2 citations · 2 across the 3 of their papers we have counts for
4 papers
Precise Convergence Speed of Clipped SGD
David A. R. Robin
We present a tightened convergence analysis of clipped gradient descent on -smooth functions, with quantitative constants. Building on the ideas of Koloskova et al (202…
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…
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…
Convergence beyond the over-parameterized regime using Rayleigh quotients
David A. R. Robin, Kevin Scaman, Marc Lelarge
In this paper, we present a new strategy to prove the convergence of deep learning architectures to a zero training (or even testing) loss by gradient flow. Our analysis is centere…