Matthias Hein

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Bernt SchieleProfessor, Max Planck Institute for Informatics, Saarland Informatics Campus, Saarland UniversityVerified email at mpi-inf.mpg.de
Francesco CroceUniversity of TübingenVerified email at cs.uni-saarland.de
Ulrike von LuxburgUniversity of TübingenVerified email at informatik.uni-tuebingen.de
Maksym AndriushchenkoPhD student at EPFLVerified email at epfl.ch
Francesco TudiscoGran Sasso Science InstituteVerified email at gssi.it
Anna KhorevaResearch Group Leader at Bosch Center for Artificial IntelligenceVerified email at bosch.com
Antoine GautierQuantPiVerified email at quantpi.com
Thomas BühlerAviraVerified email at avira.com
Maksim LapinSenior Applied Scientist, AmazonVerified email at amazon.com
Olivier BousquetGoogleVerified email at m4x.org
Rodrigo BenensonResearch scientist at GoogleVerified email at google.com
Syama Sundar RangapuramAmazonVerified email at amazon.de
Simon SetzerSaarland UniversityVerified email at mia.uni-saarland.de
Florian SteinkeProfessor, Energy Information Networks & Systems, TU DarmstadtVerified email at eins.tu-darmstadt.de
Pedro MercadoSaarland University - University of Tübingen - AWS AI Labs, Amazon Web ServicesVerified email at amazon.com
Mahesh Chandra MukkamalaMathematical Consultant | Mathematical Optimization, Machine Learning and Applied MathematicsVerified email at math.uni-tuebingen.de
Bernhard SchölkopfDirector, Max Planck Institute for Intelligent Systems; Professor at ETH Zürich, and DistinguishedVerified email at tuebingen.mpg.de
Zeynep AkataProfessor of Computer Science, University of TübingenVerified email at uni-tuebingen.de
Moritz GerlachUniversity of PotsdamVerified email at uni-potsdam.de
Pavlo LutsikGerman Cancer Research CenterVerified email at dkfz.de

Matthias Hein

Professor of Computer Science, University of Tübingen

Verified email at uni-tuebingen.de - Homepage

Machine Learning Optimization Statistics


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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	629	2016
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	547	2017
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks F Croce, M Hein International conference on machine learning, 2206-2216, 2020	422	2020
Formal guarantees on the robustness of a classifier against adversarial manipulation M Hein, M Andriushchenko NIPS 2017, 2017	381	2017
From graphs to manifolds–weak and strong pointwise consistency of graph Laplacians M Hein, JY Audibert, U Luxburg International Conference on Computational Learning Theory, 470-485, 2005	354	2005
Spectral clustering based on the graph p-Laplacian T Bühler, M Hein Proceedings of the 26th annual international conference on machine learning …, 2009	314	2009
Graph laplacians and their convergence on random neighborhood graphs. M Hein, JY Audibert, U Luxburg Journal of Machine Learning Research 8 (6), 2007	299	2007
Why relu networks yield high-confidence predictions far away from the training data and how to mitigate the problem M Hein, M Andriushchenko, J Bitterwolf Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019	245	2019
The loss surface of deep and wide neural networks Q Nguyen, M Hein International conference on machine learning, 2603-2612, 2017	241	2017
Intrinsic dimensionality estimation of submanifolds in R^d M Hein, JY Audibert Proceedings of the 22nd international conference on Machine learning, 289-296, 2005	241	2005
Square attack: a query-efficient black-box adversarial attack via random search M Andriushchenko, F Croce, N Flammarion, M Hein European Conference on Computer Vision, 484-501, 2020	233	2020
Manifold denoising M Hein, M Maier Advances in neural information processing systems 19, 2006	230	2006
Variants of rmsprop and adagrad with logarithmic regret bounds MC Mukkamala, M Hein International conference on machine learning, 2545-2553, 2017	215	2017
Hilbertian metrics and positive definite kernels on probability measures M Hein, O Bousquet International Workshop on Artificial Intelligence and Statistics, 136-143, 2005	211	2005
Influence of graph construction on graph-based clustering measures M Maier, U Luxburg, M Hein Advances in neural information processing systems 21, 2008	205	2008
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 23, 2010	204	2010
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	173	2013
Disentangling adversarial robustness and generalization D Stutz, M Hein, B Schiele Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019	155	2019
Getting lost in space: Large sample analysis of the resistance distance U Luxburg, A Radl, M Hein Advances in neural information processing systems 23, 2010	152	2010
Measure based regularization O Bousquet, O Chapelle, M Hein Advances in Neural Information Processing Systems 16, 2003	148	2003

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