Ingo Steinwart

发表

How to Compare Different Loss Functions and Their Risks

Ingo Steinwart, 2007 .

Fast rates for support vector machines using Gaussian kernels

Ingo Steinwart, C. Scovel, 2007, 0708.1838.

QP Algorithms with Guaranteed Accuracy and Run Time for Support Vector Machines

Don R. Hush, Ingo Steinwart, Patrick Kelly, 2006, J. Mach. Learn. Res..

Approximate Duality

Ingo Steinwart, C. Scovel, D. Hush, 2007 .

LEARNING RATES FOR DENSITY LEVEL DETECTION

Ingo Steinwart, C. Scovel, D. Hush, 2005 .

Fast Rates for Support Vector Machines

Ingo Steinwart, Clint Scovel, Ingo Steinwart, 2005, COLT.

Some oracle inequalities for regularized boosting classifiers

Ingo Steinwart, 2009 .

A new concentration result for regularized risk minimizers

Ingo Steinwart, C. Scovel, D. Hush, 2006, math/0612779.

Consistency of support vector machines and other regularized kernel classifiers

Ingo Steinwart, Ingo Steinwart, 2005, IEEE Transactions on Information Theory.

Entropy of C(K)-Valued Operators

Ingo Steinwart, 2000 .

Entropy numbers of convex hulls and an application to learning algorithms

Ingo Steinwart, 2003 .

Optimal Rates for Regularized Least Squares Regression

Don R. Hush, Ingo Steinwart, Clint Scovel, 2009, COLT.

On the Influence of the Kernel on the Consistency of Support Vector Machines

Ingo Steinwart, Ingo Steinwart, 2002, J. Mach. Learn. Res..

Support vector machines

Andreas Christmann, Ingo Steinwart, Ingo Steinwart, 2008, Data Mining and Knowledge Discovery Handbook.

Mercer’s Theorem on General Domains: On the Interaction between Measures, Kernels, and RKHSs

Ingo Steinwart, C. Scovel, 2012 .

Estimating conditional quantiles with the help of the pinball loss

Ingo Steinwart, A. Christmann, 2011, 1102.2101.

Consistency and robustness of kernel-based regression in convex risk minimization

Ingo Steinwart, A. Christmann, 2007, 0709.0626.

Fast rates to bayes for kernel methods

Ingo Steinwart, C. Scovel, 2004, NIPS 2004.

When do Support Vector Machines learn fast ?

Ingo Steinwart, C. Scovel, 2022 .

Consistency and robustness of kernel based regression

Ingo Steinwart, A. Christmann, 2005 .

On consistency and robustness properties of support vector machines for heavy-tailed distributions

Arnout Van Messem, Ingo Steinwart, A. Christmann, 2010 .

Support Vector Machines are Universally Consistent

Ingo Steinwart, Ingo Steinwart, 2002, J. Complex..

Optimal learning rates for least squares SVMs using Gaussian kernels

Ingo Steinwart, Mona Eberts, Ingo Steinwart, 2011, NIPS.

A Classification Framework for Anomaly Detection

Don R. Hush, Ingo Steinwart, Clint Scovel, 2005, J. Mach. Learn. Res..

Sparseness of Support Vector Machines

Ingo Steinwart, Ingo Steinwart, 2003, J. Mach. Learn. Res..

Fully Adaptive Density-Based Clustering

Ingo Steinwart, 2014, 1409.8437.

Universal Kernels on Non-Standard Input Spaces

Andreas Christmann, Ingo Steinwart, Ingo Steinwart, 2010, NIPS.

Strictly proper kernel scores and characteristic kernels on compact spaces

Johanna F. Ziegel, Ingo Steinwart, J. Ziegel, 2017, Applied and Computational Harmonic Analysis.

On the Inuence of the Kernel on the Consistency of

Ingo Steinwart, 2001 .

Kernel Density Estimation for Dynamical Systems

Johan A. K. Suykens, Yunlong Feng, Ingo Steinwart, 2016, J. Mach. Learn. Res..

A Bernstein-type Inequality for Some Mixing Processes and Dynamical Systems with an Application to Learning

Ingo Steinwart, H. Hang, 2015, 1501.03059.

Forecasting the Evolution of Dynamical Systems from Noisy Observations

Ingo Steinwart, M. Anghel, 2007, 0707.4146.

Adaptive Density Level Set Clustering

Ingo Steinwart, Ingo Steinwart, 2011, COLT.

Optimal regression rates for SVMs using Gaussian kernels

Ingo Steinwart, Mona Eberts, 2013 .

Metric entropy of convex hulls in type p spaces-the critical case

Ingo Steinwart, J. Creutzig, 2001 .

Best-scored Random Forest Classification pdf

Xiaoyu Liu, Ingo Steinwart, Hanyuan Hang, 2019, ArXiv.

