2 papers
cs.LG2026
Robustness Cannot be Reduced to Regularization: Studying Adversarial Training Beyond the Linear Case
David A. R. Robin, Rafael Pinot, Yann Chevaleyre
The vulnerability of ML models to adversarial examples has recently emerged as a major concern. While adversarial training is one of the most effective countermeasures to this issu…
cs.LG2026
Equalized Generative Treatment: Matching f-divergences for Fairness in Generative Models
Alexandre Verine, Rafael Pinot, Florian Le Bronnec
Fairness is a crucial concern for generative models, which not only reflect but can also amplify societal and cultural biases. Existing fairness notions for generative models are l…