15 citations · 17 across the 6 of their papers we have counts for
6 papers
Hierarchical Scoring for Machine Learning Classifier Error Impact Evaluation
Erin Lanus, Daniel Wolodkin, Laura J. Freeman
A common use of machine learning (ML) models is predicting the class of a sample. Object detection is an extension of classification that includes localization of the object via a…
Test & Evaluation Best Practices for Machine Learning-Enabled Systems
Jaganmohan Chandrasekaran, Tyler Cody, Nicola McCarthy +2
Machine learning (ML) - based software systems are rapidly gaining adoption across various domains, making it increasingly essential to ensure they perform as intended. This report…
A Survey of Data Security: Practices from Cybersecurity and Challenges of Machine Learning
Padmaksha Roy, Jaganmohan Chandrasekaran, Erin Lanus +2
Machine learning (ML) is increasingly being deployed in critical systems. The data dependence of ML makes securing data used to train and test ML-enabled systems of utmost importan…
Evaluating Automated Driving Planner Robustness against Adversarial Influence
Andres Molina-Markham, Silvia G. Ionescu, Erin Lanus +3
Evaluating the robustness of automated driving planners is a critical and challenging task. Although methodologies to evaluate vehicles are well established, they do not yet accoun…
Systematic Training and Testing for Machine Learning Using Combinatorial Interaction Testing
Tyler Cody, Erin Lanus, Daniel D. Doyle +1
This paper demonstrates the systematic use of combinatorial coverage for selecting and characterizing test and training sets for machine learning models. The presented work adapts…
Test and Evaluation Framework for Multi-Agent Systems of Autonomous Intelligent Agents
Erin Lanus, Ivan Hernandez, Adam Dachowicz +6
Test and evaluation is a necessary process for ensuring that engineered systems perform as intended under a variety of conditions, both expected and unexpected. In this work, we co…