We perform a comparative analysis of machine learning methods for the canonical problem
of empirical asset pricing: measuring asset risk premiums. We demonstrate large economic …

We explore the use of deep learning hierarchical models for problems in financial prediction
and classification. Financial prediction problems–such as those presented in designing and …

We explore the use of deep learning hierarchical models for problems in financial prediction
and classification. Financial prediction problems--such as those presented in designing and …

Modern imaging methods rely strongly on Bayesian inference techniques to solve
challenging imaging problems. Currently, the predominant Bayesian computation approach …

Hypergraph neural networks (HNNs) using neural networks to encode hypergraphs provide
a promising way to model higher-order relations in data and further solve relevant prediction …

The goal of this paper is to contrast and survey the major advances in two of the most
commonly used high-dimensional techniques, namely, the Lasso and horseshoe …

Convex nonsmooth optimization problems, whose solutions live in very high dimensional
spaces, have become ubiquitous. To solve them, the class of first-order algorithms known as …

Deep learning is a form of machine learning for nonlinear high dimensional pattern
matching and prediction. By taking a Bayesian probabilistic perspective, we provide a …

Designing the radar waveform, which ensures spectral compatibility with the communication
systems, is known to be challenging. In addition to having the desirable transmitter …

Proximal splitting algorithms are well suited to solving large-scale nonsmooth optimization
problems, in particular those arising in machine learning. We propose a new primal-dual …