On the Influence of the Kernel on the Consistency of Support Vector Machines. - Ingo Steinwart (J. Mach. Learn. Res., 2001)

Support Vector Machines are Universally Consistent. - Ingo Steinwart (J. Complex., 2002)

Sparseness of Support Vector Machines—Some Asymptotically Sharp Bounds. - Ingo Steinwart (NIPS, 2003)

Sparseness of Support Vector Machines. - Ingo Steinwart (J. Mach. Learn. Res., 2003)

On the Optimal Parameter Choice for v-Support Vector Machines. - Ingo Steinwart (IEEE Trans. Pattern Anal. Mach. Intell., 2003)

Density Level Detection is Classification. - Ingo Steinwart; Don R. Hush; Clint Scovel (NIPS, 2004)

Fast Rates to Bayes for Kernel Machines. - Ingo Steinwart; Clint Scovel (NIPS, 2004)

Entropy of convex hulls–some Lorentz norm results. - Ingo Steinwart (J. Approx. Theory, 2004)

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

Fast Rates for Support Vector Machines. - Ingo Steinwart; Clint Scovel (COLT, 2005)

A Classification Framework for Anomaly Detection. - Ingo Steinwart; Don R. Hush; Clint Scovel (J. Mach. Learn. Res., 2005)

Consistency of support vector machines and other regularized kernel classifiers. - Ingo Steinwart (IEEE Trans. Inf. Theory, 2005)

Function Classes That Approximate the Bayes Risk. - Ingo Steinwart; Don R. Hush; Clint Scovel (COLT, 2006)

An Oracle Inequality for Clipped Regularized Risk Minimizers. - Ingo Steinwart; Don R. Hush; Clint Scovel (NIPS, 2006)

QP Algorithms with Guaranteed Accuracy and Run Time for Support Vector Machines. - Don R. Hush; Patrick Kelly; Clint Scovel; Ingo Steinwart (J. Mach. Learn. Res., 2006)

An Explicit Description of the Reproducing Kernel Hilbert Spaces of Gaussian RBF Kernels. - Ingo Steinwart; Don R. Hush; Clint Scovel (IEEE Trans. Inf. Theory, 2006)

Gaps in Support Vector Optimization. - Nikolas List; Don R. Hush; Clint Scovel; Ingo Steinwart (COLT, 2007)

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)

Stability of Unstable Learning Algorithms. - Don R. Hush; Clint Scovel; Ingo Steinwart (Mach. Learn., 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)

Optimal Rates for Regularized Least Squares Regression. - Ingo Steinwart; Don R. Hush; Clint Scovel (COLT, 2009)

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

Oracle inequalities for support vector machines that are based on random entropy numbers. - Ingo Steinwart (J. Complex., 2009)

Learning from dependent observations. - Ingo Steinwart; Don R. Hush; Clint Scovel (J. Multivar. Anal., 2009)

Using support vector machines for anomalous change detection. - Ingo Steinwart; James Theiler; Daniel Llamocca (IGARSS, 2010)

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

Radial kernels and their reproducing kernel Hilbert spaces. - Clint Scovel; Don R. Hush; Ingo Steinwart; James Theiler (J. Complex., 2010)

Optimal learning rates for least squares SVMs using Gaussian kernels. - Mona Eberts; Ingo Steinwart (NIPS, 2011)

Adaptive Density Level Set Clustering. - Ingo Steinwart (COLT, 2011)

Training SVMs Without Offset. - Ingo Steinwart; Don R. Hush; Clint Scovel (J. Mach. Learn. Res., 2011)

Consistency and Rates for Clustering with DBSCAN. - Bharath K. Sriperumbudur; Ingo Steinwart (AISTATS, 2012)

Some Remarks on the Statistical Analysis of SVMs and Related Methods. - Ingo Steinwart (Empirical Inference, 2013)

Elicitation and Identification of Properties. - Ingo Steinwart; Chloé Pasin; Robert C. Williamson; Siyu Zhang (COLT, 2014)

Fast learning from α-mixing observations. - Hanyuan Hang; Ingo Steinwart (J. Multivar. Anal., 2014)

Towards an axiomatic approach to hierarchical clustering of measures. - Philipp Thomann; Ingo Steinwart; Nico Schmid (J. Mach. Learn. Res., 2015)

Learning with Hierarchical Gaussian Kernels. - Ingo Steinwart; Philipp Thomann; Nico Schmid (CoRR, 2016)

Optimal Learning Rates for Localized SVMs. - Mona Meister; Ingo Steinwart (J. Mach. Learn. Res., 2016)

