1 code implementation • NeurIPS 2023 • Thomas Pethick, Wanyun Xie, Volkan Cevher
This paper presents a theoretical analysis of linear interpolation as a principled method for stabilizing (large-scale) neural network training.
no code implementations • 8 Jun 2023 • Ali Ramezani-Kebrya, Fanghui Liu, Thomas Pethick, Grigorios Chrysos, Volkan Cevher
This paper addresses intra-client and inter-client covariate shifts in federated learning (FL) with a focus on the overall generalization performance.
1 code implementation • ICLR 2022 • Thomas Pethick, Puya Latafat, Panagiotis Patrinos, Olivier Fercoq, Volkan Cevher
This paper introduces a new extragradient-type algorithm for a class of nonconvex-nonconcave minimax problems.
1 code implementation • 17 Feb 2023 • Thomas Pethick, Grigorios G. Chrysos, Volkan Cevher
Despite progress in adversarial training (AT), there is a substantial gap between the top-performing and worst-performing classes in many datasets.
1 code implementation • 17 Feb 2023 • Thomas Pethick, Olivier Fercoq, Puya Latafat, Panagiotis Patrinos, Volkan Cevher
This paper introduces a family of stochastic extragradient-type algorithms for a class of nonconvex-nonconcave problems characterized by the weak Minty variational inequality (MVI).
no code implementations • NeurIPS 2021 • Kimon Antonakopoulos, Thomas Pethick, Ali Kavis, Panayotis Mertikopoulos, Volkan Cevher
Our first result is that the algorithm achieves the optimal rates of convergence for cocoercive problems when the profile of the randomness is known to the optimizer: $\mathcal{O}(1/\sqrt{T})$ for absolute noise profiles, and $\mathcal{O}(1/T)$ for relative ones.
no code implementations • NeurIPS 2021 • ChaeHwan Song, Ali Ramezani-Kebrya, Thomas Pethick, Armin Eftekhari, Volkan Cevher
Overparameterization refers to the important phenomenon where the width of a neural network is chosen such that learning algorithms can provably attain zero loss in nonconvex training.
no code implementations • 29 Sep 2021 • Thomas Pethick, Grigorios Chrysos, Volkan Cevher
In this work, we identify that the focus on the average accuracy metric can create vulnerabilities to the "weakest" class.