← Authors

Matthias Hein

80 papers · 2006–2025 · 11 conferences · across top CS/AI conferences

Achievements

🧭 Keyword Pioneer 🌍 Conference Polyglot (11) πŸ—ΊοΈ Taxonomy Completionist (28) 🐣 Hot Topic Early Bird 🐝 Cross-Pollinator (14) πŸƒ Academic Marathon (19) 🌈 Renaissance Researcher (5) πŸŒ‰ Interdisciplinary Bridge 🏠 Conference Loyalist (27) 🌟 Keyword Trendsetter Combo (6) 🀝 Dynamic Duo (15) πŸ‘‘ Triple Crown πŸ† Keyword Champion (6) πŸ† Grand Slam πŸ”¬ Deep Specialist (16) ❓ The Questioner πŸ“ˆ Trend Setter πŸš€ Conference Pioneer πŸ”₯ Unstoppable (20) ⚑ Prolific Year (5) πŸ’Ž Century Club (80) πŸ—ƒοΈ Keyword Collector (101)

Conferences

NIPS (27) ICML (22) CVPR (10) ICLR (5) AISTATS (4) ICCV (4) ECCV (3) JMLR (2) AAAI (1) MICCAI (1) UAI (1)

Top co-authors

Francesco Croce (15) Bernt Schiele (11) Maksym Andriushchenko (7) Quynh Nguyen (6) Maximilian Augustin (6) Julian Bitterwolf (5) Alexander Meinke (5) Philipp Hennig (4) Maksim Lapin (4) Francesco Tudisco (4)

Keywords

adversarial robustness (15) spectral clustering (9) adversarial attack (7) out-of-distribution detection (6) graph laplacian (6) adversarial training (6) image classification (6) uncertainty quantification (5) neural network (5) adversarial example (4) image classifier (3) stochastic block model (3) bayesian neural network (3) semi-supervised learning (3) manifold learning (3) random walk (3) graph clustering (3) constrained optimization (3) combinatorial optimization (3) normalized cut (3)

