Ingo Steinwart

Publication Activity (10 Years)

Years Active: 2001-2024
Publications (10 Years): 36

Top Topics

Gaussian Kernels

Stochastic Process

Top Venues

J. Mach. Learn. Res.

J. Approx. Theory

Publications

Ingo Steinwart
Conditioning of Banach Space Valued Gaussian Random Variables: An Approximation Approach Based on Martingales. CoRR (2024)
David Holzmüller, Léo Grinsztajn, Ingo Steinwart
Better by Default: Strong Pre-Tuned MLPs and Boosted Trees on Tabular Data. CoRR (2024)
Moritz Haas, David Holzmüller, Ulrike von Luxburg, Ingo Steinwart
Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension. CoRR (2023)
David Holzmüller, Viktor Zaverkin, Johannes Kästner, Ingo Steinwart
A Framework and Benchmark for Deep Batch Active Learning for Regression. J. Mach. Learn. Res. 24 (2023)
Moritz Haas, David Holzmüller, Ulrike von Luxburg, Ingo Steinwart
Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension. NeurIPS (2023)
Thomas Hamm, Ingo Steinwart
Intrinsic Dimension Adaptive Partitioning for Kernel Methods. SIAM J. Math. Data Sci. 4 (2) (2022)
Manuel Nonnenmacher, Thomas Pfeil, Ingo Steinwart, David Reeb
SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning. ICLR (2022)
Marvin Pförtner, Ingo Steinwart, Philipp Hennig, Jonathan Wenger
Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers. CoRR (2022)
David Holzmüller, Ingo Steinwart
Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent. J. Mach. Learn. Res. 23 (2022)
Manuel Nonnenmacher, Lukas Oldenburg, Ingo Steinwart, David Reeb
Utilizing Expert Features for Contrastive Learning of Time-Series Representations. CoRR (2022)
David Holzmüller, Viktor Zaverkin, Johannes Kästner, Ingo Steinwart
A Framework and Benchmark for Deep Batch Active Learning for Regression. CoRR (2022)
Manuel T. Nonnenmacher, Lukas Oldenburg, Ingo Steinwart, David Reeb
Utilizing Expert Features for Contrastive Learning of Time-Series Representations. ICML (2022)
Ingrid Blaschzyk, Ingo Steinwart
Improved Classification Rates for Localized SVMs. J. Mach. Learn. Res. 23 (2022)
Manuel Nonnenmacher, David Reeb, Ingo Steinwart
Which Minimizer Does My Neural Network Converge To? ECML/PKDD (3) (2021)
Ingo Steinwart, Simon Fischer
A closer look at covering number bounds for Gaussian kernels. J. Complex. 62 (2021)
Manuel Nonnenmacher, Thomas Pfeil, Ingo Steinwart, David Reeb
SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning. CoRR (2021)
Ingo Steinwart
Reproducing Kernel Hilbert Spaces Cannot Contain all Continuous Functions on a Compact Metric Space. CoRR (2020)
David Holzmüller, Ingo Steinwart
Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent. CoRR (2020)
Simon Fischer, Ingo Steinwart
Sobolev Norm Learning Rates for Regularized Least-Squares Algorithms. J. Mach. Learn. Res. 21 (2020)
Manuel Nonnenmacher, David Reeb, Ingo Steinwart
Which Minimizer Does My Neural Network Converge To? CoRR (2020)
Hanyuan Hang, Xiaoyu Liu, Ingo Steinwart
Best-scored Random Forest Classification. CoRR (2019)
Nicole Mücke, Ingo Steinwart
Global Minima of DNNs: The Plenty Pantry. CoRR (2019)
Ingo Steinwart
A Sober Look at Neural Network Initializations. CoRR (2019)
Muhammad Farooq, Ingo Steinwart
Learning rates for kernel-based expectile regression. Mach. Learn. 108 (2) (2019)
Hanyuan Hang, Ingo Steinwart
Optimal Learning with Anisotropic Gaussian SVMs. CoRR (2018)
Hanyuan Hang, Ingo Steinwart, Yunlong Feng, Johan A. K. Suykens
Kernel Density Estimation for Dynamical Systems. J. Mach. Learn. Res. 19 (2018)
Muhammad Farooq, Ingo Steinwart
An SVM-like approach for expectile regression. Comput. Stat. Data Anal. 109 (2017)
Muhammad Farooq, Ingo Steinwart
Learning Rates for Kernel-Based Expectile Regression. CoRR (2017)
Philipp Thomann, Ingrid Blaschzyk, Mona Meister, Ingo Steinwart
Spatial Decompositions for Large Scale SVMs. AISTATS (2017)
Ingo Steinwart, Philipp Thomann
liquidSVM: A Fast and Versatile SVM package. CoRR (2017)
Ingo Steinwart
A short note on the comparison of interpolation widths, entropy numbers, and Kolmogorov widths. J. Approx. Theory 215 (2017)
Ingo Steinwart, Philipp Thomann, Nico Schmid
Learning with Hierarchical Gaussian Kernels. CoRR (2016)
Hanyuan Hang, Yunlong Feng, Ingo Steinwart, Johan A. K. Suykens
Learning Theory Estimates with Observations from General Stationary Stochastic Processes. Neural Comput. 28 (12) (2016)
Mona Meister, Ingo Steinwart
Optimal Learning Rates for Localized SVMs. J. Mach. Learn. Res. 17 (2016)
