Robin Staab

PhD student

robin.staab@inf.ethz.ch
CAB H 85.2

About me

I am Robin Staab, a PhD student at the Secure, Reliable, and Intelligent Systems Lab of ETH Zürich, advised by Prof. Martin Vechev, since July 2023.

Education

  • ETH Zurich, 2020 - 2023

    • M. Sc. Computer Science

  • ETH Zurich, 2016 - 2020

    • B. Sc. Computer Science

Work Experience

  • Snyk , Aug-Dec 2021

    • Machine Learning Engineering Intern, Zurich

Awards

2023 Willi Studer Prize for Best Master's Degree in Computer Science Awarded

Publications

2024

A Synthetic Dataset for Personal Attribute Inference
Hanna Yukhymenko, Robin Staab, Mark Vero, Martin Vechev
NeurIPS Datasets and Benchmarks 2024

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Code
Private Attribute Inference from Images with Vision-Language Models
Batuhan Tömekçe, Mark Vero, Robin Staab, Martin Vechev
NeurIPS 2024

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Code
Exploiting LLM Quantization
Kazuki Egashira, Mark Vero, Robin Staab, Jingxuan He, Martin Vechev
NeurIPS 2024 NextGenAISafety@ICML24 Oral

Website
Paper
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Ward: Provable RAG Dataset Inference via LLM Watermarks
Nikola Jovanović, Robin Staab, Maximilian Baader, Martin Vechev
arXiv 2024

Paper
COMPL-AI Framework: A Technical Interpretation and LLM Benchmarking Suite for the EU Artificial Intelligence Act
Philipp Guldimann, Alexander Spiridonov, Robin Staab, Nikola Jovanović, Mark Vero, Velko Vechev, Anna Gueorguieva, Mislav Balunović, Nikola Konstantinov, Pavol Bielik, Petar Tsankov, Martin Vechev
arXiv 2024

Website
Paper
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Discovering Clues of Spoofed LM Watermarks
Thibaud Gloaguen, Nikola Jovanović, Robin Staab, Martin Vechev
arXiv 2024

Paper
Watermark Stealing in Large Language Models
Nikola Jovanović, Robin Staab, Martin Vechev
ICML 2024 R2-FM@ICLR24 Oral

Website
Paper
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From Principle to Practice: Vertical Data Minimization for Machine Learning
Robin Staab, Nikola Jovanović, Mislav Balunović, Martin Vechev
IEEE S&P 2024

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Beyond Memorization: Violating Privacy Via Inference with Large Language Models
Robin Staab, Mark Vero, Mislav Balunović, Martin Vechev
ICLR 2024 Spotlight, 2024 PPPM-Award

Website
Paper
Code
Back to the Drawing Board for Fair Representation Learning
Angéline Pouget, Nikola Jovanović, Mark Vero, Robin Staab, Martin Vechev
arXiv 2024

Paper
Black-Box Detection of Language Model Watermarks
Thibaud Gloaguen, Nikola Jovanović, Robin Staab, Martin Vechev
arXiv 2024

Paper
Large Language Models are Advanced Anonymizers
Robin Staab, Mark Vero, Mislav Balunović, Martin Vechev
arXiv 2024

Paper

2023

Abstract Interpretation of Fixpoint Iterators with Applications to Neural Networks
Mark Niklas Müller, Marc Fischer, Robin Staab, Martin Vechev
PLDI 2023

Paper

2022

Bayesian Framework for Gradient Leakage
Mislav Balunović, Dimitar I. Dimitrov, Robin Staab, Martin Vechev
ICLR 2022

Blog
Paper
Code