11 citations · 22 across the 5 of their papers we have counts for
5 papers
RESIST: Resilient Decentralized Learning Using Consensus Gradient Descent
Cheng Fang, Rishabh Dixit, Waheed U. Bajwa +1
Empirical risk minimization (ERM) is a cornerstone of modern machine learning (ML), supported by advances in optimization theory that ensure efficient solutions with provable algor…
Algorithmic Stability of Heavy-Tailed SGD with General Loss Functions
Anant Raj, Lingjiong Zhu, Mert Gürbüzbalaban +1
Heavy-tail phenomena in stochastic gradient descent (SGD) have been reported in several empirical studies. Experimental evidence in previous works suggests a strong interplay betwe…
First Exit Time Analysis of Stochastic Gradient Descent Under Heavy-Tailed Gradient Noise
Thanh Huy Nguyen, Umut Şimşekli, Mert Gürbüzbalaban +1
Stochastic gradient descent (SGD) has been widely used in machine learning due to its computational efficiency and favorable generalization properties. Recently, it has been empiri…
A Stronger Convergence Result on the Proximal Incremental Aggregated Gradient Method
Nuri Denizcan Vanli, Mert Gurbuzbalaban, Asu Ozdaglar
We study the convergence rate of the proximal incremental aggregated gradient (PIAG) method for minimizing the sum of a large number of smooth component functions (where the sum is…
Global Convergence Rate of Proximal Incremental Aggregated Gradient Methods
Nuri Denizcan Vanli, Mert Gurbuzbalaban, Asu Ozdaglar
We focus on the problem of minimizing the sum of smooth component functions (where the sum is strongly convex) and a non-smooth convex function, which arises in regularized empiric…