Adversarial Perturbation Intensity Achieving Chosen Intra-Technique Transferability Level for Logistic Regression. - Martin Gubri (CoRR, 2018)

Search-based adversarial testing and improvement of constrained credit scoring systems. - Salah Ghamizi; Maxime Cordy; Martin Gubri; Mike Papadakis; Andrey Boytsov; Yves Le Traon; Anne Goujon (ESEC/SIGSOFT FSE, 2020)

Efficient and Transferable Adversarial Examples from Bayesian Neural Networks. - Martin Gubri; Maxime Cordy; Mike Papadakis; Yves Le Traon (CoRR, 2020)

Effective and Efficient Data Poisoning in Semi-Supervised Learning. - Adriano Franci; Maxime Cordy; Martin Gubri; Mike Papadakis; Yves Le Traon (CoRR, 2020)

Influence-driven data poisoning in graph-based semi-supervised classifiers. - Adriano Franci; Maxime Cordy; Martin Gubri; Mike Papadakis; Yves Le Traon (CAIN, 2022)

LGV: Boosting Adversarial Example Transferability from Large Geometric Vicinity. - Martin Gubri; Maxime Cordy; Mike Papadakis; Yves Le Traon; Koushik Sen (ECCV (4), 2022)

Efficient and transferable adversarial examples from bayesian neural networks. - Martin Gubri; Maxime Cordy; Mike Papadakis; Yves Le Traon; Koushik Sen (UAI, 2022)

ProPILE: Probing Privacy Leakage in Large Language Models. - Siwon Kim; Sangdoo Yun; Hwaran Lee; Martin Gubri; Sungroh Yoon; Seong Joon Oh (NeurIPS, 2023)

Going Further: Flatness at the Rescue of Early Stopping for Adversarial Example Transferability. - Martin Gubri; Maxime Cordy; Yves Le Traon (CoRR, 2023)

What Matters in Model Training to Transfer Adversarial Examples. - Martin Gubri (2023)

TRAP: Targeted Random Adversarial Prompt Honeypot for Black-Box Identification. - Martin Gubri; Dennis Ulmer; Hwaran Lee; Sangdoo Yun; Seong Joon Oh (ACL (Findings), 2024)

Calibrating Large Language Models Using Their Generations Only. - Dennis Ulmer; Martin Gubri; Hwaran Lee; Sangdoo Yun; Seong Joon Oh (ACL (1), 2024)

Social Science Is Necessary for Operationalizing Socially Responsible Foundation Models. - Adam Davies; Elisa Nguyen; Michael Simeone; Erik Johnston; Martin Gubri (CoRR, 2024)

Scaling Up Membership Inference: When and How Attacks Succeed on Large Language Models. - Haritz Puerto; Martin Gubri; Sangdoo Yun; Seong Joon Oh (NAACL (Findings), 2025)

Testing Uniform Random Samplers: Methods, Datasets and Protocols. - Olivier Zeyen; Maxime Cordy; Martin Gubri; Gilles Perrouin; Mathieu Acher (CoRR, 2025)

C-SEO Bench: Does Conversational SEO Work? - Haritz Puerto; Martin Gubri; Tommaso Green; Seong Joon Oh; Sangdoo Yun (CoRR, 2025)

Leaky Thoughts: Large Reasoning Models Are Not Private Thinkers. - Tommaso Green; Martin Gubri; Haritz Puerto; Sangdoo Yun; Seong Joon Oh (CoRR, 2025)