Fast growth of produced data from deferent smart devices such as smart mobiles, IoT/IIoT
networks, and vehicular networks running different specific applications such as Augmented …

The increasing penetration of renewable generations, along with the deregulation and
marketization of power industry, promotes the transformation of energy market operation …

Progressively applying Gaussian noise transforms complex data distributions to
approximately Gaussian. Reversing this dynamic defines a generative model. When the …

Since the seminal work of Venkatakrishnan, Bouman, and Wohlberg [Proceedings of the
Global Conference on Signal and Information Processing, IEEE, 2013, pp. 945--948] in …

For the task of sampling from a density $\pi\propto\exp (-V) $ on $\R^ d $, where $ V $ is
possibly non-convex but $ L $-gradient Lipschitz, we prove that averaged Langevin Monte …

Markov chain Monte Carlo (MCMC) is an essential set of tools for estimating features of
probability distributions commonly encountered in modern applications. For MCMC …

This book is concerned with extreme value theory for stochastic processes whose finite-
dimensional distributions are heavy-tailed in the restrictive sense of regular variation. These …

Purpose We introduce a framework that enables efficient sampling from learned probability
distributions for MRI reconstruction. Method Samples are drawn from the posterior …

The idea of slicing divergences has been proven to be successful when comparing two
probability measures in various machine learning applications including generative …

This paper delves into stochastic optimization problems that involve Markovian noise. We
present a unified approach for the theoretical analysis of first-order gradient methods for …