Maximal Margin Classification for Metric Spaces. - Matthias Hein 001; Olivier Bousquet (COLT, 2003)

Measure Based Regularization. - Olivier Bousquet; Olivier Chapelle; Matthias Hein 001 (NIPS, 2003)

Hilbertian Metrics on Probability Measures and Their Application in SVM?s. - Matthias Hein 001; Thomas Navin Lal; Olivier Bousquet (DAGM-Symposium, 2004)

Hilbertian Metrics and Positive Definite Kernels on Probability Measures. - Matthias Hein 001; Olivier Bousquet (AISTATS, 2005)

From Graphs to Manifolds - Weak and Strong Pointwise Consistency of Graph Laplacians. - Matthias Hein 001; Jean-Yves Audibert; Ulrike von Luxburg (COLT, 2005)

Intrinsic dimensionality estimation of submanifolds in Rd. - Matthias Hein 001; Jean-Yves Audibert (ICML, 2005)

Maximal margin classification for metric spaces. - Matthias Hein 001; Olivier Bousquet; Bernhard Schölkopf (J. Comput. Syst. Sci., 2005)

Geometrical aspects of statistical learning theory. - Matthias Hein 001 (2005)

Uniform Convergence of Adaptive Graph-Based Regularization. - Matthias Hein 001 (COLT, 2006)

Manifold Denoising. - Matthias Hein 001; Markus Maier 001 (NIPS, 2006)

Manifold Denoising as Preprocessing for Finding Natural Representations of Data. - Matthias Hein 001; Markus Maier 001 (AAAI, 2007)

Cluster Identification in Nearest-Neighbor Graphs. - Markus Maier 001; Matthias Hein 001; Ulrike von Luxburg (ALT, 2007)

Graph Laplacians and their Convergence on Random Neighborhood Graphs. - Matthias Hein 001; Jean-Yves Audibert; Ulrike von Luxburg (J. Mach. Learn. Res., 2007)

Influence of graph construction on graph-based clustering measures. - Markus Maier 001; Ulrike von Luxburg; Matthias Hein 001 (NIPS, 2008)

Non-parametric Regression Between Manifolds. - Florian Steinke; Matthias Hein 001 (NIPS, 2008)

Enhancement of Bright Video Features for HDR Displays. - Piotr Didyk; Rafal Mantiuk; Matthias Hein 001; Hans-Peter Seidel (Comput. Graph. Forum, 2008)

Manifold-valued Thin-Plate Splines with Applications in Computer Graphics. - Florian Steinke; Matthias Hein 001; Jan Peters 001; Bernhard Schölkopf (Comput. Graph. Forum, 2008)

Spectral clustering based on the graph p-Laplacian. - Thomas Bühler; Matthias Hein 001 (ICML, 2009)

Robust Nonparametric Regression with Metric-Space Valued Output. - Matthias Hein 001 (NIPS, 2009)

Semi-supervised Regression using Hessian energy with an application to semi-supervised dimensionality reduction. - Kwang In Kim; Florian Steinke; Matthias Hein 001 (NIPS, 2009)

Large-scale antibody profiling of human blood sera: The future of molecular diagnosis. - Andreas Keller; Nicole Ludwig 001; Sabrina Heisel; Petra Leidinger; Claudia Andres; Wolf-Ingo Steudel; Hanno Huwer; Bernhard Burgeth; Matthias Hein 001; Joachim Weickert; Eckart Meese; Hans-Peter Lenhof (Inform. Spektrum, 2009)

Optimal construction of k-nearest-neighbor graphs for identifying noisy clusters. - Markus Maier 001; Matthias Hein 001; Ulrike von Luxburg (Theor. Comput. Sci., 2009)

An Inverse Power Method for Nonlinear Eigenproblems with Applications in 1-Spectral Clustering and Sparse PCA. - Matthias Hein 001; Thomas Bühler (NIPS, 2010)

Getting lost in space: Large sample analysis of the resistance distance. - Ulrike von Luxburg; Agnes Radl; Matthias Hein 001 (NIPS, 2010)

Hitting times, commute distances and the spectral gap for large random geometric graphs - Ulrike von Luxburg; Agnes Radl; Matthias Hein 001 (CoRR, 2010)

Nonparametric Regression between General Riemannian Manifolds. - Florian Steinke; Matthias Hein 001; Bernhard Schölkopf (SIAM J. Imaging Sci., 2010)

