83 citations · 96 across the 11 of their papers we have counts for
11 papers
Using Markov Boundary Approach for Interpretable and Generalizable Feature Selection
Anwesha Bhattacharyya, Yaqun Wang, Joel Vaughan +1
The perceived advantage of machine learning (ML) models is that they are flexible and can incorporate a large number of features. However, many of these are typically correlated or…
Behavior of Hyper-Parameters for Selected Machine Learning Algorithms: An Empirical Investigation
Anwesha Bhattacharyya, Joel Vaughan, Vijayan N. Nair
Hyper-parameters (HPs) are an important part of machine learning (ML) model development and can greatly influence performance. This paper studies their behavior for three algorithm…
Quantifying Inherent Randomness in Machine Learning Algorithms
Soham Raste, Rahul Singh, Joel Vaughan +1
Most machine learning (ML) algorithms have several stochastic elements, and their performances are affected by these sources of randomness. This paper uses an empirical study to sy…
Interpretable Feature Engineering for Time Series Predictors using Attention Networks
Tianjie Wang, Jie Chen, Joel Vaughan +1
Regression problems with time-series predictors are common in banking and many other areas of application. In this paper, we use multi-head attention networks to develop interpreta…
Traversing the Local Polytopes of ReLU Neural Networks: A Unified Approach for Network Verification
Shaojie Xu, Joel Vaughan, Jie Chen +2
Although neural networks (NNs) with ReLU activation functions have found success in a wide range of applications, their adoption in risk-sensitive settings has been limited by the…
Supervised Linear Dimension-Reduction Methods: Review, Extensions, and Comparisons
Shaojie Xu, Joel Vaughan, Jie Chen +2
Principal component analysis (PCA) is a well-known linear dimension-reduction method that has been widely used in data analysis and modeling. It is an unsupervised learning techniq…
Supervised Machine Learning Techniques: An Overview with Applications to Banking
Linwei Hu, Jie Chen, Joel Vaughan +4
This article provides an overview of Supervised Machine Learning (SML) with a focus on applications to banking. The SML techniques covered include Bagging (Random Forest or RF), Bo…
Adaptive Explainable Neural Networks (AxNNs)
Jie Chen, Joel Vaughan, Vijayan N. Nair +1
While machine learning techniques have been successfully applied in several fields, the black-box nature of the models presents challenges for interpreting and explaining the resul…
Explainable Neural Networks based on Additive Index Models
Joel Vaughan, Agus Sudjianto, Erind Brahimi +2
Machine Learning algorithms are increasingly being used in recent years due to their flexibility in model fitting and increased predictive performance. However, the complexity of t…
Network-wide Statistical Modeling and Prediction of Computer Traffic
Joel Vaughan, Stilian A. Stoev, George Michailidis
In order to maintain consistent quality of service, computer network engineers face the task of monitoring the traffic fluctuations on the individual links making up the network. H…
Global Modeling and Prediction of Computer Network Traffic
Stilian A. Stoev, George Michailidis, Joel Vaughan
We develop a probabilistic framework for global modeling of the traffic over a computer network. This model integrates existing single-link (-flow) traffic models with the routing…