Matthias Hein

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Citations	13017	9919
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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
Quynh NguyenIndependent ResearcherVerified email at quynhnguyen.org
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
Simon SetzerSaarland UniversityVerified email at mia.uni-saarland.de
Syama Sundar RangapuramAmazonVerified email at amazon.de
Florian SteinkeProfessor, Energy Information Networks & Systems, TU DarmstadtVerified email at eins.tu-darmstadt.de
Philipp HennigUniversity of Tübingen & Max Planck Institute for Intelligent SystemsVerified email at tue.mpg.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 ZürichVerified email at tuebingen.mpg.de
Zeynep AkataProfessor of Computer Science, University of TübingenVerified email at uni-tuebingen.de

Matthias Hein

Professor of Computer Science, University of Tübingen

Verified email at uni-tuebingen.de - Homepage

Machine Learning Optimization Statistics


Title Sort by citations Sort by year Sort by title	Cited by Cited by	Year
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	967	2020
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	738	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	731	2017
Square attack: a query-efficient black-box adversarial attack via random search M Andriushchenko, F Croce, N Flammarion, M Hein Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23 …, 2020	569	2020
Formal guarantees on the robustness of a classifier against adversarial manipulation M Hein, M Andriushchenko NIPS 2017, 2017	493	2017
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	407	2019
From Graphs to Manifolds-Weak and Strong Pointwise Consistency of Graph Laplacians. M Hein, JY Audibert, U Von Luxburg COLT 3559, 470-485, 2005	376	2005
Spectral clustering based on the graph p-Laplacian T Bühler, M Hein Proceedings of the 26th annual international conference on machine learning …, 2009	348	2009
Graph laplacians and their convergence on random neighborhood graphs. M Hein, JY Audibert, U Luxburg Journal of Machine Learning Research 8 (6), 2007	315	2007
Robustbench: a standardized adversarial robustness benchmark F Croce, M Andriushchenko, V Sehwag, E Debenedetti, N Flammarion, ... arXiv preprint arXiv:2010.09670, 2020	301	2020
Minimally distorted adversarial examples with a fast adaptive boundary attack F Croce, M Hein International Conference on Machine Learning, 2196-2205, 2020	297	2020
Variants of rmsprop and adagrad with logarithmic regret bounds MC Mukkamala, M Hein International conference on machine learning, 2545-2553, 2017	283	2017
The loss surface of deep and wide neural networks Q Nguyen, M Hein International conference on machine learning, 2603-2612, 2017	271	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	271	2005
Manifold denoising M Hein, M Maier Advances in neural information processing systems 19, 2006	246	2006
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	228	2010
Hilbertian metrics and positive definite kernels on probability measures M Hein, O Bousquet International Workshop on Artificial Intelligence and Statistics, 136-143, 2005	222	2005
Disentangling adversarial robustness and generalization D Stutz, M Hein, B Schiele Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern …, 2019	220	2019
Influence of graph construction on graph-based clustering measures M Maier, U Luxburg, M Hein Advances in neural information processing systems 21, 2008	212	2008
Non-negative least squares for high-dimensional linear models: Consistency and sparse recovery without regularization M Slawski, M Hein	202	2013

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