Beyond Spectral Clustering - Tight Relaxations of Balanced Graph Cuts. - Matthias Hein 001; Simon Setzer (NIPS, 2011)

Sparse recovery by thresholded non-negative least squares. - Martin Slawski; Matthias Hein 001 (NIPS, 2011)

How the result of graph clustering methods depends on the construction of the graph - Markus Maier 001; Ulrike von Luxburg; Matthias Hein 001 (CoRR, 2011)

Mathematical and Computational Foundations of Learning Theory (Dagstuhl Seminar 11291). - Matthias Hein 001; Gábor Lugosi; Lorenzo Rosasco; Steve Smale (Dagstuhl Reports, 2011)

Isotope pattern deconvolution for peptide mass spectrometry by non-negative least squares/least absolute deviation template matching. - Martin Slawski; Rene Hussong; Andreas Tholey; Thomas Jakoby; Barbara Gregorius; Andreas Hildebrandt 001; Matthias Hein 001 (BMC Bioinform., 2012)

Constrained 1-Spectral Clustering. - Syama Sundar Rangapuram; Matthias Hein 001 (AISTATS, 2012)

Constrained fractional set programs and their application in local clustering and community detection. - Thomas Bühler; Syama Sundar Rangapuram; Simon Setzer; Matthias Hein 001 (ICML (1), 2013)

The Total Variation on Hypergraphs - Learning on Hypergraphs Revisited. - Matthias Hein 001; Simon Setzer; Leonardo Jost; Syama Sundar Rangapuram (NIPS, 2013)

Matrix factorization with binary components. - Martin Slawski; Matthias Hein 001; Pavlo Lutsik (NIPS, 2013)

Towards realistic team formation in social networks based on densest subgraphs. - Syama Sundar Rangapuram; Thomas Bühler; Matthias Hein 001 (WWW, 2013)

Nonlinear Eigenproblems in Data Analysis - Balanced Graph Cuts and the RatioDCA-Prox. - Leonardo Jost; Simon Setzer; Matthias Hein 001 (CoRR, 2013)

Learning Using Privileged Information: SVM+ and Weighted SVM. - Maksim Lapin; Matthias Hein 001; Bernt Schiele (CoRR, 2013)

Scalable Multitask Representation Learning for Scene Classification. - Maksim Lapin; Bernt Schiele; Matthias Hein 001 (CVPR, 2014)

Learning Must-Link Constraints for Video Segmentation Based on Spectral Clustering. - Anna Khoreva; Fabio Galasso; Matthias Hein 001; Bernt Schiele (GCPR, 2014)

Robust PCA: Optimization of the Robust Reconstruction Error Over the Stiefel Manifold. - Anastasia Podosinnikova; Simon Setzer; Matthias Hein 001 (GCPR, 2014)

Tight Continuous Relaxation of the Balanced k-Cut Problem. - Syama Sundar Rangapuram; Pramod Kaushik Mudrakarta; Matthias Hein 001 (NIPS, 2014)

Hitting and commute times in large random neighborhood graphs. - Ulrike von Luxburg; Agnes Radl; Matthias Hein 001 (J. Mach. Learn. Res., 2014)

Learning using privileged information: SV M+ and weighted SVM. - Maksim Lapin; Matthias Hein 001; Bernt Schiele (Neural Networks, 2014)

Classifier based graph construction for video segmentation. - Anna Khoreva; Fabio Galasso; Matthias Hein 001; Bernt Schiele (CVPR, 2015)

A flexible tensor block coordinate ascent scheme for hypergraph matching. - Quynh Nguyen Ngoc; Antoine Gautier 001; Matthias Hein 001 (CVPR, 2015)

Efficient Output Kernel Learning for Multiple Tasks. - Pratik Jawanpuria; Maksim Lapin; Matthias Hein 001; Bernt Schiele (NIPS, 2015)

Top-k Multiclass SVM. - Maksim Lapin; Matthias Hein 001; Bernt Schiele (NIPS, 2015)

Regularization-Free Estimation in Trace Regression with Symmetric Positive Semidefinite Matrices. - Martin Slawski; Ping Li 001; Matthias Hein 001 (NIPS, 2015)

