Matthias Hein

Publication Activity (10 Years)

Years Active: 2003-2024
Publications (10 Years): 148

Top Venues

CoRR

ICML

NeurIPS

CVPR

Darmstadt University of Technology, Germany (PHD)

Education History

Darmstadt University of Technology, Germany

PHD

Publications

Jan Nikolas Morshuis, Matthias Hein, Christian F. Baumgartner
Segmentation-guided MRI reconstruction for meaningfully diverse reconstructions. CoRR (2024)
Amit Peleg, Matthias Hein
Bias of Stochastic Gradient Descent or the Architecture: Disentangling the Effects of Overparameterization of Neural Networks. CoRR (2024)
Niclas Popp, Jan Hendrik Metzen, Matthias Hein
Zero-Shot Distillation for Image Encoders: How to Make Effective Use of Synthetic Data. CoRR (2024)
Christian Schlarmann, Naman Deep Singh, Francesco Croce, Matthias Hein
Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models. CoRR (2024)
Maximilian Müller, Matthias Hein
How to train your ViT for OOD Detection. CoRR (2024)
Francesco Croce, Matthias Hein
Segment (Almost) Nothing: Prompt-Agnostic Adversarial Attacks on Segmentation Models. SaTML (2024)
Valentyn Boreiko, Matthias Hein, Jan Hendrik Metzen
Identification of Fine-grained Systematic Errors via Controlled Scene Generation. CoRR (2024)
Valentyn Boreiko, Matthias Hein, Jan Hendrik Metzen
Identifying Systematic Errors in Object Detectors with the SCROD Pipeline. CoRR (2023)
Václav Vorácek, Matthias Hein
Sound Randomized Smoothing in Floating-Point Arithmetic. ICLR (2023)
Naman D. Singh, Francesco Croce, Matthias Hein
Revisiting Adversarial Training for ImageNet: Architectures, Training and Generalization across Threat Models. CoRR (2023)
Maksym Andriushchenko, Francesco Croce, Maximilian Müller, Matthias Hein, Nicolas Flammarion
A modern look at the relationship between sharpness and generalization. CoRR (2023)
Maksym Andriushchenko, Francesco Croce, Maximilian Müller, Matthias Hein, Nicolas Flammarion
A Modern Look at the Relationship between Sharpness and Generalization. ICML (2023)
Václav Vorácek, Matthias Hein
Improving l1-Certified Robustness via Randomized Smoothing by Leveraging Box Constraints. ICML (2023)
Christian Schlarmann, Matthias Hein
On the Adversarial Robustness of Multi-Modal Foundation Models. CoRR (2023)
Antoine Gautier, Francesco Tudisco, Matthias Hein
Nonlinear Perron-Frobenius Theorems for Nonnegative Tensors. SIAM Rev. 65 (2) (2023)
Julian Bitterwolf, Maximilian Müller, Matthias Hein
In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation. ICML (2023)
Maximilian Augustin, Yannic Neuhaus, Matthias Hein
Analyzing and Explaining Image Classifiers via Diffusion Guidance. CoRR (2023)
Valentyn Boreiko, Matthias Hein, Jan Hendrik Metzen
Identifying Systematic Errors in Object Detectors with the SCROD Pipeline. ICCV (Workshops) (2023)
Francesco Croce, Matthias Hein
Segment (Almost) Nothing: Prompt-Agnostic Adversarial Attacks on Segmentation Models. CoRR (2023)
Maksym Yatsura, Kaspar Sakmann, N. Grace Hua, Matthias Hein, Jan Hendrik Metzen
Certified Defences Against Adversarial Patch Attacks on Semantic Segmentation. ICLR (2023)
Indu Ilanchezian, Valentyn Boreiko, Laura Kühlewein, Ziwei Huang, Murat Seçkin Ayhan, Matthias Hein, Lisa M. Koch, Philipp Berens
Generating Realistic Counterfactuals for Retinal Fundus and OCT Images using Diffusion Models. CoRR (2023)
Naman Deep Singh, Francesco Croce, Matthias Hein
Revisiting Adversarial Training for ImageNet: Architectures, Training and Generalization across Threat Models. NeurIPS (2023)
Julian Bitterwolf, Maximilian Müller, Matthias Hein
In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation. CoRR (2023)
Maximilian Müller, Tiffany Vlaar, David Rolnick, Matthias Hein
