KIT200x - Eine Expertenumfrage über die Zukunft der Kommunikations- und Informationstechnik jenseits der Jahrtausendwende. - Andreas Christmann; Günter Schwichtenberg (GI-Fachtagung über Rechenzentren, 1997)

On group sequential tests based on robust location and scale estimators in the two-sample problem. - Andreas Christmann (Comput. Stat., 1999)

Classification Based on the Support Vector Machine, Regression Depth, and Discriminant Analysis. - Andreas Christmann; Paul Fischer; Thorsten Joachims (COMPSTAT, 2002)

Comparison between various regression depth methods and the support vector machine to approximate the minimum number of misclassifications. - Andreas Christmann; Paul Fischer; Thorsten Joachims (Comput. Stat., 2002)

Regression depth and support vector machine. - Andreas Christmann (Data Depth: Robust Multivariate Analysis, Computational Geometry and Applications, 2003)

Robustness against separation and outliers in logistic regression. - Peter J. Rousseeuw; Andreas Christmann (Comput. Stat. Data Anal., 2003)

On a Combination of Convex Risk Minimization Methods. - Andreas Christmann (GfKl, 2004)

On Robustness Properties of Convex Risk Minimization Methods for Pattern Recognition. - Andreas Christmann; Ingo Steinwart (J. Mach. Learn. Res., 2004)

How SVMs can estimate quantiles and the median. - Andreas Christmann; Ingo Steinwart (NIPS, 2007)

Robust learning from bites for data mining. - Andreas Christmann; Ingo Steinwart; Mia Hubert (Comput. Stat. Data Anal., 2007)

A robust estimator for the tail index of Pareto-type distributions. - B. Vandewalle; Jan Beirlant; Andreas Christmann; Mia Hubert (Comput. Stat. Data Anal., 2007)

Support Vector Machines. - Ingo Steinwart; Andreas Christmann (Springer, 2008)

Sparsity of SVMs that use the epsilon-insensitive loss. - Ingo Steinwart; Andreas Christmann (NIPS, 2008)

Bouligand Derivatives and Robustness of Support Vector Machines for Regression. - Andreas Christmann; Arnout Van Messem (J. Mach. Learn. Res., 2008)

Fast Learning from Non-i.i.d. Observations. - Ingo Steinwart; Andreas Christmann (NIPS, 2009)

Universal Kernels on Non-Standard Input Spaces. - Andreas Christmann; Ingo Steinwart (NIPS, 2010)

A review on consistency and robustness properties of support vector machines for heavy-tailed distributions. - Arnout Van Messem; Andreas Christmann (Adv. Data Anal. Classif., 2010)

Robustness of reweighted Least Squares Kernel Based Regression. - Michiel Debruyne; Andreas Christmann; Mia Hubert; Johan A. K. Suykens (J. Multivar. Anal., 2010)

On qualitative robustness of support vector machines. - Robert Hable; Andreas Christmann (J. Multivar. Anal., 2011)

Consistency of support vector machines using additive kernels for additive models. - Andreas Christmann; Robert Hable (Comput. Stat. Data Anal., 2012)

On the Consistency of the Bootstrap Approach for Support Vector Machines and Related Kernel-Based Methods. - Andreas Christmann; Robert Hable (Empirical Inference, 2013)

Robustness Versus Consistency in Ill-Posed Classification and Regression Problems. - Robert Hable; Andreas Christmann (Classification and Data Mining, 2013)

A short note on extension theorems and their connection to universal consistency in machine learning. - Andreas Christmann; Florian Dumpert; Dao-Hong Xiang (CoRR, 2016)

On the robustness of regularized pairwise learning methods based on kernels. - Andreas Christmann; Ding-Xuan Zhou (J. Complex., 2016)

Total stability of kernel methods. - Andreas Christmann; Dao-Hong Xiang; Ding-Xuan Zhou (Neurocomputing, 2018)

Universal consistency and robustness of localized support vector machines. - Florian Dumpert; Andreas Christmann (Neurocomputing, 2018)

On the robustness of kernel-based pairwise learning. - Patrick Gensler; Andreas Christmann (CoRR, 2020)

Total Stability of SVMs and Localized SVMs. - Hannes Köhler; Andreas Christmann (J. Mach. Learn. Res., 2022)

Bootstrap SGD: Algorithmic Stability and Robustness. - Andreas Christmann; Yunwen Lei (CoRR, 2024)

Optimality of Robust Online Learning. - Zheng-Chu Guo; Andreas Christmann; Lei Shi 010 (Found. Comput. Math., 2024)