Mathematical and Computational Foundations of Learning Theory (Dagstuhl Seminar 15361). - Matthias Hein 001; Gábor Lugosi; Lorenzo Rosasco (Dagstuhl Reports, 2015)

Correction of noisy labels via mutual consistency check. - Sahely Bhadra; Matthias Hein 001 (Neurocomputing, 2015)

Weakly Supervised Object Boundaries. - Anna Khoreva; Rodrigo Benenson; Mohamed Omran; Matthias Hein 001; Bernt Schiele (CVPR, 2016)

Loss Functions for Top-k Error: Analysis and Insights. - Maksim Lapin; Matthias Hein 001; Bernt Schiele (CVPR, 2016)

Latent Embeddings for Zero-Shot Classification. - Yongqin Xian; Zeynep Akata; Gaurav Sharma 004; Quynh Nguyen 001; Matthias Hein 001; Bernt Schiele (CVPR, 2016)

Improved Image Boundaries for Better Video Segmentation. - Anna Khoreva; Rodrigo Benenson; Fabio Galasso; Matthias Hein 001; Bernt Schiele (ECCV Workshops (3), 2016)

Globally Optimal Training of Generalized Polynomial Neural Networks with Nonlinear Spectral Methods. - Antoine Gautier 001; Quynh Nguyen 001; Matthias Hein 001 (NIPS, 2016)

Clustering Signed Networks with the Geometric Mean of Laplacians. - Pedro Mercado 001; Francesco Tudisco; Matthias Hein 001 (NIPS, 2016)

Weakly Supervised Semantic Labelling and Instance Segmentation. - Anna Khoreva; Rodrigo Benenson; Jan Hendrik Hosang; Matthias Hein 001; Bernt Schiele (CoRR, 2016)

Nodal domain theorem for the graph p-Laplacian. - Francesco Tudisco; Matthias Hein 001 (CoRR, 2016)

Simple Does It: Weakly Supervised Instance and Semantic Segmentation. - Anna Khoreva; Rodrigo Benenson; Jan Hendrik Hosang; Matthias Hein 001; Bernt Schiele (CVPR, 2017)

Variants of RMSProp and Adagrad with Logarithmic Regret Bounds. - Mahesh Chandra Mukkamala; Matthias Hein 001 (ICML, 2017)

The Loss Surface of Deep and Wide Neural Networks. - Quynh Nguyen 001; Matthias Hein 001 (ICML, 2017)

Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation. - Matthias Hein 001; Maksym Andriushchenko (NIPS, 2017)

An Efficient Multilinear Optimization Framework for Hypergraph Matching. - Quynh Nguyen 001; Francesco Tudisco; Antoine Gautier 001; Matthias Hein 001 (IEEE Trans. Pattern Anal. Mach. Intell., 2017)

The Power Mean Laplacian for Multilayer Graph Clustering. - Pedro Mercado 001; Antoine Gautier 001; Francesco Tudisco; Matthias Hein 001 (AISTATS, 2018)

A Randomized Gradient-Free Attack on ReLU Networks. - Francesco Croce; Matthias Hein 001 (GCPR, 2018)

The loss surface and expressivity of deep convolutional neural networks. - Quynh Nguyen 001; Matthias Hein 001 (ICLR (Workshop), 2018)

Optimization Landscape and Expressivity of Deep CNNs. - Quynh Nguyen 001; Matthias Hein 001 (ICML, 2018)

Neural Networks Should Be Wide Enough to Learn Disconnected Decision Regions. - Quynh Nguyen 001; Mahesh Chandra Mukkamala; Matthias Hein 001 (ICML, 2018)

A unifying Perron-Frobenius theorem for nonnegative tensors via multi-homogeneous maps. - Antoine Gautier 001; Francesco Tudisco; Matthias Hein 001 (CoRR, 2018)

Logit Pairing Methods Can Fool Gradient-Based Attacks. - Marius Mosbach; Maksym Andriushchenko; Thomas Alexander Trost; Matthias Hein 001; Dietrich Klakow (CoRR, 2018)

Analysis and Optimization of Loss Functions for Multiclass, Top-k, and Multilabel Classification. - Maksim Lapin; Matthias Hein 001; Bernt Schiele (IEEE Trans. Pattern Anal. Mach. Intell., 2018)

Community Detection in Networks via Nonlinear Modularity Eigenvectors. - Francesco Tudisco; Pedro Mercado 001; Matthias Hein 001 (SIAM J. Appl. Math., 2018)