Normalization Layers Are All That Sharpness-Aware Minimization Needs. NeurIPS (2023)
Maximilian Müller, Tiffany Vlaar, David Rolnick, Matthias Hein
Normalization Layers Are All That Sharpness-Aware Minimization Needs. CoRR (2023)
David Stutz, Nandhini Chandramoorthy, Matthias Hein, Bernt Schiele
Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure DNN Accelerators. IEEE Trans. Pattern Anal. Mach. Intell. 45 (3) (2023)
Yannic Neuhaus, Maximilian Augustin, Valentyn Boreiko, Matthias Hein
Spurious Features Everywhere - Large-Scale Detection of Harmful Spurious Features in ImageNet. ICCV (2023)
Francesco Croce, Naman D. Singh, Matthias Hein
Robust Semantic Segmentation: Strong Adversarial Attacks and Fast Training of Robust Models. CoRR (2023)
Christian Schlarmann, Matthias Hein
On the Adversarial Robustness of Multi-Modal Foundation Models. ICCV (Workshops) (2023)
Julian Bitterwolf, Alexander Meinke, Maximilian Augustin, Matthias Hein
Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities. CoRR (2022)
Francesco Croce, Matthias Hein
On the interplay of adversarial robustness and architecture components: patches, convolution and attention. CoRR (2022)
Francesco Croce, Maksym Andriushchenko, Naman D. Singh, Nicolas Flammarion, Matthias Hein
Sparse-RS: A Versatile Framework for Query-Efficient Sparse Black-Box Adversarial Attacks. AAAI (2022)
Francesco Croce, Matthias Hein
-Threat Models via Quick Fine-Tuning of Robust Classifiers. ICML (2022)
Yannic Neuhaus, Maximilian Augustin, Valentyn Boreiko, Matthias Hein
Spurious Features Everywhere - Large-Scale Detection of Harmful Spurious Features in ImageNet. CoRR (2022)
Maximilian Augustin, Valentyn Boreiko, Francesco Croce, Matthias Hein
Diffusion Visual Counterfactual Explanations. NeurIPS (2022)
Maximilian Augustin, Valentyn Boreiko, Francesco Croce, Matthias Hein
Diffusion Visual Counterfactual Explanations. CoRR (2022)
Jan Nikolas Morshuis, Sergios Gatidis, Matthias Hein, Christian F. Baumgartner
Adversarial Robustness of MR Image Reconstruction Under Realistic Perturbations. MLMIR@MICCAI (2022)
Agustinus Kristiadi, Matthias Hein, Philipp Hennig
Being a Bit Frequentist Improves Bayesian Neural Networks. AISTATS (2022)
Václav Vorácek, Matthias Hein
Provably Adversarially Robust Nearest Prototype Classifiers. CoRR (2022)
Valentyn Boreiko, Maximilian Augustin, Francesco Croce, Philipp Berens, Matthias Hein
Sparse Visual Counterfactual Explanations in Image Space. CoRR (2022)
Valentyn Boreiko, Indu Ilanchezian, Murat Seçkin Ayhan, Sarah Müller, Lisa M. Koch, Hanna Faber, Philipp Berens, Matthias Hein
Visual Explanations for the Detection of Diabetic Retinopathy from Retinal Fundus Images. MICCAI (2) (2022)
Alexander Meinke, Julian Bitterwolf, Matthias Hein
Provably Adversarially Robust Detection of Out-of-Distribution Data (Almost) for Free. NeurIPS (2022)
Jan Nikolas Morshuis, Sergios Gatidis, Matthias Hein, Christian F. Baumgartner
Adversarial Robustness of MR Image Reconstruction under Realistic Perturbations. CoRR (2022)
Julian Bitterwolf, Alexander Meinke, Maximilian Augustin, Matthias Hein
Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities. ICML (2022)
Václav Vorácek, Matthias Hein
Sound Randomized Smoothing in Floating-Point Arithmetics. CoRR (2022)
Francesco Croce, Sven Gowal, Thomas Brunner, Evan Shelhamer, Matthias Hein, A. Taylan Cemgil
Evaluating the Adversarial Robustness of Adaptive Test-time Defenses. CoRR (2022)
Václav Vorácek, Matthias Hein
Provably Adversarially Robust Nearest Prototype Classifiers. ICML (2022)
Francesco Croce, Sven Gowal, Thomas Brunner, Evan Shelhamer, Matthias Hein, A. Taylan Cemgil
Evaluating the Adversarial Robustness of Adaptive Test-time Defenses. ICML (2022)
Daniel Heller, Patrick Ferber, Julian Bitterwolf, Matthias Hein, Jörg Hoffmann
