Steinwart, Ingo

40 publications

MLJ 2025 Empirical Risk Minimization in the Interpolating Regime with Application to Neural Network Learning Nicole Mücke, Ingo Steinwart

NeurIPS 2024 Better by Default: Strong Pre-Tuned MLPs and Boosted Trees on Tabular Data David Holzmüller, Léo Grinsztajn, Ingo Steinwart

JMLR 2023 A Framework and Benchmark for Deep Batch Active Learning for Regression David Holzmüller, Viktor Zaverkin, Johannes Kästner, Ingo Steinwart

JMLR 2023 Adaptive Clustering Using Kernel Density Estimators Ingo Steinwart, Bharath K. Sriperumbudur, Philipp Thomann

NeurIPS 2023 Mind the Spikes: Benign Overfitting of Kernels and Neural Networks in Fixed Dimension Moritz Haas, David Holzmüller, Ulrike V. Luxburg, Ingo Steinwart

JMLR 2022 Improved Classification Rates for Localized SVMs Ingrid Blaschzyk, Ingo Steinwart

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

JMLR 2022 Training Two-Layer ReLU Networks with Gradient Descent Is Inconsistent David Holzmüller, Ingo Steinwart

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

ECML-PKDD 2021 Which Minimizer Does My Neural Network Converge to? Manuel Nonnenmacher, David Reeb, Ingo Steinwart

JMLR 2020 Sobolev Norm Learning Rates for Regularized Least-Squares Algorithms Simon Fischer, Ingo Steinwart

MLJ 2019 Learning Rates for Kernel-Based Expectile Regression Muhammad Farooq, Ingo Steinwart

JMLR 2018 Kernel Density Estimation for Dynamical Systems Hanyuan Hang, Ingo Steinwart, Yunlong Feng, Johan A.K. Suykens

AISTATS 2017 Spatial Decompositions for Large Scale SVMs Philipp Thomann, Ingrid Blaschzyk, Mona Meister, Ingo Steinwart

JMLR 2016 Optimal Learning Rates for Localized SVMs Mona Meister, Ingo Steinwart

JMLR 2015 Towards an Axiomatic Approach to Hierarchical Clustering of Measures Philipp Thomann, Ingo Steinwart, Nico Schmid

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

COLT 2013 COLT 2013 - The 26th Annual Conference on Learning Theory, June 12-14, 2013, Princeton University, NJ, USA Shai Shalev-Shwartz, Ingo Steinwart

AISTATS 2012 Consistency and Rates for Clustering with DBSCAN Bharath Sriperumbudur, Ingo Steinwart

COLT 2011 Adaptive Density Level Set Clustering Ingo Steinwart

NeurIPS 2011 Optimal Learning Rates for Least Squares SVMs Using Gaussian Kernels Mona Eberts, Ingo Steinwart

JMLR 2011 Training SVMs Without Offset Ingo Steinwart, Don Hush, Clint Scovel

NeurIPS 2010 Universal Kernels on Non-Standard Input Spaces Andreas Christmann, Ingo Steinwart

NeurIPS 2009 Fast Learning from Non-I.i.d. Observations Ingo Steinwart, Andreas Christmann

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

NeurIPS 2008 Sparsity of SVMs That Use the Epsilon-Insensitive Loss Ingo Steinwart, Andreas Christmann

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

NeurIPS 2007 How SVMs Can Estimate Quantiles and the Median Andreas Christmann, Ingo Steinwart

MLJ 2007 Stability of Unstable Learning Algorithms Don R. Hush, Clint Scovel, Ingo Steinwart

NeurIPS 2006 An Oracle Inequality for Clipped Regularized Risk Minimizers Ingo Steinwart, Don Hush, Clint Scovel

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

JMLR 2006 QP Algorithms with Guaranteed Accuracy and Run Time for Support Vector Machines Don Hush, Patrick Kelly, Clint Scovel, Ingo Steinwart

JMLR 2005 A Classification Framework for Anomaly Detection Ingo Steinwart, Don Hush, Clint Scovel

COLT 2005 Fast Rates for Support Vector Machines Ingo Steinwart, Clint Scovel

NeurIPS 2004 Density Level Detection Is Classification Ingo Steinwart, Don Hush, Clint Scovel

NeurIPS 2004 Fast Rates to Bayes for Kernel Machines Ingo Steinwart, Clint Scovel

JMLR 2004 On Robustness Properties of Convex Risk Minimization Methods for Pattern Recognition Andreas Christmann, Ingo Steinwart

JMLR 2003 Sparseness of Support Vector Machines Ingo Steinwart

NeurIPS 2003 Sparseness of Support Vector Machines---Some Asymptotically Sharp Bounds Ingo Steinwart

JMLR 2001 On the Influence of the Kernel on the Consistency of Support Vector Machines (Kernel Machines Section) Ingo Steinwart