3 citations · 9 across the 9 of their papers we have counts for
5 papers · 1 filter
Stable Nonconvex-Nonconcave Training via Linear Interpolation
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. We argue that instabilities in the…
Federated Learning under Covariate Shifts with Generalization Guarantees
Ali Ramezani-Kebrya, Fanghui Liu, Thomas Pethick +2
This paper addresses intra-client and inter-client covariate shifts in federated learning (FL) with a focus on the overall generalization performance. To handle covariate shifts, w…
Escaping limit cycles: Global convergence for constrained nonconvex-nonconcave minimax problems
Thomas Pethick, Puya Latafat, Panagiotis Patrinos +2
This paper introduces a new extragradient-type algorithm for a class of nonconvex-nonconcave minimax problems. It is well-known that finding a local solution for general minimax pr…
Solving stochastic weak Minty variational inequalities without increasing batch size
Thomas Pethick, Olivier Fercoq, Puya Latafat +2
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)…
Revisiting adversarial training for the worst-performing class
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. For example, on CIFAR10, the acc…