Neural Network Heuristic Functions: Taking Confidence into Account. SOCS (2022)
Maksym Yatsura, Kaspar Sakmann, N. Grace Hua, Matthias Hein, Jan Hendrik Metzen
Certified Defences Against Adversarial Patch Attacks on Semantic Segmentation. CoRR (2022)
Valentyn Boreiko, Maximilian Augustin, Francesco Croce, Philipp Berens, Matthias Hein
Sparse Visual Counterfactual Explanations in Image Space. GCPR (2022)
David Stutz, Nandhini Chandramoorthy, Matthias Hein, Bernt Schiele
Bit Error Robustness for Energy-Efficient DNN Accelerators. MLSys (2021)
David Stutz, Nandhini Chandramoorthy, Matthias Hein, Bernt Schiele
Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure DNN Accelerators. CoRR (2021)
Francesco Croce, Maksym Andriushchenko, Vikash Sehwag, Edoardo Debenedetti, Nicolas Flammarion, Mung Chiang, Prateek Mittal, Matthias Hein
RobustBench: a standardized adversarial robustness benchmark. NeurIPS Datasets and Benchmarks (2021)
Francesco Croce, Matthias Hein
-APGD for Sparse Adversarial Attacks on Image Classifiers. ICML (2021)
Maksym Yatsura, Jan Hendrik Metzen, Matthias Hein
Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks. CoRR (2021)
Agustinus Kristiadi, Matthias Hein, Philipp Hennig
Learnable uncertainty under Laplace approximations. UAI (2021)
Agustinus Kristiadi, Matthias Hein, Philipp Hennig
An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence. NeurIPS (2021)
Alexander Meinke, Julian Bitterwolf, Matthias Hein
Provably Robust Detection of Out-of-distribution Data (almost) for free. CoRR (2021)
Francesco Croce, Matthias Hein
-threat models at the price of one and how to quickly fine-tune robust models to another threat model. CoRR (2021)
Maksym Yatsura, Jan Hendrik Metzen, Matthias Hein
Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks. NeurIPS (2021)
David Stutz, Matthias Hein, Bernt Schiele
Relating Adversarially Robust Generalization to Flat Minima. ICCV (2021)
Francesco Croce, Matthias Hein
-APGD for sparse adversarial attacks on image classifiers. CoRR (2021)
Antoine Gautier, Matthias Hein, Francesco Tudisco
The Global Convergence of the Nonlinear Power Method for Mixed-Subordinate Matrix Norms. J. Sci. Comput. 88 (1) (2021)
David Stutz, Matthias Hein, Bernt Schiele
Relating Adversarially Robust Generalization to Flat Minima. CoRR (2021)
Agustinus Kristiadi, Matthias Hein, Philipp Hennig
Being a Bit Frequentist Improves Bayesian Neural Networks. CoRR (2021)
Francesco Croce, Matthias Hein
Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack. ICML (2020)
Agustinus Kristiadi, Matthias Hein, Philipp Hennig
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks. ICML (2020)
Francesco Croce, Maksym Andriushchenko, Naman D. Singh, Nicolas Flammarion, Matthias Hein
Sparse-RS: a versatile framework for query-efficient sparse black-box adversarial attacks. CoRR (2020)
Julian Bitterwolf, Alexander Meinke, Matthias Hein
Provable Worst Case Guarantees for the Detection of Out-of-Distribution Data. CoRR (2020)
Agustinus Kristiadi, Matthias Hein, Philipp Hennig
Fixing Asymptotic Uncertainty of Bayesian Neural Networks with Infinite ReLU Features. CoRR (2020)
Nicolás García Trillos, Moritz Gerlach, Matthias Hein, Dejan Slepcev
Error Estimates for Spectral Convergence of the Graph Laplacian on Random Geometric Graphs Toward the Laplace-Beltrami Operator. Found. Comput. Math. 20 (4) (2020)
Francesco Croce, Matthias Hein
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks. ICML (2020)
David Stutz, Matthias Hein, Bernt Schiele
Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks. ICML (2020)
Maximilian Augustin, Alexander Meinke, Matthias Hein
Adversarial Robustness on In- and Out-Distribution Improves Explainability. CoRR (2020)