Learning Theory Estimates with Observations from General Stationary Stochastic Processes. - Hanyuan Hang; Yunlong Feng; Ingo Steinwart; Johan A. K. Suykens (Neural Comput., 2016)

Spatial Decompositions for Large Scale SVMs. - Philipp Thomann; Ingrid Blaschzyk; Mona Meister; Ingo Steinwart (AISTATS, 2017)

liquidSVM: A Fast and Versatile SVM package. - Ingo Steinwart; Philipp Thomann (CoRR, 2017)

An SVM-like approach for expectile regression. - Muhammad Farooq; Ingo Steinwart (Comput. Stat. Data Anal., 2017)

A short note on the comparison of interpolation widths, entropy numbers, and Kolmogorov widths. - Ingo Steinwart (J. Approx. Theory, 2017)

Optimal Learning with Anisotropic Gaussian SVMs. - Hanyuan Hang; Ingo Steinwart (CoRR, 2018)

Kernel Density Estimation for Dynamical Systems. - Hanyuan Hang; Ingo Steinwart; Yunlong Feng; Johan A. K. Suykens (J. Mach. Learn. Res., 2018)

A Sober Look at Neural Network Initializations. - Ingo Steinwart (CoRR, 2019)

Global Minima of DNNs: The Plenty Pantry. - Nicole Mücke; Ingo Steinwart (CoRR, 2019)

Best-scored Random Forest Classification. - Hanyuan Hang; Xiaoyu Liu; Ingo Steinwart (CoRR, 2019)

Learning rates for kernel-based expectile regression. - Muhammad Farooq; Ingo Steinwart (Mach. Learn., 2019)

Reproducing Kernel Hilbert Spaces Cannot Contain all Continuous Functions on a Compact Metric Space. - Ingo Steinwart (CoRR, 2020)

Sobolev Norm Learning Rates for Regularized Least-Squares Algorithms. - Simon Fischer 004; Ingo Steinwart (J. Mach. Learn. Res., 2020)

Which Minimizer Does My Neural Network Converge To? - Manuel Nonnenmacher; David Reeb; Ingo Steinwart (ECML/PKDD (3), 2021)

A closer look at covering number bounds for Gaussian kernels. - Ingo Steinwart; Simon Fischer 004 (J. Complex., 2021)

Code for: Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments. - Viktor Zaverkin; David Holzmüller; Ingo Steinwart; Johannes Kästner (DaRUS, 2021)

SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning. - Manuel Nonnenmacher; Thomas Pfeil; Ingo Steinwart; David Reeb (ICLR, 2022)

Utilizing Expert Features for Contrastive Learning of Time-Series Representations. - Manuel T. Nonnenmacher; Lukas Oldenburg; Ingo Steinwart; David Reeb (ICML, 2022)

Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers. - Marvin Pförtner; Ingo Steinwart; Philipp Hennig; Jonathan Wenger (CoRR, 2022)

Improved Classification Rates for Localized SVMs. - Ingrid Blaschzyk; Ingo Steinwart (J. Mach. Learn. Res., 2022)

Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent. - David Holzmüller; Ingo Steinwart (J. Mach. Learn. Res., 2022)

Code for: Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent. - David Holzmüller; Ingo Steinwart (DaRUS, 2022)

Intrinsic Dimension Adaptive Partitioning for Kernel Methods. - Thomas Hamm; Ingo Steinwart (SIAM J. Math. Data Sci., 2022)

Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension. - Moritz Haas; David Holzmüller; Ulrike von Luxburg; Ingo Steinwart (NeurIPS, 2023)

A Framework and Benchmark for Deep Batch Active Learning for Regression. - David Holzmüller; Viktor Zaverkin; Johannes Kästner; Ingo Steinwart (J. Mach. Learn. Res., 2023)

Adaptive Clustering Using Kernel Density Estimators. - Ingo Steinwart; Bharath K. Sriperumbudur; Philipp Thomann (J. Mach. Learn. Res., 2023)

Code and Data for: A Framework and Benchmark for Deep Batch Active Learning for Regression [arXiv v3]. - David Holzmüller; Viktor Zaverkin; Johannes Kästner; Ingo Steinwart (DaRUS, 2023)

Better by default: Strong pre-tuned MLPs and boosted trees on tabular data. - David Holzmüller; Léo Grinsztajn; Ingo Steinwart (NeurIPS, 2024)

Conditioning of Banach Space Valued Gaussian Random Variables: An Approximation Approach Based on Martingales. - Ingo Steinwart (CoRR, 2024)

Empirical risk minimization in the interpolating regime with application to neural network learning. - Nicole Mücke; Ingo Steinwart (Mach. Learn., 2025)