Ingo Steinwart

80 papers A* 11A 2C 1Journal 54Unranked 11

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Self-Regularized Learning Methods. CoRR Max Schölpple, Liu Fanghui, Ingo Steinwart
2025	J	`jnl`	Empirical risk minimization in the interpolating regime with application to neural network learning. Mach. Learn. Nicole Mücke, Ingo Steinwart
2025	J	`jnl`	Nonparametric Instrumental Variable Regression with Observed Covariates. CoRR Zikai Shen, Zonghao Chen, Dimitri Meunier, Ingo Steinwart, Arthur Gretton, Zhu Li
2024	J	`jnl`	Better by Default: Strong Pre-Tuned MLPs and Boosted Trees on Tabular Data. CoRR David Holzmüller, Léo Grinsztajn, Ingo Steinwart
2024	A*	`conf`	Better by default: Strong pre-tuned MLPs and boosted trees on tabular data. NeurIPS David Holzmüller, Léo Grinsztajn, Ingo Steinwart
2024	J	`jnl`	Conditioning of Banach Space Valued Gaussian Random Variables: An Approximation Approach Based on Martingales. CoRR Ingo Steinwart
2023	J	`jnl`	A Framework and Benchmark for Deep Batch Active Learning for Regression. J. Mach. Learn. Res. David Holzmüller, Viktor Zaverkin, Johannes Kästner, Ingo Steinwart
2023	J	`jnl`	Adaptive Clustering Using Kernel Density Estimators. J. Mach. Learn. Res. Ingo Steinwart, Bharath K. Sriperumbudur, Philipp Thomann
2023	A*	`conf`	Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension. NeurIPS Moritz Haas, David Holzmüller, Ulrike von Luxburg, Ingo Steinwart
2023	J	`jnl`	Mind the spikes: Benign overfitting of kernels and neural networks in fixed dimension. CoRR Moritz Haas, David Holzmüller, Ulrike von Luxburg, Ingo Steinwart
2022	J	`jnl`	A Framework and Benchmark for Deep Batch Active Learning for Regression. CoRR David Holzmüller, Viktor Zaverkin, Johannes Kästner, Ingo Steinwart
2022	J	`jnl`	Improved Classification Rates for Localized SVMs. J. Mach. Learn. Res. Ingrid Blaschzyk, Ingo Steinwart
2022	J	`jnl`	Intrinsic Dimension Adaptive Partitioning for Kernel Methods. SIAM J. Math. Data Sci. Thomas Hamm, Ingo Steinwart
2022	J	`jnl`	Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers. CoRR Marvin Pförtner, Ingo Steinwart, Philipp Hennig, Jonathan Wenger
2022	A*	`conf`	SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning. ICLR Manuel Nonnenmacher, Thomas Pfeil, Ingo Steinwart, David Reeb
2022	J	`jnl`	Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent. J. Mach. Learn. Res. David Holzmüller, Ingo Steinwart
2022	A*	`conf`	Utilizing Expert Features for Contrastive Learning of Time-Series Representations. ICML Manuel T. Nonnenmacher, Lukas Oldenburg, Ingo Steinwart, David Reeb
2022	J	`jnl`	Utilizing Expert Features for Contrastive Learning of Time-Series Representations. CoRR Manuel Nonnenmacher, Lukas Oldenburg, Ingo Steinwart, David Reeb
2021	J	`jnl`	A closer look at covering number bounds for Gaussian kernels. J. Complex. Ingo Steinwart, Simon Fischer
2021	J	`jnl`	SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning. CoRR Manuel Nonnenmacher, Thomas Pfeil, Ingo Steinwart, David Reeb
2021	—	`conf`	Which Minimizer Does My Neural Network Converge To? ECML/PKDD (3) Manuel Nonnenmacher, David Reeb, Ingo Steinwart
2020	J	`jnl`	Reproducing Kernel Hilbert Spaces Cannot Contain all Continuous Functions on a Compact Metric Space. CoRR Ingo Steinwart