Provable Robustness of ReLU networks via Maximization of Linear Regions. - Francesco Croce; Maksym Andriushchenko; Matthias Hein 001 (AISTATS, 2019)

Why ReLU Networks Yield High-Confidence Predictions Far Away From the Training Data and How to Mitigate the Problem. - Matthias Hein 001; Maksym Andriushchenko; Julian Bitterwolf (CVPR, 2019)

Disentangling Adversarial Robustness and Generalization. - David Stutz; Matthias Hein 001; Bernt Schiele (CVPR, 2019)

Sparse and Imperceivable Adversarial Attacks. - Francesco Croce; Matthias Hein 001 (ICCV, 2019)

On the loss landscape of a class of deep neural networks with no bad local valleys. - Quynh Nguyen 001; Mahesh Chandra Mukkamala; Matthias Hein 001 (ICLR (Poster), 2019)

Spectral Clustering of Signed Graphs via Matrix Power Means. - Pedro Mercado 001; Francesco Tudisco; Matthias Hein 001 (ICML, 2019)

Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs. - Pedro Mercado 001; Francesco Tudisco; Matthias Hein 001 (NeurIPS, 2019)

Provably robust boosted decision stumps and trees against adversarial attacks. - Maksym Andriushchenko; Matthias Hein 001 (NeurIPS, 2019)

Provable robustness against all adversarial lp-perturbations for p≥1. - Francesco Croce; Matthias Hein 001 (CoRR, 2019)

Confidence-Calibrated Adversarial Training: Towards Robust Models Generalizing Beyond the Attack Used During Training. - David Stutz; Matthias Hein 001; Bernt Schiele (CoRR, 2019)

The Perron-Frobenius Theorem for Multihomogeneous Mappings. - Antoine Gautier 001; Francesco Tudisco; Matthias Hein 001 (SIAM J. Matrix Anal. Appl., 2019)

A Unifying Perron-Frobenius Theorem for Nonnegative Tensors via Multihomogeneous Maps. - Antoine Gautier 001; Francesco Tudisco; Matthias Hein 001 (SIAM J. Matrix Anal. Appl., 2019)

Square Attack: A Query-Efficient Black-Box Adversarial Attack via Random Search. - Maksym Andriushchenko; Francesco Croce; Nicolas Flammarion; Matthias Hein 001 (ECCV (23), 2020)

Adversarial Robustness on In- and Out-Distribution Improves Explainability. - Maximilian Augustin; Alexander Meinke; Matthias Hein 001 (ECCV (26), 2020)

Provable robustness against all adversarial $l_p$-perturbations for $p geq 1$. - Francesco Croce; Matthias Hein 001 (ICLR, 2020)

Towards neural networks that provably know when they don't know. - Alexander Meinke; Matthias Hein 001 (ICLR, 2020)

Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack. - Francesco Croce; Matthias Hein 001 (ICML, 2020)

Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks. - Francesco Croce; Matthias Hein 001 (ICML, 2020)

Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks. - Agustinus Kristiadi; Matthias Hein 001; Philipp Hennig (ICML, 2020)

Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks. - David Stutz; Matthias Hein 001; Bernt Schiele (ICML, 2020)

Certifiably Adversarially Robust Detection of Out-of-Distribution Data. - Julian Bitterwolf; Alexander Meinke; Matthias Hein 001 (NeurIPS, 2020)

Computing the norm of nonnegative matrices and the log-Sobolev constant of Markov chains. - Antoine Gautier 001; Matthias Hein 001; Francesco Tudisco (CoRR, 2020)

On Mitigating Random and Adversarial Bit Errors. - David Stutz; Nandhini Chandramoorthy; Matthias Hein 001; Bernt Schiele (CoRR, 2020)

Provable Worst Case Guarantees for the Detection of Out-of-Distribution Data. - Julian Bitterwolf; Alexander Meinke; Matthias Hein 001 (CoRR, 2020)

Fixing Asymptotic Uncertainty of Bayesian Neural Networks with Infinite ReLU Features. - Agustinus Kristiadi; Matthias Hein 001; Philipp Hennig (CoRR, 2020)

RobustBench: a standardized adversarial robustness benchmark. - Francesco Croce; Maksym Andriushchenko; Vikash Sehwag; Nicolas Flammarion; Mung Chiang; Prateek Mittal; Matthias Hein 001 (CoRR, 2020)