Improved Classification Rates under Refined Margin Conditions

Ingo Steinwart, Ingrid Blaschzyk, 2016, 1610.09109.

Hypothesis Testing for Validation and Certification

Ingo Steinwart, Clint Scovel, Ingo Steinwart, 2013 .

PAC-Bayesian Bounds for Deep Gaussian Processes

Ingo Steinwart, R. Foll, 2019, 1909.09985.

Consistency of kernel-based quantile regression

Andreas Christmann, Ingo Steinwart, Ingo Steinwart, 2008 .

Sparsity of SVMs that use the ∊-insensitive loss

Ingo Steinwart, A. Christmann, 2008, NIPS 2008.

Mini-Workshop: Mathematics of Machine Learning

G. Lugosi, S. Geer, L. Györfi, 2011 .

On Robustness Properties of Convex Risk Minimization Methods for Pattern Recognition

Andreas Christmann, Ingo Steinwart, Ingo Steinwart, 2004, J. Mach. Learn. Res..

Improved Classification Rates for Localized SVMs

Ingo Steinwart, Ingrid Blaschzyk, 2019, J. Mach. Learn. Res..

2 . Detecting Density Levels is a Classification Problem

Ingo Steinwart, C. Scovel, 2005 .

Representation of quasi‐monotone functionals by families of separating hyperplanes

Ingo Steinwart, 2015, 1508.05249.

Fast Rates to Bayes for Kernel Machines

Ingo Steinwart, Clint Scovel, Ingo Steinwart, 2004, NIPS.

On the Existence of Continuous Modifications of Vector-Valued Random Fields

Ingo Steinwart, Ingo Steinwart, Katrin Mittmann, 2003 .

On Consistency and Robustness of Support Vector Machines

Andreas Christmann, Ingo Steinwart, Ingo Steinwart, 2009 .

Provably Fast Algorithms for Anomaly Detection

Ingo Steinwart, C. Scovel, D. Hush, 2013 .

SOSP: Efficiently Capturing Global Correlations by Second-Order Structured Pruning

Ingo Steinwart, Thomas Pfeil, D. Reeb, 2021, ICLR.

A new concentration result for regularized risk minimizers

Ingo Steinwart, C. Scovel, D. Hush, 2006, math/0612779.

Support vector machine

Andreas Christmann, Ingo Steinwart, Ingo Steinwart, 2013 .

On the Influence of the Kernel on the Generalization Ability of Support Vector Machines

Ingo Steinwart, Ingo Steinwart, 2001 .

Mercer’s Theorem on General Domains: On the Interaction between Measures, Kernels, and RKHSs

Ingo Steinwart, C. Scovel, 2012, Constructive Approximation.

Convergence Types and Rates in Generic Karhunen-Loève Expansions with Applications to Sample Path Properties

Ingo Steinwart, 2018, Potential Analysis.

Learning rates for kernel-based expectile regression

Ingo Steinwart, M. Farooq, 2017, Machine Learning.

Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers

Philipp Hennig, Ingo Steinwart, Jonathan Wenger, 2022, ArXiv.

Which Minimizer Does My Neural Network Converge To?

Ingo Steinwart, D. Reeb, Manuel Nonnenmacher, 2020, ECML/PKDD.

Optimal Learning Rates for Localized SVMs

Ingo Steinwart, Mona Meister, Ingo Steinwart, 2015, J. Mach. Learn. Res..

Learning with Hierarchical Gaussian Kernels pdf

Nico Schmid, Ingo Steinwart, Philipp Thomann, 2016, ArXiv.

Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent pdf

Ingo Steinwart, David Holzmüller, Ingo Steinwart, 2020, J. Mach. Learn. Res..

Towards an axiomatic approach to hierarchical clustering of measures pdf

Nico Schmid, Ingo Steinwart, Philipp Thomann, 2015, Journal of machine learning research.

A closer look at covering number bounds for Gaussian kernels

Ingo Steinwart, Simon Fischer, Ingo Steinwart, 2019, J. Complex..

Oracle inequalities for support vector machines that are based on random entropy numbers

Ingo Steinwart, Ingo Steinwart, 2009, J. Complex..

Support Vector Machines for Forecasting the Evolution of an Unknown Ergodic Dynamical System from Observations with Unknown Noise

Marian Anghel, Ingo Steinwart, Ingo Steinwart, 2022 .

Robust learning from bites for data mining

Andreas Christmann, Mia Hubert, Ingo Steinwart, 2007, Comput. Stat. Data Anal..

Adaptive Clustering Using Kernel Density Estimators

Ingo Steinwart, Bharath K. Sriperumbudur, Ingo Steinwart, 2017, 1708.05254.

Measuring the Capacity of Sets of Functions in the Analysis of ERM

Ingo Steinwart, Ingo Steinwart, 2015 .

An Explicit Description of the Reproducing Kernel Hilbert Spaces of Gaussian RBF Kernels

Don R. Hush, Ingo Steinwart, Clint Scovel, 2006, IEEE Transactions on Information Theory.

