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Peter Bühlmann
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- affiliation: ETH Zürich, Switzerland
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2020 – today
- 2024
- [j40]Christoph Schultheiss, Peter Bühlmann:
Assessing the Overall and Partial Causal Well-Specification of Nonlinear Additive Noise Models. J. Mach. Learn. Res. 25: 159:1-159:41 (2024) - [i17]Juan L. Gamella, Jonas Peters, Peter Bühlmann:
The Causal Chambers: Real Physical Systems as a Testbed for AI Methodology. CoRR abs/2404.11341 (2024) - [i16]Yihong Gu, Cong Fang, Peter Bühlmann, Jianqing Fan:
Causality Pursuit from Heterogeneous Environments via Neural Adversarial Invariance Learning. CoRR abs/2405.04715 (2024) - 2023
- [j39]Malte Londschien, Peter Bühlmann, Solt Kovács:
Random Forests for Change Point Detection. J. Mach. Learn. Res. 24: 216:1-216:45 (2023) - [j38]Jeffrey Näf
, Corinne Emmenegger, Peter Bühlmann, Nicolai Meinshausen:
Confidence and Uncertainty Assessment for Distributional Random Forests. J. Mach. Learn. Res. 24: 366:1-366:77 (2023) - [j37]Fernando Marmolejo-Ramos
Distributional regression modeling via generalized additive models for location, scale, and shape: An overview through a data set from learning analytics. WIREs Data Mining Knowl. Discov. 13(1) (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 - [i15]Michael J. Zellinger, Peter Bühlmann:
repliclust: Synthetic Data for Cluster Analysis. CoRR abs/2303.14301 (2023) - [i14]Xinwei Shen, Peter Bühlmann, Armeen Taeb:
Causality-oriented robustness: exploiting general additive interventions. CoRR abs/2307.10299 (2023) - [i13]Zhenyu Wang, Peter Bühlmann, Zijian Guo:
Distributionally Robust Machine Learning with Multi-source Data. CoRR abs/2309.02211 (2023) - [i12]Alexander Henzi, Xinwei Shen, Michael Law, Peter Bühlmann:
Invariant Probabilistic Prediction. CoRR abs/2309.10083 (2023) - 2022
- [j36]Kim Philipp Jablonski
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) - [j34]Cyrill Scheidegger, Julia Hörrmann, Peter Bühlmann:
The Weighted Generalised Covariance Measure. J. Mach. Learn. Res. 23: 273:1-273:68 (2022) - [j33]Domagoj Cevid, Loris Michel, Jeffrey Näf
Distributional Random Forests: Heterogeneity Adjustment and Multivariate Distributional Regression. J. Mach. Learn. Res. 23: 333:1-333:79 (2022) - [j32]Lucas Kook
Distributional anchor regression. Stat. Comput. 32(3): 39 (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
- [j31]Malte Londschien
Change-Point Detection for Graphical Models in the Presence of Missing Values. J. Comput. Graph. Stat. 30(3): 768-779 (2021) - [j30]Yuansi Chen, Peter Bühlmann:
Domain adaptation under structural causal models. J. Mach. Learn. Res. 22: 261:1-261:80 (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
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) - [i7]Solt Kovács, Housen Li, Lorenz Haubner, Axel Munk, Peter Bühlmann:
Optimistic search strategy: Change point detection for large-scale data via adaptive logarithmic queries. CoRR abs/2010.10194 (2020) - [i6]Yuansi Chen, Peter Bühlmann:
Domain adaptation under structural causal models. CoRR abs/2010.15764 (2020)
2010 – 2019
- 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) - [i5]Malte Londschien, Solt Kovács, Peter Bühlmann:
Change point detection for graphical models in presence of missing values. CoRR abs/1907.05409 (2019) - 2018
- [i4]Niklas Pfister, Sebastian Weichwald
groupICA: Independent component analysis for grouped data. CoRR abs/1806.01094 (2018) - 2016
- [j26]Laura Buzdugan, Markus Kalisch, Arcadi Navarro
Assessing statistical significance in multivariable genome wide association analysis. Bioinform. 32(13): 1990-2000 (2016) - [j25]Peter Bühlmann