Out-distribution aware Self-training in an Open World Setting. - Maximilian Augustin; Matthias Hein 001 (CoRR, 2020)

Error Estimates for Spectral Convergence of the Graph Laplacian on Random Geometric Graphs Toward the Laplace-Beltrami Operator. - Nicolás García Trillos; Moritz Gerlach; Matthias Hein 001; Dejan Slepcev (Found. Comput. Math., 2020)

Scaling up the Randomized Gradient-Free Adversarial Attack Reveals Overestimation of Robustness Using Established Attacks. - Francesco Croce; Jonas Rauber; Matthias Hein 001 (Int. J. Comput. Vis., 2020)

Relating Adversarially Robust Generalization to Flat Minima. - David Stutz; Matthias Hein 001; Bernt Schiele (ICCV, 2021)

Mind the Box: l1-APGD for Sparse Adversarial Attacks on Image Classifiers. - Francesco Croce; Matthias Hein 001 (ICML, 2021)

Bit Error Robustness for Energy-Efficient DNN Accelerators. - David Stutz; Nandhini Chandramoorthy; Matthias Hein 001; Bernt Schiele (MLSys, 2021)

RobustBench: a standardized adversarial robustness benchmark. - Francesco Croce; Maksym Andriushchenko; Vikash Sehwag; Edoardo Debenedetti; Nicolas Flammarion; Mung Chiang; Prateek Mittal; Matthias Hein 001 (NeurIPS Datasets and Benchmarks, 2021)

An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence. - Agustinus Kristiadi; Matthias Hein 001; Philipp Hennig (NeurIPS, 2021)

Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks. - Maksym Yatsura; Jan Hendrik Metzen; Matthias Hein 001 (NeurIPS, 2021)

Learnable uncertainty under Laplace approximations. - Agustinus Kristiadi; Matthias Hein 001; Philipp Hennig (UAI, 2021)

Adversarial robustness against multiple lp-threat models at the price of one and how to quickly fine-tune robust models to another threat model. - Francesco Croce; Matthias Hein 001 (CoRR, 2021)

Provably Robust Detection of Out-of-distribution Data (almost) for free. - Alexander Meinke; Julian Bitterwolf; Matthias Hein 001 (CoRR, 2021)

The Global Convergence of the Nonlinear Power Method for Mixed-Subordinate Matrix Norms. - Antoine Gautier 001; Matthias Hein 001; Francesco Tudisco (J. Sci. Comput., 2021)

Sparse-RS: A Versatile Framework for Query-Efficient Sparse Black-Box Adversarial Attacks. - Francesco Croce; Maksym Andriushchenko; Naman D. Singh; Nicolas Flammarion; Matthias Hein 001 (AAAI, 2022)

Being a Bit Frequentist Improves Bayesian Neural Networks. - Agustinus Kristiadi; Matthias Hein 001; Philipp Hennig (AISTATS, 2022)

Sparse Visual Counterfactual Explanations in Image Space. - Valentyn Boreiko; Maximilian Augustin; Francesco Croce; Philipp Berens; Matthias Hein 001 (GCPR, 2022)

Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities. - Julian Bitterwolf; Alexander Meinke; Maximilian Augustin; Matthias Hein 001 (ICML, 2022)

Adversarial Robustness against Multiple and Single lp-Threat Models via Quick Fine-Tuning of Robust Classifiers. - Francesco Croce; Matthias Hein 001 (ICML, 2022)

Evaluating the Adversarial Robustness of Adaptive Test-time Defenses. - Francesco Croce; Sven Gowal; Thomas Brunner; Evan Shelhamer; Matthias Hein 001; A. Taylan Cemgil (ICML, 2022)

Provably Adversarially Robust Nearest Prototype Classifiers. - Václav Vorácek; Matthias Hein 001 (ICML, 2022)

Visual Explanations for the Detection of Diabetic Retinopathy from Retinal Fundus Images. - Valentyn Boreiko; Indu Ilanchezian; Murat Seçkin Ayhan; Sarah Müller; Lisa M. Koch; Hanna Faber; Philipp Berens; Matthias Hein 001 (MICCAI (2), 2022)

