Matthias Hein - Google Scholar Citations

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Citations	4831	3532
h-index	38	33
i10-index	71	65

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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
Antoine GautierPhD student, Saarland UniversityVerified email at uni-saarland.de
Anna KhorevaResearch Scientist at Bosch Center for Artificial IntelligenceVerified email at bosch.com
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
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	282	2005
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	273	2016
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	227	2007
Spectral clustering based on the graph p-Laplacian T Bühler, M Hein Proceedings of the 26th Annual International Conference on Machine Learning …, 2009	224	2009
Hilbertian metrics and positive definite kernels on probability measures M Hein, O Bousquet Max Planck Institute for Biological Cybernetics, 2004	193	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	182	2005
Manifold denoising M Hein, M Maier Advances in neural information processing systems, 561-568, 2007	174	2007
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	163	2017
Influence of graph construction on graph-based clustering measures M Maier, UV Luxburg, M Hein Advances in neural information processing systems, 1025-1032, 2009	156	2009
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	147	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	133	2017
Measure based regularization O Bousquet, O Chapelle, M Hein Advances in Neural Information Processing Systems, 1221-1228, 2004	131	2004
The loss surface of deep and wide neural networks Q Nguyen, M Hein Proceedings of the 34th International Conference on Machine Learning-Volume …, 2017	129	2017
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	117	2013
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	102	2009
Learning using privileged information: SVM+ and weighted SVM M Lapin, M Hein, B Schiele Neural Networks 53, 95-108, 2014	89	2014
Getting lost in space: Large sample analysis of the commute distance U Von Luxburg, A Radl, M Hein Advances in Neural Information Processing Systems 23, 2622-2630, 2010	89	2010
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	89	2009
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	76	2011

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