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


Oct 19, 2021
Manuel Nonnenmacher, Thomas Pfeil, Ingo Steinwart, David Reeb


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Fast and Sample-Efficient Interatomic Neural Network Potentials for Molecules and Materials Based on Gaussian Moments


Sep 20, 2021
Viktor Zaverkin, David Holzmüller, Ingo Steinwart, Johannes Kästner

* Manuscript accepted for publication in J. Chem. Theory Comput.; Code published at https://gitlab.com/zaverkin_v/gmnn 

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Which Minimizer Does My Neural Network Converge To?


Nov 04, 2020
Manuel Nonnenmacher, David Reeb, Ingo Steinwart

* 27 pages incl. appendix 

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Reproducing Kernel Hilbert Spaces Cannot Contain all Continuous Functions on a Compact Metric Space


Mar 13, 2020
Ingo Steinwart

* 2 pages 

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Training Two-Layer ReLU Networks with Gradient Descent is Inconsistent


Feb 12, 2020
David Holzmüller, Ingo Steinwart


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Best-scored Random Forest Classification


May 27, 2019
Hanyuan Hang, Xiaoyu Liu, Ingo Steinwart


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Global Minima of DNNs: The Plenty Pantry


May 25, 2019
Nicole Mücke, Ingo Steinwart


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A Sober Look at Neural Network Initializations


Mar 27, 2019
Ingo Steinwart


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Spatial Decompositions for Large Scale SVMs


Feb 08, 2018
Philipp Thomann, Ingrid Blaschzyk, Mona Meister, Ingo Steinwart

* Proceedings of Machine Learning Research Volume 54: Proceedings of the 20th International Conference on Artificial Intelligence and Statistics 2017 (A. Singh and J. Zhu, eds.), pp. 1329-1337, 2017 

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Strictly proper kernel scores and characteristic kernels on compact spaces


Dec 14, 2017
Ingo Steinwart, Johanna F. Ziegel


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Adaptive Clustering Using Kernel Density Estimators


Aug 17, 2017
Ingo Steinwart, Bharath K. Sriperumbudur, Philipp Thomann


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Learning Rates for Kernel-Based Expectile Regression


Feb 27, 2017
Muhammad Farooq, Ingo Steinwart


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Sobolev Norm Learning Rates for Regularized Least-Squares Algorithm


Feb 23, 2017
Simon Fischer, Ingo Steinwart


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liquidSVM: A Fast and Versatile SVM package


Feb 22, 2017
Ingo Steinwart, Philipp Thomann


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Learning with Hierarchical Gaussian Kernels


Dec 02, 2016
Ingo Steinwart, Philipp Thomann, Nico Schmid


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Kernel Density Estimation for Dynamical Systems


Jul 13, 2016
Hanyuan Hang, Ingo Steinwart, Yunlong Feng, Johan A. K. Suykens


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Learning theory estimates with observations from general stationary stochastic processes


May 10, 2016
Hanyuan Hang, Yunlong Feng, Ingo Steinwart, Johan A. K. Suykens

* arXiv admin note: text overlap with arXiv:1501.03059 

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Fully adaptive density-based clustering


Oct 28, 2015
Ingo Steinwart

* Annals of Statistics 2015, Vol. 43, No. 5, 2132-2167 
* Published at http://dx.doi.org/10.1214/15-AOS1331 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

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Representation of Quasi-Monotone Functionals by Families of Separating Hyperplanes


Aug 21, 2015
Ingo Steinwart

* 23 pages 

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Towards an Axiomatic Approach to Hierarchical Clustering of Measures


Aug 15, 2015
Philipp Thomann, Ingo Steinwart, Nico Schmid

* Journal of Machine Learning Research. 16(Sep):1949-2002, 2015 

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Optimal Learning Rates for Localized SVMs


Jul 23, 2015
Mona Eberts, Ingo Steinwart

* 68 pages, 20 figures, and 11 tables 

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An SVM-like Approach for Expectile Regression


Jul 14, 2015
Muhammad Farooq, Ingo Steinwart


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Fast rates for support vector machines using Gaussian kernels


Aug 14, 2007
Ingo Steinwart, Clint Scovel

* Annals of Statistics 2007, Vol. 35, No. 2, 575-607 
* Published at http://dx.doi.org/10.1214/009053606000001226 in the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

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Learning from dependent observations


Jul 02, 2007
Ingo Steinwart, Don Hush, Clint Scovel

* submitted to Journal of Multivariate Analysis 

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