Andreas Christmann

35 papers Journal 24Unranked 9
YearRankTypeTitle / Venue / Authors
2025 J jnl
Truncated Kernel Stochastic Gradient Descent with General Losses and Spherical Radial Basis Functions.
CoRR
JinHui Bai, Andreas Christmann, Lei Shi
2024 J jnl
Bootstrap SGD: Algorithmic Stability and Robustness.
CoRR
Andreas Christmann, Yunwen Lei
2024 J jnl
Optimality of Robust Online Learning.
Found. Comput. Math.
Zheng-Chu Guo, Andreas Christmann, Lei Shi
2023 J jnl
Optimality of Robust Online Learning.
CoRR
Zheng-Chu Guo, Andreas Christmann, Lei Shi
2022 J jnl
Total Stability of SVMs and Localized SVMs.
J. Mach. Learn. Res.
Hannes Köhler, Andreas Christmann
2021 J jnl
Total Stability of SVMs and Localized SVMs.
CoRR
Hannes Köhler, Andreas Christmann
2020 J jnl
On the robustness of kernel-based pairwise learning.
CoRR
Patrick Gensler, Andreas Christmann
2018 J jnl
Total stability of kernel methods.
Neurocomputing
Andreas Christmann, Dao-Hong Xiang, Ding-Xuan Zhou
2018 J jnl
Universal consistency and robustness of localized support vector machines.
Neurocomputing
Florian Dumpert, Andreas Christmann
2017 J jnl
Total stability of kernel methods.
CoRR
Andreas Christmann, Dao-Hong Xiang, Ding-Xuan Zhou
2016 J jnl
A short note on extension theorems and their connection to universal consistency in machine learning.
CoRR
Andreas Christmann, Florian Dumpert, Dao-Hong Xiang
2016 J jnl
On the robustness of regularized pairwise learning methods based on kernels.
J. Complex.
Andreas Christmann, Ding-Xuan Zhou
2013 J jnl
On the Consistency of the Bootstrap Approach for Support Vector Machines and Related Kernel Based Methods
CoRR
Andreas Christmann, Robert Hable
2013 — conf
On the Consistency of the Bootstrap Approach for Support Vector Machines and Related Kernel-Based Methods.
Empirical Inference
Andreas Christmann, Robert Hable
2013 — ch.
Robustness Versus Consistency in Ill-Posed Classification and Regression Problems.
Classification and Data Mining
Robert Hable, Andreas Christmann
2012 J jnl
Consistency of support vector machines using additive kernels for additive models.
Comput. Stat. Data Anal.
Andreas Christmann, Robert Hable
2011 J jnl
On qualitative robustness of support vector machines.
J. Multivar. Anal.
Robert Hable, Andreas Christmann
2010 J jnl
A review on consistency and robustness properties of support vector machines for heavy-tailed distributions.
Adv. Data Anal. Classif.
Arnout Van Messem, Andreas Christmann
2010 J jnl
Robustness of reweighted Least Squares Kernel Based Regression.
J. Multivar. Anal.
Michiel Debruyne, Andreas Christmann, Mia Hubert, Johan A. K. Suykens
2010 — conf
Universal Kernels on Non-Standard Input Spaces.
NIPS
Andreas Christmann, Ingo Steinwart
2009 — conf
Fast Learning from Non-i.i.d. Observations.
NIPS
Ingo Steinwart, Andreas Christmann
2008 J jnl
Bouligand Derivatives and Robustness of Support Vector Machines for Regression.
J. Mach. Learn. Res.
Andreas Christmann, Arnout Van Messem
2008 — conf
Sparsity of SVMs that use the epsilon-insensitive loss.
NIPS
Ingo Steinwart, Andreas Christmann
2008 — book
Support Vector Machines.
Ingo Steinwart, Andreas Christmann
2007 J jnl
A robust estimator for the tail index of Pareto-type distributions.
Comput. Stat. Data Anal.
B. Vandewalle, Jan Beirlant, Andreas Christmann, Mia Hubert
2007 — conf
How SVMs can estimate quantiles and the median.
NIPS
Andreas Christmann, Ingo Steinwart
2007 J jnl
Robust learning from bites for data mining.
Comput. Stat. Data Anal.
Andreas Christmann, Ingo Steinwart, Mia Hubert
2004 J jnl
On Robustness Properties of Convex Risk Minimization Methods for Pattern Recognition.
J. Mach. Learn. Res.
Andreas Christmann, Ingo Steinwart
2004 — conf
On a Combination of Convex Risk Minimization Methods.
GfKl
Andreas Christmann
2003 — conf
Regression depth and support vector machine.
Data Depth: Robust Multivariate Analysis, Computational Geometry and Applications
Andreas Christmann
2003 J jnl
Robustness against separation and outliers in logistic regression.
Comput. Stat. Data Anal.
Peter J. Rousseeuw, Andreas Christmann
2002 — conf
Classification Based on the Support Vector Machine, Regression Depth, and Discriminant Analysis.
COMPSTAT
Andreas Christmann, Paul Fischer, Thorsten Joachims
2002 J jnl
Comparison between various regression depth methods and the support vector machine to approximate the minimum number of misclassifications.
Comput. Stat.
Andreas Christmann, Paul Fischer, Thorsten Joachims
1999 J jnl
On group sequential tests based on robust location and scale estimators in the two-sample problem.
Comput. Stat.
Andreas Christmann
1997 — conf
KIT200x - Eine Expertenumfrage über die Zukunft der Kommunikations- und Informationstechnik jenseits der Jahrtausendwende.
GI-Fachtagung über Rechenzentren
Andreas Christmann, Günter Schwichtenberg