2020	J	`jnl`	Sobolev Norm Learning Rates for Regularized Least-Squares Algorithms. J. Mach. Learn. Res. Simon Fischer, Ingo Steinwart
2020	J	`jnl`	Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent. CoRR David Holzmüller, Ingo Steinwart
2020	J	`jnl`	Which Minimizer Does My Neural Network Converge To? CoRR Manuel Nonnenmacher, David Reeb, Ingo Steinwart
2019	J	`jnl`	A Sober Look at Neural Network Initializations. CoRR Ingo Steinwart
2019	J	`jnl`	Best-scored Random Forest Classification. CoRR Hanyuan Hang, Xiaoyu Liu, Ingo Steinwart
2019	J	`jnl`	Global Minima of DNNs: The Plenty Pantry. CoRR Nicole Mücke, Ingo Steinwart
2019	J	`jnl`	Learning rates for kernel-based expectile regression. Mach. Learn. Muhammad Farooq, Ingo Steinwart
2018	J	`jnl`	Kernel Density Estimation for Dynamical Systems. J. Mach. Learn. Res. Hanyuan Hang, Ingo Steinwart, Yunlong Feng, Johan A. K. Suykens
2018	J	`jnl`	Optimal Learning with Anisotropic Gaussian SVMs. CoRR Hanyuan Hang, Ingo Steinwart
2017	J	`jnl`	A short note on the comparison of interpolation widths, entropy numbers, and Kolmogorov widths. J. Approx. Theory Ingo Steinwart
2017	J	`jnl`	An SVM-like approach for expectile regression. Comput. Stat. Data Anal. Muhammad Farooq, Ingo Steinwart
2017	J	`jnl`	Learning Rates for Kernel-Based Expectile Regression. CoRR Muhammad Farooq, Ingo Steinwart
2017	A	`conf`	Spatial Decompositions for Large Scale SVMs. AISTATS Philipp Thomann, Ingrid Blaschzyk, Mona Meister, Ingo Steinwart
2017	J	`jnl`	liquidSVM: A Fast and Versatile SVM package. CoRR Ingo Steinwart, Philipp Thomann
2016	J	`jnl`	Learning Theory Estimates with Observations from General Stationary Stochastic Processes. Neural Comput. Hanyuan Hang, Yunlong Feng, Ingo Steinwart, Johan A. K. Suykens
2016	J	`jnl`	Learning theory estimates with observations from general stationary stochastic processes. CoRR Hanyuan Hang, Yunlong Feng, Ingo Steinwart, Johan A. K. Suykens
2016	J	`jnl`	Learning with Hierarchical Gaussian Kernels. CoRR Ingo Steinwart, Philipp Thomann, Nico Schmid
2016	J	`jnl`	Optimal Learning Rates for Localized SVMs. J. Mach. Learn. Res. Mona Meister, Ingo Steinwart
2016	J	`jnl`	Spatial Decompositions for Large Scale SVMs. CoRR Philipp Thomann, Ingo Steinwart, Ingrid Blaschzyk, Mona Meister
2015	J	`jnl`	Towards an Axiomatic Approach to Hierarchical Clustering of Measures. CoRR Philipp Thomann, Ingo Steinwart, Nico Schmid
2015	J	`jnl`	Towards an axiomatic approach to hierarchical clustering of measures. J. Mach. Learn. Res. Philipp Thomann, Ingo Steinwart, Nico Schmid
2014	A*	`conf`	Elicitation and Identification of Properties. COLT Ingo Steinwart, Chloé Pasin, Robert C. Williamson, Siyu Zhang
2014	J	`jnl`	Fast learning from α-mixing observations. J. Multivar. Anal. Hanyuan Hang, Ingo Steinwart
2013	A*	`ed.`	COLT 2013 - The 26th Annual Conference on Learning Theory, June 12-14, 2013, Princeton University, NJ, USA COLT Shai Shalev-Shwartz, Ingo Steinwart
2013	—	`conf`	Some Remarks on the Statistical Analysis of SVMs and Related Methods. Empirical Inference Ingo Steinwart
2012	A	`conf`	Consistency and Rates for Clustering with DBSCAN. AISTATS Bharath K. Sriperumbudur, Ingo Steinwart
2011	A*	`conf`	Adaptive Density Level Set Clustering. COLT Ingo Steinwart
