Steep increases in air temperatures and CO 2 emissions have been associated with the
global demand for energy. This is coupled with population growth and improved living …

An ever-increasing share of human interaction, communication, and culture is recorded as
digital text. We provide an introduction to the use of text as an input to economic research …

A novel variational autoencoder is developed to model images, as well as associated labels
or captions. The Deep Generative Deconvolutional Network (DGDN) is used as a decoder of …

We develop a deep learning model to predict traffic flows. The main contribution is
development of an architecture that combines a linear model that is fitted using ℓ 1 …

An important problem in econometrics and marketing is to infer the causal impact that a
designed market intervention has exerted on an outcome metric over time. This paper …

The recent advances of wearable sensors are remarkable but there are still limitations that
they need to be refabricated to tune the sensor for target signal. However, biological sensory …

Learning-from-crowds aims to design proper aggregation strategies to infer the unknown
true labels from the noisy labels provided by ordinary web workers. This paper presents max …

Stacking is a widely used model averaging technique that asymptotically yields optimal
predictions among linear averages. We show that stacking is most effective when model …

We propose the Bayesian bridge estimator for regularized regression and classification. Two
key mixture representations for the Bayesian bridge model are developed: a scale mixture of …

Calibration of credible regions derived from under-or misspecified models is an important
and challenging problem. In this paper, we introduce a scalar tuning parameter that controls …