Matthias Hein - Google Scholar Citations

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	All	Since 2015
Citations	5004	3450
h-index	39	32
i10-index	76	68

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Bernt SchieleProfessor, Max Planck Institute for Informatics, Saarland Informatics Campus, Saarland UniversityVerified email at mpi-inf.mpg.de
Ulrike von LuxburgUniversity of TuebingenVerified email at informatik.uni-tuebingen.de
Quynh NguyenDepartment of Mathematics and Computer Science, Saarland Informatics Campus, Saarland UniversityVerified email at cs.uni-saarland.de
Olivier BousquetGoogleVerified email at m4x.org
Thomas BühlerAviraVerified email at avira.com
Maksim LapinComputer Vision Scientist, Amazon BerlinVerified email at amazon.com
Francesco TudiscoGran Sasso Science InstituteVerified email at gssi.it
Anna KhorevaResearch Scientist at Bosch Center for Artificial IntelligenceVerified email at bosch.com
Antoine GautierPhD student, Saarland UniversityVerified email at uni-saarland.de
Florian SteinkeProfessor, Energy Information Networks & Systems, TU DarmstadtVerified email at eins.tu-darmstadt.de
Syama Sundar RangapuramAmazonVerified email at amazon.de
Maksym AndriushchenkoPhD student, EPFLVerified email at epfl.ch
Simon SetzerSaarland UniversityVerified email at mia.uni-saarland.de
Rodrigo BenensonResearch scientist at GoogleVerified email at google.com
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems; and Distinguished Amazon ScholarVerified email at tuebingen.mpg.de
Francesco CroceUniversity of TübingenVerified email at cs.uni-saarland.de
Pedro MercadoSaarland University - University of TübingenVerified email at cs.uni-saarland.de
Mahesh Chandra MukkamalaMathematical Optimization Group, Saarland UniversityVerified email at math.uni-sb.de
Zeynep AkataProfessor of Computer Science, University of TübingenVerified email at uni-tuebingen.de
Pavlo LutsikGerman Cancer Research CenterVerified email at dkfz.de

Matthias Hein

Professor of Computer Science, University of Tuebingen

Verified email at uni-tuebingen.de - Homepage

Machine Learning Optimization Statistics


Title	Cited by	Year
Latent embeddings for zero-shot classification Y Xian, Z Akata, G Sharma, Q Nguyen, M Hein, B Schiele Proceedings of the IEEE Conference on Computer Vision and Pattern …, 2016	290	2016
From graphs to manifolds–weak and strong pointwise consistency of graph Laplacians M Hein, JY Audibert, U Von Luxburg International Conference on Computational Learning Theory, 470-485, 2005	283	2005
Spectral clustering based on the graph p-Laplacian T Bühler, M Hein Proceedings of the 26th Annual International Conference on Machine Learning …, 2009	230	2009
Graph Laplacians and their convergence on random neighborhood graphs M Hein, JY Audibert, U Luxburg Journal of Machine Learning Research 8 (Jun), 1325-1368, 2007	225	2007
Hilbertian metrics and positive definite kernels on probability measures M Hein, O Bousquet Max Planck Institute for Biological Cybernetics, 2004	191	2004
Intrinsic dimensionality estimation of submanifolds in R^d M Hein, JY Audibert Proceedings of the 22nd international conference on Machine learning, 289-296, 2005	189	2005
Manifold denoising M Hein, M Maier Advances in neural information processing systems, 561-568, 2007	180	2007
Influence of graph construction on graph-based clustering measures M Maier, UV Luxburg, M Hein Advances in neural information processing systems, 1025-1032, 2009	160	2009
Simple does it: Weakly supervised instance and semantic segmentation A Khoreva, R Benenson, J Hosang, M Hein, B Schiele Proceedings of the IEEE conference on computer vision and pattern …, 2017	159	2017
An inverse power method for nonlinear eigenproblems with applications in 1-spectral clustering and sparse PCA M Hein, T Bühler Advances in Neural Information Processing Systems, 847-855, 2010	156	2010
Formal guarantees on the robustness of a classifier against adversarial manipulation M Hein, M Andriushchenko Advances in Neural Information Processing Systems, 2266-2276, 2017	146	2017
The loss surface of deep and wide neural networks Q Nguyen, M Hein Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017	133	2017
Measure based regularization O Bousquet, O Chapelle, M Hein Advances in Neural Information Processing Systems, 1221-1228, 2004	133	2004
Non-negative least squares for high-dimensional linear models: Consistency and sparse recovery without regularization M Slawski, M Hein Electronic Journal of Statistics 7, 3004-3056, 2013	126	2013
Getting lost in space: Large sample analysis of the resistance distance UV Luxburg, A Radl, M Hein Advances in Neural Information Processing Systems, 2622-2630, 2010	111	2010
Optimal construction of k-nearest-neighbor graphs for identifying noisy clusters M Maier, M Hein, U Von Luxburg Theoretical Computer Science 410 (19), 1749-1764, 2009	106	2009
Semi-supervised regression using Hessian energy with an application to semi-supervised dimensionality reduction KI Kim, F Steinke, M Hein Advances in Neural Information Processing Systems, 979-987, 2009	91	2009
Learning using privileged information: SVM+ and weighted SVM M Lapin, M Hein, B Schiele Neural Networks 53, 95-108, 2014	90	2014
Hitting and commute times in large random neighborhood graphs U Von Luxburg, A Radl, M Hein The Journal of Machine Learning Research 15 (1), 1751-1798, 2014	82	2014
Beyond spectral clustering-tight relaxations of balanced graph cuts M Hein, S Setzer Advances in neural information processing systems, 2366-2374, 2011	79	2011

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