Adversarial Robustness of MR Image Reconstruction Under Realistic Perturbations. - Jan Nikolas Morshuis; Sergios Gatidis; Matthias Hein 001; Christian F. Baumgartner (MLMIR@MICCAI, 2022)

Diffusion Visual Counterfactual Explanations. - Maximilian Augustin; Valentyn Boreiko; Francesco Croce; Matthias Hein 001 (NeurIPS, 2022)

Provably Adversarially Robust Detection of Out-of-Distribution Data (Almost) for Free. - Alexander Meinke; Julian Bitterwolf; Matthias Hein 001 (NeurIPS, 2022)

Neural Network Heuristic Functions: Taking Confidence into Account. - Daniel Heller; Patrick Ferber; Julian Bitterwolf; Matthias Hein 001; Jörg Hoffmann 001 (SOCS, 2022)

Sound Randomized Smoothing in Floating-Point Arithmetics. - Václav Vorácek; Matthias Hein 001 (CoRR, 2022)

On the interplay of adversarial robustness and architecture components: patches, convolution and attention. - Francesco Croce; Matthias Hein 001 (CoRR, 2022)

Spurious Features Everywhere - Large-Scale Detection of Harmful Spurious Features in ImageNet. - Yannic Neuhaus; Maximilian Augustin; Valentyn Boreiko; Matthias Hein 001 (ICCV, 2023)

Identifying Systematic Errors in Object Detectors with the SCROD Pipeline. - Valentyn Boreiko; Matthias Hein 001; Jan Hendrik Metzen (ICCV (Workshops), 2023)

On the Adversarial Robustness of Multi-Modal Foundation Models. - Christian Schlarmann; Matthias Hein 001 (ICCV (Workshops), 2023)

Sound Randomized Smoothing in Floating-Point Arithmetic. - Václav Vorácek; Matthias Hein 001 (ICLR, 2023)

Certified Defences Against Adversarial Patch Attacks on Semantic Segmentation. - Maksym Yatsura; Kaspar Sakmann; N. Grace Hua; Matthias Hein 001; Jan Hendrik Metzen (ICLR, 2023)

A Modern Look at the Relationship between Sharpness and Generalization. - Maksym Andriushchenko; Francesco Croce; Maximilian Müller; Matthias Hein 001; Nicolas Flammarion (ICML, 2023)

In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation. - Julian Bitterwolf; Maximilian Müller; Matthias Hein 001 (ICML, 2023)

Improving l1-Certified Robustness via Randomized Smoothing by Leveraging Box Constraints. - Václav Vorácek; Matthias Hein 001 (ICML, 2023)

Normalization Layers Are All That Sharpness-Aware Minimization Needs. - Maximilian Müller; Tiffany Vlaar; David Rolnick; Matthias Hein 001 (NeurIPS, 2023)

Revisiting Adversarial Training for ImageNet: Architectures, Training and Generalization across Threat Models. - Naman Deep Singh; Francesco Croce; Matthias Hein 001 (NeurIPS, 2023)

Robust Semantic Segmentation: Strong Adversarial Attacks and Fast Training of Robust Models. - Francesco Croce; Naman D. Singh; Matthias Hein 001 (CoRR, 2023)

Generating Realistic Counterfactuals for Retinal Fundus and OCT Images using Diffusion Models. - Indu Ilanchezian; Valentyn Boreiko; Laura Kühlewein; Ziwei Huang; Murat Seçkin Ayhan; Matthias Hein 001; Lisa M. Koch; Philipp Berens (CoRR, 2023)

Analyzing and Explaining Image Classifiers via Diffusion Guidance. - Maximilian Augustin; Yannic Neuhaus; Matthias Hein 001 (CoRR, 2023)

Random and Adversarial Bit Error Robustness: Energy-Efficient and Secure DNN Accelerators. - David Stutz; Nandhini Chandramoorthy; Matthias Hein 001; Bernt Schiele (IEEE Trans. Pattern Anal. Mach. Intell., 2023)

Nonlinear Perron-Frobenius Theorems for Nonnegative Tensors. - Antoine Gautier 001; Francesco Tudisco; Matthias Hein 001 (SIAM Rev., 2023)

DiG-IN: Diffusion Guidance for Investigating Networks - Uncovering Classifier Differences, Neuron Visualisations, and Visual Counterfactual Explanations. - Maximilian Augustin; Yannic Neuhaus; Matthias Hein 001 (CVPR, 2024)

