In this paper, we propose a novel stochastic gradient estimator—ProbAbilistic Gradient
Estimator (PAGE)—for nonconvex optimization. PAGE is easy to implement as it is designed …

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

We study finite-sum distributed optimization problems involving a master node and $ n-1$
local nodes under the popular $\delta $-similarity and $\mu $-strong convexity conditions …

In this paper we study the lower complexity bounds for finite-sum optimization problems,
where the objective is the average of $ n $ individual component functions. We consider a …

In this work, we introduce a novel stochastic proximal alternating linearized minimization
algorithm [J. Bolte, S. Sabach, and M. Teboulle, Math. Program., 146 (2014), pp. 459--494] …

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

Federated learning (FL) is a subfield of machine learning where multiple clients try to
collaboratively learn a model over a network under communication constraints. We consider …

The variance reduction class of algorithms including the representative ones, SVRG and
SARAH, have well documented merits for empirical risk minimization problems. However …

In this paper, for solving a broad class of large-scale nonconvex and nonsmooth
optimization problems, we propose a stochastic two-step inertial Bregman proximal …

In convex optimization, the problem of finding near-stationary points has not been
adequately studied yet, unlike other optimality measures such as the function value. Even in …