Clipped Stochastic Methods for Variational Inequalities with Heavy-Tailed Noise. - Eduard Gorbunov; Marina Danilova; David Dobre; Pavel E. Dvurechenskii; Alexander V. Gasnikov; Gauthier Gidel (NeurIPS, 2022)

Dissecting adaptive methods in GANs. - Samy Jelassi; David Dobre; Arthur Mensch; Yuanzhi Li; Gauthier Gidel (CoRR, 2022)

Adversarial Attacks and Defenses in Large Language Models: Old and New Threats. - Leo Schwinn; David Dobre; Stephan Günnemann; Gauthier Gidel (ICBINB, 2023)

Raising the Bar for Certified Adversarial Robustness with Diffusion Models. - Thomas Altstidl; David Dobre; Björn M. Eskofier; Gauthier Gidel; Leo Schwinn (CoRR, 2023)

Sarah Frank-Wolfe: Methods for Constrained Optimization with Best Rates and Practical Features. - Aleksandr Beznosikov; David Dobre; Gauthier Gidel (ICML, 2024)

On the Scalability of Certified Adversarial Robustness with Generated Data. - Thomas Altstidl; David Dobre; Arthur Kosmala; Bjoern M. Eskofier; Gauthier Gidel; Leo Schwinn (NeurIPS, 2024)

Soft Prompt Threats: Attacking Safety Alignment and Unlearning in Open-Source LLMs through the Embedding Space. - Leo Schwinn; David Dobre; Sophie Xhonneux; Gauthier Gidel; Stephan Günnemann (NeurIPS, 2024)

In-Context Learning Can Re-learn Forbidden Tasks. - Sophie Xhonneux; David Dobre; Jian Tang; Gauthier Gidel; Dhanya Sridhar (CoRR, 2024)

Learning Diverse Attacks on Large Language Models for Robust Red-Teaming and Safety Tuning. - Seanie Lee; Minsu Kim 004; Lynn Cherif; David Dobre; Juho Lee 001; Sung Ju Hwang; Kenji Kawaguchi; Gauthier Gidel; Yoshua Bengio; Nikolay Malkin; Moksh Jain (ICLR, 2025)

A generative approach to LLM harmfulness detection with special red flag tokens. - Sophie Xhonneux; David Dobre; Mehrnaz Mohfakhami; Leo Schwinn; Gauthier Gidel (CoRR, 2025)