Towards Reliable Evaluation and Fast Training of Robust Semantic Segmentation Models. - Francesco Croce; Naman D. Singh; Matthias Hein 001 (ECCV (79), 2024)

Bias of Stochastic Gradient Descent or the Architecture: Disentangling the Effects of Overparameterization of Neural Networks. - Amit Peleg; Matthias Hein 001 (ICML, 2024)

Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models. - Christian Schlarmann; Naman Deep Singh; Francesco Croce; Matthias Hein 001 (ICML, 2024)

Segmentation-Guided MRI Reconstruction for Meaningfully Diverse Reconstructions. - Jan Nikolas Morshuis; Matthias Hein 001; Christian F. Baumgartner (DGM4MICCAI@MICCAI, 2024)

Segment (Almost) Nothing: Prompt-Agnostic Adversarial Attacks on Segmentation Models. - Francesco Croce; Matthias Hein 001 (SaTML, 2024)

Identification of Fine-grained Systematic Errors via Controlled Scene Generation. - Valentyn Boreiko; Matthias Hein 001; Jan Hendrik Metzen (CoRR, 2024)

Zero-Shot Distillation for Image Encoders: How to Make Effective Use of Synthetic Data. - Niclas Popp; Jan Hendrik Metzen; Matthias Hein 001 (CoRR, 2024)

How to train your ViT for OOD Detection. - Maximilian Müller; Matthias Hein 001 (CoRR, 2024)

LoGex: Improved tail detection of extremely rare histopathology classes via guided diffusion. - Maximilian Müller; Matthias Hein 001 (CoRR, 2024)

A Realistic Threat Model for Large Language Model Jailbreaks. - Valentyn Boreiko; Alexander Panfilov; Václav Vorácek; Matthias Hein 001; Jonas Geiping (CoRR, 2024)

Perturb and Recover: Fine-tuning for Effective Backdoor Removal from CLIP. - Naman Deep Singh; Francesco Croce; Matthias Hein 001 (CoRR, 2024)

Object-Focused Data Selection for Dense Prediction Tasks. - Niclas Popp; Dan Zhang; Jan Hendrik Metzen; Matthias Hein 001; Lukas Schott (CoRR, 2024)

Feature Distillation Improves Zero-Shot Transfer from Synthetic Images. - Niclas Popp; Jan Hendrik Metzen; Matthias Hein 001 (Trans. Mach. Learn. Res., 2024)

Adversarially Robust CLIP Models Can Induce Better (Robust) Perceptual Metrics. - Francesco Croce; Christian Schlarmann; Naman Deep Singh; Matthias Hein 001 (SaTML, 2025)

DASH: Detection and Assessment of Systematic Hallucinations of VLMs. - Maximilian Augustin; Yannic Neuhaus; Matthias Hein 001 (CoRR, 2025)

RePOPE: Impact of Annotation Errors on the POPE Benchmark. - Yannic Neuhaus; Matthias Hein 001 (CoRR, 2025)

Mahalanobis++: Improving OOD Detection via Feature Normalization. - Maximilian Müller; Matthias Hein 001 (CoRR, 2025)

Advancing Compositional Awareness in CLIP with Efficient Fine-Tuning. - Amit Peleg; Naman Deep Singh; Matthias Hein 001 (CoRR, 2025)

Improving Knowledge Distillation Under Unknown Covariate Shift Through Confidence-Guided Data Augmentation. - Niclas Popp; Kevin Alexander Laube; Matthias Hein 001; Lukas Schott (CoRR, 2025)

FuseLIP: Multimodal Embeddings via Early Fusion of Discrete Tokens. - Christian Schlarmann; Francesco Croce; Nicolas Flammarion; Matthias Hein 001 (CoRR, 2025)

Robustness in Both Domains: CLIP Needs a Robust Text Encoder. - Elías Abad-Rocamora; Christian Schlarmann; Naman Deep Singh; Yongtao Wu; Matthias Hein 001; Volkan Cevher (CoRR, 2025)

Mind the Detail: Uncovering Clinically Relevant Image Details in Accelerated MRI with Semantically Diverse Reconstructions. - Jan Nikolas Morshuis; Christian Schlarmann; Thomas Küstner; Christian F. Baumgartner; Matthias Hein 001 (CoRR, 2025)