The last decade witnessed a growing interest in Bayesian learning. Yet, the technicality of
the topic and the multitude of ingredients involved therein, besides the complexity of turning …

Variational Bayes (VB) has become a widely-used tool for Bayesian inference in statistics
and machine learning. Nonetheless, the development of the existing VB algorithms is so far …

Key to effective generic, or “black-box,” variational inference is the selection of an
approximation to the target density that balances accuracy and speed. Copula models are …

Gaussian distributions are widely used in Bayesian variational inference to approximate
intractable posterior densities, but the ability to accommodate skewness can improve …

In semiparametric regression, traditional methods such as mixed generalized additive
models (GAM), computed via Laplace approximation or variational approximation using …

Non-Euclidean spaces routinely arise in modern statistical applications such as in medical
imaging, robotics, and computer vision, to name a few. While traditional Bayesian …

Learning efficient graph representation is the key to favorably addressing downstream tasks
on graphs, such as node or graph property prediction. Given the non-Euclidean structural …

ANALYTIC METHODS IN FINANCE WITH APPLICATIONS TO PORTFOLIO AND RISK
MANAGEMENT Page 1 ANALYTIC METHODS IN FINANCE WITH APPLICATIONS TO …

One of the most predominant techniques that have achieved phenomenal success in many
modern applications is deep learning. The obsession with massive data analysis in image …