Agustinus Kristiadi, Matthias Hein, Philipp Hennig
Learnable Uncertainty under Laplace Approximations. CoRR (2020)
Julian Bitterwolf, Alexander Meinke, Matthias Hein
Certifiably Adversarially Robust Detection of Out-of-Distribution Data. NeurIPS (2020)
Francesco Croce, Matthias Hein
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks. CoRR (2020)
Maximilian Augustin, Matthias Hein
Out-distribution aware Self-training in an Open World Setting. CoRR (2020)
Francesco Croce, Jonas Rauber, Matthias Hein
Scaling up the Randomized Gradient-Free Adversarial Attack Reveals Overestimation of Robustness Using Established Attacks. Int. J. Comput. Vis. 128 (4) (2020)
Francesco Croce, Matthias Hein
Provable robustness against all adversarial $l_p$-perturbations for $p\geq 1$. ICLR (2020)
Alexander Meinke, Matthias Hein
Towards neural networks that provably know when they don't know. ICLR (2020)
David Stutz, Nandhini Chandramoorthy, Matthias Hein, Bernt Schiele
On Mitigating Random and Adversarial Bit Errors. CoRR (2020)
Agustinus Kristiadi, Matthias Hein, Philipp Hennig
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks. CoRR (2020)
Francesco Croce, Maksym Andriushchenko, Vikash Sehwag, Nicolas Flammarion, Mung Chiang, Prateek Mittal, Matthias Hein
RobustBench: a standardized adversarial robustness benchmark. CoRR (2020)
Maximilian Augustin, Alexander Meinke, Matthias Hein
Adversarial Robustness on In- and Out-Distribution Improves Explainability. ECCV (26) (2020)
Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion, Matthias Hein
Square Attack: A Query-Efficient Black-Box Adversarial Attack via Random Search. ECCV (23) (2020)
Antoine Gautier, Matthias Hein, Francesco Tudisco
Computing the norm of nonnegative matrices and the log-Sobolev constant of Markov chains. CoRR (2020)
Pedro Mercado, Francesco Tudisco, Matthias Hein
Spectral Clustering of Signed Graphs via Matrix Power Means. ICML (2019)
Maksym Andriushchenko, Matthias Hein
Provably robust boosted decision stumps and trees against adversarial attacks. NeurIPS (2019)
David Stutz, Matthias Hein, Bernt Schiele
Confidence-Calibrated Adversarial Training: Towards Robust Models Generalizing Beyond the Attack Used During Training. CoRR (2019)
Quynh Nguyen, Mahesh Chandra Mukkamala, Matthias Hein
On the loss landscape of a class of deep neural networks with no bad local valleys. ICLR (Poster) (2019)
Antoine Gautier, Francesco Tudisco, Matthias Hein
The Perron-Frobenius Theorem for Multihomogeneous Mappings. SIAM J. Matrix Anal. Appl. 40 (3) (2019)
Matthias Hein, Maksym Andriushchenko, Julian Bitterwolf
Why ReLU Networks Yield High-Confidence Predictions Far Away From the Training Data and How to Mitigate the Problem. CVPR (2019)
Pedro Mercado, Francesco Tudisco, Matthias Hein
Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs. NeurIPS (2019)
Francesco Croce, Maksym Andriushchenko, Matthias Hein
Provable Robustness of ReLU networks via Maximization of Linear Regions. AISTATS (2019)
Maksym Andriushchenko, Matthias Hein
Provably Robust Boosted Decision Stumps and Trees against Adversarial Attacks. CoRR (2019)
Maksym Andriushchenko, Francesco Croce, Nicolas Flammarion, Matthias Hein
Square Attack: a query-efficient black-box adversarial attack via random search. CoRR (2019)
Pedro Mercado, Francesco Tudisco, Matthias Hein
Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs. CoRR (2019)
Francesco Croce, Jonas Rauber, Matthias Hein
Scaling up the randomized gradient-free adversarial attack reveals overestimation of robustness using established attacks. CoRR (2019)

Matthias Hein.css-eqlu8i{font-size:small;padding-left:4px;}

Publication Activity (10 Years)

Top Topics

Top Venues

Matthias Hein