In this paper, we tackle air quality forecasting by using machine learning approaches to
predict the hourly concentration of air pollutants (eg, ozone, particle matter (PM 2.5) and …

A nonnegative latent factorization of tensors (NLFT) model precisely represents the temporal
patterns hidden in multichannel data emerging from various applications. It often adopts a …

We consider online convex optimization with stochastic constraints where the objective
functions are arbitrarily time-varying and the constraint functions are independent and …

Plug-and-Play (PnP) is a non-convex optimization framework that combines proximal
algorithms, for example, the alternating direction method of multipliers (ADMM), with …

Zeroth-order (aka, derivative-free) methods are a class of effective optimization methods for
solving complex machine learning problems, where gradients of the objective functions are …

Federated learning (FL) is a promising framework for learning from distributed data while
maintaining privacy. The development of efficient FL algorithms encounters various …

In this article, we propose a novel algorithm, namely PETRELS-ADMM, to deal with
subspace tracking in the presence of outliers and missing data. The proposed approach …

Although stochastic gradient descent (SGD) method and its variants (eg, stochastic
momentum methods, AdaGrad) are the choice of algorithms for solving non-convex …

Recently, many stochastic variance reduced alternating direction methods of multipliers
(ADMMs)(eg, SAG-ADMM and SVRG-ADMM) have made exciting progress such as linear …

In this paper, we propose a faster stochastic alternating direction method of multipliers
(ADMM) for nonconvex optimization by using a new stochastic path-integrated differential …