2011	—	`conf`	Optimal learning rates for least squares SVMs using Gaussian kernels. NIPS Mona Eberts, Ingo Steinwart
2011	J	`jnl`	Training SVMs Without Offset. J. Mach. Learn. Res. Ingo Steinwart, Don R. Hush, Clint Scovel
2010	J	`jnl`	Radial kernels and their reproducing kernel Hilbert spaces. J. Complex. Clint Scovel, Don R. Hush, Ingo Steinwart, James Theiler
2010	—	`conf`	Universal Kernels on Non-Standard Input Spaces. NIPS Andreas Christmann, Ingo Steinwart
2010	C	`conf`	Using support vector machines for anomalous change detection. IGARSS Ingo Steinwart, James Theiler, Daniel Llamocca
2009	—	`conf`	Fast Learning from Non-i.i.d. Observations. NIPS Ingo Steinwart, Andreas Christmann
2009	J	`jnl`	Learning from dependent observations. J. Multivar. Anal. Ingo Steinwart, Don R. Hush, Clint Scovel
2009	A*	`conf`	Optimal Rates for Regularized Least Squares Regression. COLT Ingo Steinwart, Don R. Hush, Clint Scovel
2009	J	`jnl`	Oracle inequalities for support vector machines that are based on random entropy numbers. J. Complex. Ingo Steinwart
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	A*	`conf`	Gaps in Support Vector Optimization. COLT Nikolas List, Don R. Hush, Clint Scovel, Ingo Steinwart
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
2007	J	`jnl`	Stability of Unstable Learning Algorithms. Mach. Learn. Don R. Hush, Clint Scovel, Ingo Steinwart
2006	J	`jnl`	An Explicit Description of the Reproducing Kernel Hilbert Spaces of Gaussian RBF Kernels. IEEE Trans. Inf. Theory Ingo Steinwart, Don R. Hush, Clint Scovel
2006	—	`conf`	An Oracle Inequality for Clipped Regularized Risk Minimizers. NIPS Ingo Steinwart, Don R. Hush, Clint Scovel
2006	A*	`conf`	Function Classes That Approximate the Bayes Risk. COLT Ingo Steinwart, Don R. Hush, Clint Scovel
2006	J	`jnl`	QP Algorithms with Guaranteed Accuracy and Run Time for Support Vector Machines. J. Mach. Learn. Res. Don R. Hush, Patrick Kelly, Clint Scovel, Ingo Steinwart
2005	J	`jnl`	A Classification Framework for Anomaly Detection. J. Mach. Learn. Res. Ingo Steinwart, Don R. Hush, Clint Scovel
2005	J	`jnl`	Consistency of support vector machines and other regularized kernel classifiers. IEEE Trans. Inf. Theory Ingo Steinwart
2005	A*	`conf`	Fast Rates for Support Vector Machines. COLT Ingo Steinwart, Clint Scovel
2004	—	`conf`	Density Level Detection is Classification. NIPS Ingo Steinwart, Don R. Hush, Clint Scovel
2004	J	`jnl`	Entropy of convex hulls--some Lorentz norm results. J. Approx. Theory Ingo Steinwart
2004	—	`conf`	Fast Rates to Bayes for Kernel Machines. NIPS Ingo Steinwart, Clint Scovel
2004	J	`jnl`	On Robustness Properties of Convex Risk Minimization Methods for Pattern Recognition. J. Mach. Learn. Res. Andreas Christmann, Ingo Steinwart
2003	J	`jnl`	On the Optimal Parameter Choice for v-Support Vector Machines. IEEE Trans. Pattern Anal. Mach. Intell. Ingo Steinwart
2003	—	`conf`	Sparseness of Support Vector Machines---Some Asymptotically Sharp Bounds. NIPS Ingo Steinwart
2003	J	`jnl`	Sparseness of Support Vector Machines. J. Mach. Learn. Res. Ingo Steinwart
2002	J	`jnl`	Support Vector Machines are Universally Consistent. J. Complex. Ingo Steinwart
2001	J	`jnl`	On the Influence of the Kernel on the Consistency of Support Vector Machines. J. Mach. Learn. Res. Ingo Steinwart