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Robin Staab
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2020 – today
- 2024
- [c4]Robin Staab, Mark Vero, Mislav Balunovic, Martin T. Vechev:
Beyond Memorization: Violating Privacy via Inference with Large Language Models. ICLR 2024 - [c3]Nikola Jovanovic, Robin Staab, Martin T. Vechev:
Watermark Stealing in Large Language Models. ICML 2024 - [c2]Robin Staab, Nikola Jovanovic, Mislav Balunovic, Martin T. Vechev:
From Principle to Practice: Vertical Data Minimization for Machine Learning. SP 2024: 4733-4752 - [i14]Robin Staab, Mark Vero, Mislav Balunovic, Martin T. Vechev:
Large Language Models are Advanced Anonymizers. CoRR abs/2402.13846 (2024) - [i13]Nikola Jovanovic, Robin Staab, Martin T. Vechev:
Watermark Stealing in Large Language Models. CoRR abs/2402.19361 (2024) - [i12]Batuhan Tömekçe, Mark Vero, Robin Staab, Martin T. Vechev:
Private Attribute Inference from Images with Vision-Language Models. CoRR abs/2404.10618 (2024) - [i11]Kazuki Egashira, Mark Vero, Robin Staab, Jingxuan He, Martin T. Vechev:
Exploiting LLM Quantization. CoRR abs/2405.18137 (2024) - [i10]Angéline Pouget, Nikola Jovanovic, Mark Vero, Robin Staab, Martin T. Vechev:
Back to the Drawing Board for Fair Representation Learning. CoRR abs/2405.18161 (2024) - [i9]Thibaud Gloaguen, Nikola Jovanovic, Robin Staab, Martin T. Vechev:
Black-Box Detection of Language Model Watermarks. CoRR abs/2405.20777 (2024) - [i8]Hanna Yukhymenko, Robin Staab, Mark Vero, Martin T. Vechev:
A Synthetic Dataset for Personal Attribute Inference. CoRR abs/2406.07217 (2024) - [i7]Thibaud Gloaguen, Nikola Jovanovic, Robin Staab, Martin T. Vechev:
Discovering Clues of Spoofed LM Watermarks. CoRR abs/2410.02693 (2024) - [i6]Nikola Jovanovic, Robin Staab, Maximilian Baader, Martin T. Vechev:
Ward: Provable RAG Dataset Inference via LLM Watermarks. CoRR abs/2410.03537 (2024) - [i5]Philipp Guldimann, Alexander Spiridonov, Robin Staab, Nikola Jovanovic, Mark Vero, Velko Vechev, Anna Gueorguieva, Mislav Balunovic, Nikola Konstantinov, Pavol Bielik, Petar Tsankov, Martin T. Vechev:
COMPL-AI Framework: A Technical Interpretation and LLM Benchmarking Suite for the EU Artificial Intelligence Act. CoRR abs/2410.07959 (2024) - 2023
- [j1]Mark Niklas Müller, Marc Fischer, Robin Staab, Martin T. Vechev:
Abstract Interpretation of Fixpoint Iterators with Applications to Neural Networks. Proc. ACM Program. Lang. 7(PLDI): 786-810 (2023) - [d1]Mark Niklas Müller, Marc Fischer, Robin Staab, Martin T. Vechev:
Abstract Interpretation of Fixpoint Iterators with Applications to Neural Networks - Artifact. Zenodo, 2023 - [i4]Robin Staab, Mark Vero, Mislav Balunovic, Martin T. Vechev:
Beyond Memorization: Violating Privacy Via Inference with Large Language Models. CoRR abs/2310.07298 (2023) - [i3]Robin Staab, Nikola Jovanovic, Mislav Balunovic, Martin T. Vechev:
From Principle to Practice: Vertical Data Minimization for Machine Learning. CoRR abs/2311.10500 (2023) - 2022
- [c1]Mislav Balunovic, Dimitar Iliev Dimitrov, Robin Staab, Martin T. Vechev:
Bayesian Framework for Gradient Leakage. ICLR 2022 - 2021
- [i2]Mark Niklas Müller, Robin Staab, Marc Fischer, Martin T. Vechev:
Effective Certification of Monotone Deep Equilibrium Models. CoRR abs/2110.08260 (2021) - [i1]Mislav Balunovic, Dimitar I. Dimitrov, Robin Staab, Martin T. Vechev:
Bayesian Framework for Gradient Leakage. CoRR abs/2111.04706 (2021)
Coauthor Index
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