16 citations · 58 across the 25 of their papers we have counts for
14 papers · 1 filter
Certified Robustness from Approximate Gaussian Mixture Structures in Pretrained Latent Spaces
Konstantinos Emmanouilidis, Tianjiao Ding, Nghia Nguyen +2
Deep learning models are vulnerable to adversarial perturbations, raising important concerns for safety-critical deployment. Empirical defenses can achieve strong robustness in pra…
Taking the Road Less Scheduled with Adaptive Polyak Steps
Dimitris Oikonomou, Matthew Buchholz, Yuen-Man Pun +2
Schedule-Free SGD, proposed in [Defazio et al., 2024], achieves optimal convergence rates without requiring the training horizon in advance, by replacing learning rate schedules wi…
Analysis of an Idealized Stochastic Polyak Method and its Application to Black-Box Model Distillation
Robert M. Gower, Guillaume Garrigos, Nicolas Loizou +3
We provide a general convergence theorem of an idealized stochastic Polyak step size called SPS. Besides convexity, we only assume a local expected gradient bound, that include…
Multiplayer Federated Learning: Reaching Equilibrium with Less Communication
TaeHo Yoon, Sayantan Choudhury, Nicolas Loizou
Traditional Federated Learning (FL) approaches assume collaborative clients with aligned objectives working towards a shared global model. However, in many real-world scenarios, cl…
Remove that Square Root: A New Efficient Scale-Invariant Version of AdaGrad
Sayantan Choudhury, Nazarii Tupitsa, Nicolas Loizou +3
Adaptive methods are extremely popular in machine learning as they make learning rate tuning less expensive. This paper introduces a novel optimization algorithm named KATE, which…
Locally Adaptive Federated Learning
Sohom Mukherjee, Nicolas Loizou, Sebastian U. Stich
Federated learning is a paradigm of distributed machine learning in which multiple clients coordinate with a central server to learn a model, without sharing their own training dat…