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ORCIDIngo Steinwart
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- affiliation: Universität Stuttgart, Germany
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2020 – today
- 2024
[i20]Ingo Steinwart:
Conditioning of Banach Space Valued Gaussian Random Variables: An Approximation Approach Based on Martingales. CoRR abs/2404.03453 (2024)- [i19]David Holzmüller, Léo Grinsztajn, Ingo Steinwart:
Better by Default: Strong Pre-Tuned MLPs and Boosted Trees on Tabular Data. CoRR abs/2407.04491 (2024) - 2023
- [j31]David Holzmüller, Viktor Zaverkin, Johannes Kästner, Ingo Steinwart:
A Framework and Benchmark for Deep Batch Active Learning for Regression. J. Mach. Learn. Res. 24: 164:1-164:81 (2023) - [j30]Ingo Steinwart, Bharath K. Sriperumbudur, Philipp Thomann:
Adaptive Clustering Using Kernel Density Estimators. J. Mach. Learn. Res. 24: 275:1-275:56 (2023) - [c23]Moritz Haas, David Holzmüller, Ulrike von Luxburg, Ingo Steinwart:
Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension. NeurIPS 2023 - [i18]Moritz Haas, David Holzmüller, Ulrike von Luxburg, Ingo Steinwart:
Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension. CoRR abs/2305.14077 (2023) - 2022
- [j29]Ingrid Blaschzyk, Ingo Steinwart:
Improved Classification Rates for Localized SVMs. J. Mach. Learn. Res. 23: 165:1-165:59 (2022) - [j28]David Holzmüller, Ingo Steinwart:
Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent. J. Mach. Learn. Res. 23: 181:1-181:82 (2022) - [j27]Thomas Hamm
, Ingo Steinwart:
Intrinsic Dimension Adaptive Partitioning for Kernel Methods. SIAM J. Math. Data Sci. 4(2): 721-749 (2022) - [c22]Manuel Nonnenmacher, Thomas Pfeil, Ingo Steinwart, David Reeb:
SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning. ICLR 2022 - [c21]Manuel T. Nonnenmacher, Lukas Oldenburg, Ingo Steinwart, David Reeb:
Utilizing Expert Features for Contrastive Learning of Time-Series Representations. ICML 2022: 16969-16989 - [i17]David Holzmüller, Viktor Zaverkin, Johannes Kästner, Ingo Steinwart:
A Framework and Benchmark for Deep Batch Active Learning for Regression. CoRR abs/2203.09410 (2022) - [i16]Manuel Nonnenmacher, Lukas Oldenburg, Ingo Steinwart, David Reeb:
Utilizing Expert Features for Contrastive Learning of Time-Series Representations. CoRR abs/2206.11517 (2022) - [i15]Marvin Pförtner, Ingo Steinwart, Philipp Hennig, Jonathan Wenger:
Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers. CoRR abs/2212.12474 (2022) - 2021
- [j26]Ingo Steinwart, Simon Fischer:
A closer look at covering number bounds for Gaussian kernels. J. Complex. 62: 101513 (2021) - [c20]Manuel Nonnenmacher, David Reeb, Ingo Steinwart:
Which Minimizer Does My Neural Network Converge To? ECML/PKDD (3) 2021: 87-102 - [i14]Manuel Nonnenmacher, Thomas Pfeil, Ingo Steinwart, David Reeb:
SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning. CoRR abs/2110.11395 (2021) - 2020
- [j25]Simon Fischer, Ingo Steinwart:
Sobolev Norm Learning Rates for Regularized Least-Squares Algorithms. J. Mach. Learn. Res. 21: 205:1-205:38 (2020) - [i13]Ingo Steinwart:
Reproducing Kernel Hilbert Spaces Cannot Contain all Continuous Functions on a Compact Metric Space. CoRR abs/2002.03171 (2020) - [i12]David Holzmüller, Ingo Steinwart:
Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent. CoRR abs/2002.04861 (2020) - [i11]Manuel Nonnenmacher, David Reeb, Ingo Steinwart:
Which Minimizer Does My Neural Network Converge To? CoRR abs/2011.02408 (2020)
2010 – 2019
- 2019
- [j24]Muhammad Farooq, Ingo Steinwart:
