Given a set of discrete probability distributions, the minimum entropy coupling is the
minimum entropy joint distribution that has the input distributions as its marginals. This has …

One-shot channel simulation (or channel synthesis) has seen increasing applications in
lossy compression, differential privacy and machine learning. In this setting, an encoder …

System testing mainly focuses on finding bugs before online deployment. However, the
system's complexity increases due to a surge in the number of users, which makes …

It is still a challenging problem for most existing forecasting methods to obtain accurate and
rapid prediction performance in short-term load forecasting because of the complexity and …

We examine the minimum entropy coupling problem, where one must find the minimum
entropy variable that has a given set of distributions S={p 1,…, pm} as its marginals …

Given two jointly distributed random variables \left(X,Y\right), a functional representation of X
is a random variable Z independent of Y, and a deterministic function g\left(⋅,⋅\right) such …

Minimum-entropy coupling (MEC)--the process of finding a joint distribution with minimum
entropy for given marginals--has applications in areas such as causality and steganography …

Dimension reduction is a technique used to transform data from a high-dimensional space
into a lower-dimensional space, aiming to retain as much of the original information as …

We study an information analogue of infinitely divisible probability distributions, where the iid
sum is replaced by the joint distribution of an iid sequence. A random variable is called …

In this paper, we introduce novel characterizations of the classical concept of majorization in
terms of upper triangular (resp., lower triangular) row-stochastic matrices, and in terms of …