Consistency and Rates for Clustering with DBSCAN

Ingo Steinwart, Bharath K. Sriperumbudur, Ingo Steinwart, 2012, AISTATS.

A Sober Look at Neural Network Initializations pdf

Ingo Steinwart, Ingo Steinwart, 2019, ArXiv.

Learning from dependent observations

Don R. Hush, Ingo Steinwart, Clint Scovel, 2007, J. Multivar. Anal..

Learning rates for kernel-based expectile regression

Ingo Steinwart, Muhammad Farooq, Ingo Steinwart, 2018, Machine Learning.

Fast Learning from Non-i.i.d. Observations

Andreas Christmann, Ingo Steinwart, Ingo Steinwart, 2009, NIPS.

Sobolev Norm Learning Rates for Regularized Least-Squares Algorithms

Ingo Steinwart, Simon Fischer, Ingo Steinwart, 2017, J. Mach. Learn. Res..

Convergence Types and Rates in Generic Karhunen-Loève Expansions with Applications to Sample Path Properties

Ingo Steinwart, Ingo Steinwart, 2014, Potential Analysis.

Some Remarks on the Statistical Analysis of SVMs and Related Methods

Ingo Steinwart, Ingo Steinwart, 2013, Empirical Inference.

How SVMs can estimate quantiles and the median

Andreas Christmann, Ingo Steinwart, Ingo Steinwart, 2007, NIPS.

Elicitation and Identification of Properties

Siyu Zhang, Ingo Steinwart, Robert C. Williamson, 2014, COLT.

Gaps in Support Vector Optimization

Don R. Hush, Ingo Steinwart, Clint Scovel, 2007, COLT.

liquidSVM: A Fast and Versatile SVM package pdf

Ingo Steinwart, Philipp Thomann, Ingo Steinwart, 2017, ArXiv.

Training SVMs Without Offset

Don R. Hush, Ingo Steinwart, Clint Scovel, 2011, J. Mach. Learn. Res..

Using support vector machines for anomalous change detection

Daniel Llamocca, James Theiler, Ingo Steinwart, 2010, 2010 IEEE International Geoscience and Remote Sensing Symposium.

Translation invariant maps on function spaces over locally compact groups

E. A. Sánchez-Pérez, Ingo Steinwart, A. Defant, 2019, Journal of Mathematical Analysis and Applications.

A short note on the comparison of interpolation widths, entropy numbers, and Kolmogorov widths

Ingo Steinwart, Ingo Steinwart, 2016, J. Approx. Theory.

Optimal Learning with Anisotropic Gaussian SVMs pdf

Ingo Steinwart, Hanyuan Hang, Ingo Steinwart, 2018, ArXiv.

Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers

Philipp Hennig, Ingo Steinwart, Jonathan Wenger, 2022, ArXiv.

Fast learning from α-mixing observations

Ingo Steinwart, Hanyuan Hang, Ingo Steinwart, 2014, J. Multivar. Anal..

Sparseness of Support Vector Machines---Some Asymptotically Sharp Bounds

Ingo Steinwart, Ingo Steinwart, 2003, NIPS.

Adaptive learning rates for support vector machines working on data with low intrinsic dimension

Ingo Steinwart, Thomas Hamm, Ingo Steinwart, 2020, The Annals of Statistics.

SVM Learning Rates for Data with Low Intrinsic Dimension

Ingo Steinwart, Thomas Hamm, 2020 .

Radial kernels and their reproducing kernel Hilbert spaces

Don R. Hush, James Theiler, Ingo Steinwart, 2010, J. Complex..

Intrinsic Dimension Adaptive Partitioning for Kernel Methods

Ingo Steinwart, Thomas Hamm, Ingo Steinwart, 2021, SIAM Journal on Mathematics of Data Science.

On the Optimal Parameter Choice for v-Support Vector Machines

Ingo Steinwart, Ingo Steinwart, 2003, IEEE Trans. Pattern Anal. Mach. Intell..

Which data-dependent bounds are suitable for SVM ’ s ? ⋆

Ingo Steinwart, Ingo Steinwart, 2010 .

Density Level Detection is Classification

Don R. Hush, Ingo Steinwart, Clint Scovel, 2004, NIPS.

An SVM-like approach for expectile regression

M. Farooq, I. Steinwart, Ingo Steinwart, 2015, Comput. Stat. Data Anal..

Learning from dependent observations

Ingo Steinwart, C. Scovel, D. Hush, 2007, J. Multivar. Anal..

Optimal Learning with Anisotropic Gaussian SVMs

Ingo Steinwart, H. Hang, Ingo Steinwart, 2018, ArXiv.

Physics-Informed Gaussian Process Regression Generalizes Linear PDE Solvers

Philipp Hennig, Ingo Steinwart, Jonathan Wenger, 2022, ArXiv.