Learning rates for kernel-based expectile regression. Mach. Learn. 108(2): 203-227 (2019) - [i10]Ingo Steinwart:
A Sober Look at Neural Network Initializations. CoRR abs/1903.11482 (2019) - [i9]Nicole Mücke, Ingo Steinwart:
Global Minima of DNNs: The Plenty Pantry. CoRR abs/1905.10686 (2019) - [i8]Hanyuan Hang, Xiaoyu Liu, Ingo Steinwart:
Best-scored Random Forest Classification. CoRR abs/1905.11028 (2019) - 2018
- [j23]Hanyuan Hang, Ingo Steinwart, Yunlong Feng, Johan A. K. Suykens:
Kernel Density Estimation for Dynamical Systems. J. Mach. Learn. Res. 19: 35:1-35:49 (2018) - [i7]Hanyuan Hang, Ingo Steinwart:
Optimal Learning with Anisotropic Gaussian SVMs. CoRR abs/1810.02321 (2018) - 2017
- [j22]Muhammad Farooq, Ingo Steinwart:
An SVM-like approach for expectile regression. Comput. Stat. Data Anal. 109: 159-181 (2017) - [j21]Ingo Steinwart:
A short note on the comparison of interpolation widths, entropy numbers, and Kolmogorov widths. J. Approx. Theory 215: 13-27 (2017) - [c19]Philipp Thomann, Ingrid Blaschzyk, Mona Meister, Ingo Steinwart:
Spatial Decompositions for Large Scale SVMs. AISTATS 2017: 1329-1337 - [i6]Ingo Steinwart, Philipp Thomann:
liquidSVM: A Fast and Versatile SVM package. CoRR abs/1702.06899 (2017) - [i5]Muhammad Farooq, Ingo Steinwart:
Learning Rates for Kernel-Based Expectile Regression. CoRR abs/1702.07552 (2017) - 2016
- [j20]Mona Meister, Ingo Steinwart:
Optimal Learning Rates for Localized SVMs. J. Mach. Learn. Res. 17: 194:1-194:44 (2016) - [j19]Hanyuan Hang, Yunlong Feng, Ingo Steinwart, Johan A. K. Suykens:
Learning Theory Estimates with Observations from General Stationary Stochastic Processes. Neural Comput. 28(12): 2853-2889 (2016) - [i4]Hanyuan Hang, Yunlong Feng, Ingo Steinwart, Johan A. K. Suykens:
Learning theory estimates with observations from general stationary stochastic processes. CoRR abs/1605.02887 (2016) - [i3]Philipp Thomann, Ingo Steinwart, Ingrid Blaschzyk, Mona Meister:
Spatial Decompositions for Large Scale SVMs. CoRR abs/1612.00374 (2016) - [i2]Ingo Steinwart, Philipp Thomann, Nico Schmid:
Learning with Hierarchical Gaussian Kernels. CoRR abs/1612.00824 (2016) - 2015
- [j18]Philipp Thomann, Ingo Steinwart, Nico Schmid:
Towards an axiomatic approach to hierarchical clustering of measures. J. Mach. Learn. Res. 16: 1949-2002 (2015) - [i1]Philipp Thomann, Ingo Steinwart, Nico Schmid:
Towards an Axiomatic Approach to Hierarchical Clustering of Measures. CoRR abs/1508.03712 (2015) - 2014
- [j17]Hanyuan Hang, Ingo Steinwart:
Fast learning from α-mixing observations. J. Multivar. Anal. 127: 184-199 (2014) - [c18]Ingo Steinwart, Chloé Pasin, Robert C. Williamson, Siyu Zhang:
Elicitation and Identification of Properties. COLT 2014: 482-526 - 2013
- [c17]Ingo Steinwart:
Some Remarks on the Statistical Analysis of SVMs and Related Methods. Empirical Inference 2013: 25-36 - [e1]Shai Shalev-Shwartz, Ingo Steinwart:
COLT 2013 - The 26th Annual Conference on Learning Theory, June 12-14, 2013, Princeton University, NJ, USA. JMLR Workshop and Conference Proceedings 30, JMLR.org 2013 [contents] - 2012
- [c16]Bharath K. Sriperumbudur, Ingo Steinwart:
Consistency and Rates for Clustering with DBSCAN. AISTATS 2012: 1090-1098 - 2011
- [j16]Ingo Steinwart, Don R. Hush, Clint Scovel:
Training SVMs Without Offset. J. Mach. Learn. Res. 12: 141-202 (2011) - [c15]Mona Eberts, Ingo Steinwart:
Optimal learning rates for least squares SVMs using Gaussian kernels. NIPS 2011: 1539-1547 - [c14]Ingo Steinwart:
Adaptive Density Level Set Clustering. COLT 2011: 703-738 - 2010
- [j15]Clint Scovel, Don R. Hush, Ingo Steinwart, James Theiler:
