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…
math.OC2025
Random Sparse Lifts: Construction, Analysis and Convergence of finite sparse networks
David A. R. Robin, Kevin Scaman, Marc Lelarge
We present a framework to define a large class of neural networks for which, by construction, training by gradient flow provably reaches arbitrarily low loss when the number of par…