Matthias Hein
80 papers · 2006–2025 · 11 venues · across top CS/AI venues
This profile combines several different researchers. Authors are keyed by name, and this name's papers fall into 11 separate collaboration groups - the largest holds only 41% of them. Pick the group below that matches the researcher you mean. How a shared name is split.
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Which Matthias Hein?
8 groups of papers that share co-authors internally but none with each other - each most likely a different researcher. Pick one to see its papers.
with Francesco Croce, Maksym Andriushchenko, Maximilian Augustin
33 papers
· 2017–2025
· ICML, NeurIPS, ICCV
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An Interpretable N-gram Perplexity Threat Model for Large Language Model Jailbreaks
ICML 2025
DASH: Detection and Assessment of Systematic Hallucinations of VLMs
ICCV 2025
Mahalanobis++: Improving OOD Detection via Feature Normalization
ICML 2025
Mind the Detail: Uncovering Clinically Relevant Image Details in Accelerated MRI with Semantically Diverse Reconstructions
MICCAI 2025
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
Towards Reliable Evaluation and Fast Training of Robust Semantic Segmentation Models
ECCV 2024
A Modern Look at the Relationship between Sharpness and Generalization
ICML 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
Revisiting Adversarial Training for ImageNet: Architectures, Training and Generalization across Threat Models
NeurIPS 2023
Sound Randomized Smoothing in Floating-Point Arithmetic
ICLR 2023
Spurious Features Everywhere - Large-Scale Detection of Harmful Spurious Features in ImageNet
ICCV 2023
Adversarial Robustness against Multiple and Single l_p-Threat Models via Quick Fine-Tuning of Robust Classifiers
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
Diffusion Visual Counterfactual Explanations
NeurIPS 2022
Evaluating the Adversarial Robustness of Adaptive Test-time Defenses
ICML 2022
Provably Adversarially Robust Detection of Out-of-Distribution Data (Almost) for Free
NeurIPS 2022
Provably Adversarially Robust Nearest Prototype Classifiers
ICML 2022
Sparse-RS: A Versatile Framework for Query-Efficient Sparse Black-Box Adversarial Attacks
AAAI 2022
Mind the Box: l_1-APGD for Sparse Adversarial Attacks on Image Classifiers
ICML 2021
Adversarial Robustness on In- and Out-Distribution Improves Explainability
ECCV 2020
Certifiably Adversarially Robust Detection of Out-of-Distribution Data
NeurIPS 2020
Minimally distorted Adversarial Examples with a Fast Adaptive Boundary Attack
ICML 2020
Provable robustness against all adversarial l_p-perturbations for p≥ 1
ICLR 2020
Reliable evaluation of adversarial robustness with an ensemble of diverse parameter-free attacks
ICML 2020
Square Attack: a query-efficient black-box adversarial attack via random search
ECCV 2020
Towards neural networks that provably know when they don't know
ICLR 2020
Provable Robustness of ReLU networks via Maximization of Linear Regions
AISTATS 2019
Provably robust boosted decision stumps and trees against adversarial attacks
NeurIPS 2019
Sparse and Imperceivable Adversarial Attacks
ICCV 2019
Why ReLU Networks Yield High-Confidence Predictions Far Away From the Training Data and How to Mitigate the Problem
CVPR 2019
Formal Guarantees on the Robustness of a Classifier against Adversarial Manipulation
NeurIPS 2017