Papers

Mahalanobis++: Improving OOD Detection via Feature Normalization ICML 2025
DASH: Detection and Assessment of Systematic Hallucinations of VLMs ICCV 2025
An Interpretable N-gram Perplexity Threat Model for Large Language Model Jailbreaks ICML 2025
Mind the Detail: Uncovering Clinically Relevant Image Details in Accelerated MRI with Semantically Diverse Reconstructions MICCAI 2025
Towards Reliable Evaluation and Fast Training of Robust Semantic Segmentation Models ECCV 2024
DiG-IN: Diffusion Guidance for Investigating Networks - Uncovering Classifier Differences Neuron Visualisations and Visual Counterfactual Explanations CVPR 2024
Robust CLIP: Unsupervised Adversarial Fine-Tuning of Vision Embeddings for Robust Large Vision-Language Models ICML 2024
Bias of Stochastic Gradient Descent or the Architecture: Disentangling the Effects of Overparameterization of Neural Networks ICML 2024
Sound Randomized Smoothing in Floating-Point Arithmetic ICLR 2023
Normalization Layers Are All That Sharpness-Aware Minimization Needs NIPS 2023
Revisiting Adversarial Training for ImageNet: Architectures, Training and Generalization across Threat Models NIPS 2023
Improving l1-Certified Robustness via Randomized Smoothing by Leveraging Box Constraints ICML 2023
In or Out? Fixing ImageNet Out-of-Distribution Detection Evaluation ICML 2023
A Modern Look at the Relationship between Sharpness and Generalization ICML 2023
Spurious Features Everywhere - Large-Scale Detection of Harmful Spurious Features in ImageNet ICCV 2023
Certified Defences Against Adversarial Patch Attacks on Semantic Segmentation ICLR 2023
Diffusion Visual Counterfactual Explanations NIPS 2022
Provably Adversarially Robust Nearest Prototype Classifiers ICML 2022
Adversarial Robustness against Multiple and Single $l_p$-Threat Models via Quick Fine-Tuning of Robust Classifiers ICML 2022
Evaluating the Adversarial Robustness of Adaptive Test-time Defenses ICML 2022
Breaking Down Out-of-Distribution Detection: Many Methods Based on OOD Training Data Estimate a Combination of the Same Core Quantities ICML 2022
Provably Adversarially Robust Detection of Out-of-Distribution Data (Almost) for Free NIPS 2022
Sparse-RS: A Versatile Framework for Query-Efficient Sparse Black-Box Adversarial Attacks AAAI 2022
Being a Bit Frequentist Improves Bayesian Neural Networks AISTATS 2022
Learnable uncertainty under Laplace approximations UAI 2021
Relating Adversarially Robust Generalization to Flat Minima ICCV 2021
An Infinite-Feature Extension for Bayesian ReLU Nets That Fixes Their Asymptotic Overconfidence NIPS 2021
Meta-Learning the Search Distribution of Black-Box Random Search Based Adversarial Attacks NIPS 2021
Mind the Box: $l_1$-APGD for Sparse Adversarial Attacks on Image Classifiers ICML 2021
Confidence-Calibrated Adversarial Training: Generalizing to Unseen Attacks ICML 2020
Towards neural networks that provably know when they don't know ICLR 2020
Provable robustness against all adversarial $l_p$-perturbations for $p\geq 1$ ICLR 2020
Adversarial Robustness on In- and Out-Distribution Improves Explainability ECCV 2020
Certifiably Adversarially Robust Detection of Out-of-Distribution Data NIPS 2020
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks ICML 2020
Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack ICML 2020
Being Bayesian, Even Just a Bit, Fixes Overconfidence in ReLU Networks ICML 2020
Square Attack: a query-efficient black-box adversarial attack via random search ECCV 2020
Disentangling Adversarial Robustness and Generalization CVPR 2019
Provably robust boosted decision stumps and trees against adversarial attacks NIPS 2019
Generalized Matrix Means for Semi-Supervised Learning with Multilayer Graphs NIPS 2019
Provable Robustness of ReLU networks via Maximization of Linear Regions AISTATS 2019
Why ReLU Networks Yield High-Confidence Predictions Far Away From the Training Data and How to Mitigate the Problem CVPR 2019
Sparse and Imperceivable Adversarial Attacks ICCV 2019
On the loss landscape of a class of deep neural networks with no bad local valleys ICLR 2019
Spectral Clustering of Signed Graphs via Matrix Power Means ICML 2019
The Power Mean Laplacian for Multilayer Graph Clustering AISTATS 2018
Neural Networks Should Be Wide Enough to Learn Disconnected Decision Regions ICML 2018
Optimization Landscape and Expressivity of Deep CNNs ICML 2018
Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation NIPS 2017
Simple Does It: Weakly Supervised Instance and Semantic Segmentation CVPR 2017
The Loss Surface of Deep and Wide Neural Networks ICML 2017
Variants of RMSProp and Adagrad with Logarithmic Regret Bounds ICML 2017
Loss Functions for Top-k Error: Analysis and Insights CVPR 2016
Weakly Supervised Object Boundaries CVPR 2016
Latent Embeddings for Zero-Shot Classification CVPR 2016
Clustering Signed Networks with the Geometric Mean of Laplacians NIPS 2016
Globally Optimal Training of Generalized Polynomial Neural Networks with Nonlinear Spectral Methods NIPS 2016
Classifier Based Graph Construction for Video Segmentation CVPR 2015
Efficient Output Kernel Learning for Multiple Tasks NIPS 2015
Top-k Multiclass SVM NIPS 2015
A Flexible Tensor Block Coordinate Ascent Scheme for Hypergraph Matching CVPR 2015
Regularization-Free Estimation in Trace Regression with Symmetric Positive Semidefinite Matrices NIPS 2015
Scalable Multitask Representation Learning for Scene Classification CVPR 2014
Hitting and Commute Times in Large Random Neighborhood Graphs JMLR 2014
Tight Continuous Relaxation of the Balanced k-Cut Problem NIPS 2014
Constrained fractional set programs and their application in local clustering and community detection ICML 2013
The Total Variation on Hypergraphs - Learning on Hypergraphs Revisited NIPS 2013
Matrix factorization with binary components NIPS 2013
Constrained 1-Spectral Clustering AISTATS 2012
Sparse recovery by thresholded non-negative least squares NIPS 2011
Beyond Spectral Clustering - Tight Relaxations of Balanced Graph Cuts NIPS 2011
Getting lost in space: Large sample analysis of the resistance distance NIPS 2010
An Inverse Power Method for Nonlinear Eigenproblems with Applications in 1-Spectral Clustering and Sparse PCA NIPS 2010
Semi-supervised Regression using Hessian energy with an application to semi-supervised dimensionality reduction NIPS 2009
Robust Nonparametric Regression with Metric-Space Valued Output NIPS 2009
Influence of graph construction on graph-based clustering measures NIPS 2008
Non-parametric Regression Between Manifolds NIPS 2008
Graph Laplacians and their Convergence on Random Neighborhood Graphs JMLR 2007
Manifold Denoising NIPS 2006