariance estimation and an extension to sparse regression. Stat. Comput. 22(1): 219-235 (2012) - 2008
- [j9]Daniel Schöner, Markus Kalisch, Christian Leisner, Lukas Meier, Marc Sohrmann, Mahamadou Faty, Yves Barral, Matthias Peter, Wilhelm Gruissem, Peter Bühlmann:
Annotating novel genes by integrating synthetic lethals and genomic information. BMC Syst. Biol. 2: 3 (2008) - [j8]Roman Werner Lutz, Markus Kalisch, Peter Bühlmann:
Robustified L2 boosting. Comput. Stat. Data Anal. 52(7): 3331-3341 (2008) - 2007
- [j5]Markus Kalisch, Peter Bühlmann:
Estimating High-Dimensional Directed Acyclic Graphs with the PC-Algorithm. J. Mach. Learn. Res. 8: 613-636 (2007) - 2006
- [j4]Amela Prelic, Stefan Bleuler, Philip Zimmermann, Anja Wille, Peter Bühlmann, Wilhelm Gruissem, Lars Hennig, Lothar Thiele, Eckart Zitzler:
A systematic comparison and evaluation of biclustering methods for gene expression data. Bioinform. 22(9): 1122-1129 (2006)
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