Magging: Maximin Aggregation for Inhomogeneous Large-Scale Data. Proc. IEEE 104(1): 126-135 (2016) - [e1]Peter Bühlmann, Petros Drineas, Michael J. Kane, Mark J. van der Laan:
Handbook of Big Data. Chapman and Hall/CRC 2016, ISBN 978-1-4822-4907-1 [contents] - 2015
- [j24]Jonas Peters
Structural Intervention Distance for Evaluating Causal Graphs. Neural Comput. 27(3): 771-799 (2015) - 2014
- [j23]Peter Bühlmann
High-dimensional variable screening and bias in subsequent inference, with an empirical comparison. Comput. Stat. 29(3): 407-430 (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) - [j21]Alain Hauser
Two optimal strategies for active learning of causal models from interventional data. Int. J. Approx. Reason. 55(4): 926-939 (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
- [j18]Bernd Fellinghauer, Peter Bühlmann
Stable graphical model estimation with Random Forests for discrete, continuous, and mixed variables. Comput. Stat. Data Anal. 64: 132-152 (2013) - [j17]Peter Bühlmann
Causal statistical inference in high dimensions. Math. Methods Oper. Res. 77(3): 357-370 (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
Causal stability ranking. Bioinform. 28(21): 2819-2823 (2012) - [j14]Alain Hauser, Peter Bühlmann:
Characterization and greedy learning of interventional Markov equivalence classes of directed acyclic graphs. J. Mach. Learn. Res. 13: 2409-2464 (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) - [i2]Alain Hauser, Peter Bühlmann:
Two Optimal Strategies for Active Learning of Causal Models from Interventions. CoRR abs/1205.4174 (2012) - 2011
- [j12]Shuheng Zhou, Philipp Rütimann, Min Xu, Peter Bühlmann:
High-dimensional Covariance Estimation Based On Gaussian Graphical Models. J. Mach. Learn. Res. 12: 2975-3026 (2011) - [c2]Alain Hauser, Peter Bühlmann:
Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs (Abstract). UAI 2011: 851 - [i1]Alain Hauser
Characterization and Greedy Learning of Interventional Markov Equivalence Classes of Directed Acyclic Graphs. CoRR abs/1104.2808 (2011) - 2010
- [j11]Torsten Hothorn, Peter Bühlmann, Thomas Kneib, Matthias Schmid, Benjamin Hofner:
Model-based Boosting 2.0. J. Mach. Learn. Res. 11: 2109-2113 (2010) - [j10]Peter Bühlmann
Twin Boosting: improved feature selection and prediction. Stat. Comput. 20(2): 119-138 (2010)
2000 – 2009
- 2008
- [j9]Daniel Schöner, Markus Kalisch, Christian Leisner, Lukas Meier
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
- [j7]Jelle J. Goeman
Analyzing gene expression data in terms of gene sets: methodological issues. Bioinform. 23(8): 980-987 (2007) - [j6]Corinne Dahinden, Giovanni Parmigiani, Mark C. Emerick, Peter Bühlmann
Penalized likelihood for sparse contingency tables with an application to full-length cDNA libraries. BMC Bioinform. 8 (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
A systematic comparison and evaluation of biclustering methods for gene expression data. Bioinform. 22(9): 1122-1129 (2006) - [j3]Torsten Hothorn
Model-based boosting in high dimensions. Bioinform. 22(22): 2828-2829 (2006) - [j2]Peter Bühlmann, Bin Yu:
Sparse Boosting. J. Mach. Learn. Res. 7: 1001-1024 (2006) - 2005
- [c1]Peter Bühlmann
Boosting and l1-Penalty Methods for High-dimensional Data with Some Applications in Genomics. GfKl 2005: 1-12 - 2003
- [j1]Marcel Dettling, Peter Bühlmann
Boosting for Tumor Classification with Gene Expression Data. Bioinform. 19(9): 1061-1069 (2003)
Coauthor Index
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last updated on 2024-10-07 21:21 CEST by the dblp team
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