Hanyuan Hang, Yunlong Feng, Ingo Steinwart, Johan A. K. Suykens
Learning theory estimates with observations from general stationary stochastic processes. CoRR (2016)
Philipp Thomann, Ingo Steinwart, Ingrid Blaschzyk, Mona Meister
Spatial Decompositions for Large Scale SVMs. CoRR (2016)
Philipp Thomann, Ingo Steinwart, Nico Schmid
Towards an Axiomatic Approach to Hierarchical Clustering of Measures. CoRR (2015)
Philipp Thomann, Ingo Steinwart, Nico Schmid
Towards an axiomatic approach to hierarchical clustering of measures. J. Mach. Learn. Res. 16 (2015)
Hanyuan Hang, Ingo Steinwart
Fast learning from α-mixing observations. J. Multivar. Anal. 127 (2014)
Ingo Steinwart, Chloé Pasin, Robert C. Williamson, Siyu Zhang
Elicitation and Identification of Properties. COLT (2014)
Ingo Steinwart
Some Remarks on the Statistical Analysis of SVMs and Related Methods. Empirical Inference (2013)
Bharath K. Sriperumbudur, Ingo Steinwart
Consistency and Rates for Clustering with DBSCAN. AISTATS (2012)
Ingo Steinwart, Don R. Hush, Clint Scovel
Training SVMs Without Offset. J. Mach. Learn. Res. 12 (2011)
Ingo Steinwart
Adaptive Density Level Set Clustering. COLT (2011)
Mona Eberts, Ingo Steinwart
Optimal learning rates for least squares SVMs using Gaussian kernels. NIPS (2011)
Clint Scovel, Don R. Hush, Ingo Steinwart, James Theiler
Radial kernels and their reproducing kernel Hilbert spaces. J. Complex. 26 (6) (2010)
Ingo Steinwart, James Theiler, Daniel Llamocca
Using support vector machines for anomalous change detection. IGARSS (2010)
Andreas Christmann, Ingo Steinwart
Universal Kernels on Non-Standard Input Spaces. NIPS (2010)
Ingo Steinwart, Don R. Hush, Clint Scovel
Learning from dependent observations. J. Multivar. Anal. 100 (1) (2009)
Ingo Steinwart, Don R. Hush, Clint Scovel
Optimal Rates for Regularized Least Squares Regression. COLT (2009)
Ingo Steinwart, Andreas Christmann
Fast Learning from Non-i.i.d. Observations. NIPS (2009)
Ingo Steinwart
Oracle inequalities for support vector machines that are based on random entropy numbers. J. Complex. 25 (5) (2009)
Ingo Steinwart, Andreas Christmann
Sparsity of SVMs that use the epsilon-insensitive loss. NIPS (2008)
Andreas Christmann, Ingo Steinwart
How SVMs can estimate quantiles and the median. NIPS (2007)
Nikolas List, Don R. Hush, Clint Scovel, Ingo Steinwart
Gaps in Support Vector Optimization. COLT (2007)
Andreas Christmann, Ingo Steinwart, Mia Hubert
Robust learning from bites for data mining. Comput. Stat. Data Anal. 52 (1) (2007)
Don R. Hush, Clint Scovel, Ingo Steinwart
Stability of Unstable Learning Algorithms. Mach. Learn. 67 (3) (2007)
Ingo Steinwart, Don R. Hush, Clint Scovel
An Oracle Inequality for Clipped Regularized Risk Minimizers. NIPS (2006)
Ingo Steinwart, Don R. Hush, Clint Scovel
An Explicit Description of the Reproducing Kernel Hilbert Spaces of Gaussian RBF Kernels. IEEE Trans. Inf. Theory 52 (10) (2006)
Don R. Hush, Patrick Kelly, Clint Scovel, Ingo Steinwart
QP Algorithms with Guaranteed Accuracy and Run Time for Support Vector Machines. J. Mach. Learn. Res. 7 (2006)
Ingo Steinwart, Don R. Hush, Clint Scovel
Function Classes That Approximate the Bayes Risk. COLT (2006)
Ingo Steinwart
Consistency of support vector machines and other regularized kernel classifiers. IEEE Trans. Inf. Theory 51 (1) (2005)
Ingo Steinwart, Don R. Hush, Clint Scovel
A Classification Framework for Anomaly Detection. J. Mach. Learn. Res. 6 (2005)
Ingo Steinwart, Clint Scovel
Fast Rates for Support Vector Machines. COLT (2005)
Ingo Steinwart, Clint Scovel
Fast Rates to Bayes for Kernel Machines. NIPS (2004)
Andreas Christmann, Ingo Steinwart
On Robustness Properties of Convex Risk Minimization Methods for Pattern Recognition. J. Mach. Learn. Res. 5 (2004)
Ingo Steinwart, Don R. Hush, Clint Scovel
Density Level Detection is Classification. NIPS (2004)
Ingo Steinwart
Entropy of convex hulls--some Lorentz norm results. J. Approx. Theory 128 (1) (2004)
Ingo Steinwart
On the Optimal Parameter Choice for v-Support Vector Machines. IEEE Trans. Pattern Anal. Mach. Intell. 25 (10) (2003)
Ingo Steinwart
Sparseness of Support Vector Machines---Some Asymptotically Sharp Bounds. NIPS (2003)
Ingo Steinwart
Sparseness of Support Vector Machines. J. Mach. Learn. Res. 4 (2003)
Ingo Steinwart
Support Vector Machines are Universally Consistent. J. Complex. 18 (3) (2002)
Ingo Steinwart
On the Influence of the Kernel on the Consistency of Support Vector Machines. J. Mach. Learn. Res. 2 (2001)