56 citations · 58 across the 4 of their papers we have counts for
4 papers
Metric Learning Improves the Ability of Combinatorial Coverage Metrics to Anticipate Classification Error
Tyler Cody, Laura Freeman
Machine learning models are increasingly used in practice. However, many machine learning methods are sensitive to test or operational data that is dissimilar to training data. Out…
Active Learning with Combinatorial Coverage
Sai Prathyush Katragadda, Tyler Cody, Peter Beling +1
Active learning is a practical field of machine learning that automates the process of selecting which data to label. Current methods are effective in reducing the burden of data l…
Rationalization for Explainable NLP: A Survey
Sai Gurrapu, Ajay Kulkarni, Lifu Huang +3
Recent advances in deep learning have improved the performance of many Natural Language Processing (NLP) tasks such as translation, question-answering, and text classification. How…
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…