2 citations · 2 across the 13 of their papers we have counts for
5 papers · 1 filter
How Worst-Case Are Adversarial Attacks? Linking Adversarial and Perturbation Robustness
Giulio Rossolini
Adversarial attacks are widely used to identify model vulnerabilities; however, their validity as proxies for robustness to random perturbations remains debated. We ask whether an…
On the Hidden Objective Biases of Group-based Reinforcement Learning
Aleksandar Fontana, Marco Simoni, Giulio Rossolini +2
Group-based reinforcement learning methods, like Group Relative Policy Optimization (GRPO), are widely used nowadays to post-train large language models. Despite their empirical su…
GTPO: Stabilizing Group Relative Policy Optimization via Gradient and Entropy Control
Marco Simoni, Aleksandar Fontana, Giulio Rossolini +2
Group Relative Policy Optimization (GRPO) is a promising policy-based approach for Large Language Model alignment, yet its performance is often limited by training instability and…
Exploiting Edge Features for Transferable Adversarial Attacks in Distributed Machine Learning
Giulio Rossolini, Fabio Brau, Alessandro Biondi +2
As machine learning models become increasingly deployed across the edge of internet of things environments, a partitioned deep learning paradigm in which models are split across mu…
Robust-by-Design Classification via Unitary-Gradient Neural Networks
Fabio Brau, Giulio Rossolini, Alessandro Biondi +1
The use of neural networks in safety-critical systems requires safe and robust models, due to the existence of adversarial attacks. Knowing the minimal adversarial perturbation of…