Ensembling neural networks is an effective way to increase accuracy, and can often match
the performance of individual larger models. This observation poses a natural question …

The inaccessibility of the target domain data causes domain generalization (DG) methods
prone to forget target discriminative features, and challenges the pervasive theme in existing …

This review paper focuses on the use of ensemble neural networks (ENN) in the
development of storm surge flood models. Storm surges are a major concern in coastal …

Ensembling has a long history in statistical data analysis, with many impactful applications.
However, in many modern machine learning settings, the benefits of ensembling are less …

The modern development of healthcare is characterized by a set of large volumes of tabular
data for monitoring and diagnosing the patient's condition. In addition, modern methods of …

Ensembles of machine learning models have been well established as a powerful method of
improving performance over a single model. Traditionally, ensembling algorithms train their …

Deep neural networks (DNNs) are the workhorses of deep learning, which constitutes the
state of the art in numerous application domains. However, DNN-based decision rules are …

Rapid and non-destructive prediction of starch content in kudzu is essential for the food
industry. In this work, we present an approach combining hyperspectral imaging (HSI) and …

Classic results establish that encouraging predictive diversity improves performance in
ensembles of low-capacity models, eg through bagging or boosting. Here we demonstrate …

We introduce a new PAC-Bayes oracle bound for unbounded losses. This result can be
understood as a PAC-Bayesian version of the Cram\'er-Chernoff bound. The proof technique …