Sharpness-aware minimization (SAM) has well documented merits in enhancing
generalization of deep neural networks, even without sizable data augmentation. Embracing …

We propose an adaptive variance-reduction method, called AdaSpider, for minimization of $
L $-smooth, non-convex functions with a finite-sum structure. In essence, AdaSpider …

The present contribution deals with decentralized policy evaluation in multi-agent Markov
decision processes using temporal-difference (TD) methods with linear function …

With the well-documented popularity of Frank Wolfe (FW) algorithms in machine learning
tasks, the present paper establishes links between FW subproblems and the notion of …

Variance reduction (VR) methods for finite-sum minimization typically require the knowledge
of problem-dependent constants that are often unknown and difficult to estimate. To address …

In this paper, we study the finite-sum convex optimization problem focusing on the general
convex case. Recently, the study of variance reduced (VR) methods and their accelerated …

The main theme of this work is a unifying algorithm,\textbf {L} oop\textbf {L} ess\textbf {S}
ARAH (L2S) for problems formulated as summation of $ n $ individual loss functions. L2S …

For finite-sum optimization, variance-reduced gradient methods (VR) compute at each
iteration the gradient of a single function (or of a mini-batch), and yet achieve faster …

This work investigates fault-resilient federated learning when the data samples are non-
uniformly distributed across workers, and the number of faulty workers is unknown to the …

Aiming at convex optimization under structural constraints, this work introduces and
analyzes a variant of the Frank Wolfe (FW) algorithm termed ExtraFW. The distinct feature of …