Radial kernels and their reproducing kernel Hilbert spaces. J. Complex. 26(6): 641-660 (2010) - [c13]Ingo Steinwart, James Theiler, Daniel Llamocca:
Using support vector machines for anomalous change detection. IGARSS 2010: 3732-3735 - [c12]Andreas Christmann, Ingo Steinwart:
Universal Kernels on Non-Standard Input Spaces. NIPS 2010: 406-414
2000 – 2009
- 2009
- [j14]Ingo Steinwart:
Oracle inequalities for support vector machines that are based on random entropy numbers. J. Complex. 25(5): 437-454 (2009) - [j13]Ingo Steinwart, Don R. Hush, Clint Scovel:
Learning from dependent observations. J. Multivar. Anal. 100(1): 175-194 (2009) - [c11]Ingo Steinwart, Don R. Hush, Clint Scovel:
Optimal Rates for Regularized Least Squares Regression. COLT 2009 - [c10]Ingo Steinwart, Andreas Christmann:
Fast Learning from Non-i.i.d. Observations. NIPS 2009: 1768-1776 - 2008
- [b1]Ingo Steinwart, Andreas Christmann:
Support Vector Machines. Information science and statistics, Springer 2008, ISBN 978-0-387-77241-7, pp. I-XVI, 1-601 - [c9]Ingo Steinwart, Andreas Christmann:
Sparsity of SVMs that use the epsilon-insensitive loss. NIPS 2008: 1569-1576 - 2007
- [j12]Andreas Christmann, Ingo Steinwart, Mia Hubert:
Robust learning from bites for data mining. Comput. Stat. Data Anal. 52(1): 347-361 (2007) - [j11]Don R. Hush, Clint Scovel, Ingo Steinwart:
Stability of Unstable Learning Algorithms. Mach. Learn. 67(3): 197-206 (2007) - [c8]Nikolas List, Don R. Hush, Clint Scovel, Ingo Steinwart:
Gaps in Support Vector Optimization. COLT 2007: 336-348 - [c7]Andreas Christmann, Ingo Steinwart:
How SVMs can estimate quantiles and the median. NIPS 2007: 305-312 - 2006
- [j10]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: 733-769 (2006) - [j9]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): 4635-4643 (2006) - [c6]Ingo Steinwart, Don R. Hush, Clint Scovel:
Function Classes That Approximate the Bayes Risk. COLT 2006: 79-93 - [c5]Ingo Steinwart, Don R. Hush, Clint Scovel:
An Oracle Inequality for Clipped Regularized Risk Minimizers. NIPS 2006: 1321-1328 - 2005
- [j8]Ingo Steinwart, Don R. Hush, Clint Scovel:
A Classification Framework for Anomaly Detection. J. Mach. Learn. Res. 6: 211-232 (2005) - [j7]Ingo Steinwart:
Consistency of support vector machines and other regularized kernel classifiers. IEEE Trans. Inf. Theory 51(1): 128-142 (2005) - [c4]Ingo Steinwart, Clint Scovel:
Fast Rates for Support Vector Machines. COLT 2005: 279-294 - 2004
- [j6]Ingo Steinwart:
Entropy of convex hulls--some Lorentz norm results. J. Approx. Theory 128(1): 42-52 (2004) - [j5]Andreas Christmann, Ingo Steinwart:
On Robustness Properties of Convex Risk Minimization Methods for Pattern Recognition. J. Mach. Learn. Res. 5: 1007-1034 (2004) - [c3]Ingo Steinwart, Don R. Hush, Clint Scovel:
Density Level Detection is Classification. NIPS 2004: 1337-1344 - [c2]Ingo Steinwart, Clint Scovel:
Fast Rates to Bayes for Kernel Machines. NIPS 2004: 1345-1352 - 2003
- [j4]Ingo Steinwart:
Sparseness of Support Vector Machines. J. Mach. Learn. Res. 4: 1071-1105 (2003) - [j3]Ingo Steinwart:
On the Optimal Parameter Choice for v-Support Vector Machines. IEEE Trans. Pattern Anal. Mach. Intell. 25(10): 1274-1284 (2003) - [c1]Ingo Steinwart:
Sparseness of Support Vector Machines---Some Asymptotically Sharp Bounds. NIPS 2003: 1069-1076 - 2002
- [j2]Ingo Steinwart:
Support Vector Machines are Universally Consistent. J. Complex. 18(3): 768-791 (2002) - 2001
- [j1]Ingo Steinwart:
On the Influence of the Kernel on the Consistency of Support Vector Machines. J. Mach. Learn. Res. 2: 67-93 (2001)
Coauthor Index
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