Lynn Vonder Haar

According to our database, Lynn Vonder Haar authored at least 16 papers between 2023 and 2026.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2026

Verifying Machine Learning Interpretability Requirements through Provenance.

Lynn Vonderhaar, Juan Ortiz Couder, Daryela Cisneros, Omar Ochoa

CoRR, April, 2026

Provenance as a Machine Learning Non-Functional Requirement: Trends and Future Directions.

Lynn Vonderhaar, Timothy Elvira, Omar Ochoa

Int. J. Semantic Comput., March, 2026

Measuring Contextual Reliance of Object Detection Models: A Black Box Explainability Method.

Lynn Vonderhaar, Timothy Elvira, Omar Ochoa

Int. J. Artif. Intell. Tools, March, 2026

Embedding Provenance in Computer Vision Datasets with JSON-LD.

Lynn Vonderhaar, Timothy Elvira, Tyler Thomas Procko, Omar Ochoa

CoRR, March, 2026

Verifying Machine Learning Interpretability and Explainability Requirements Through Provenance.

Lynn Vonderhaar, Juan Ortiz Couder, Tyler Thomas Procko, Eva Lueddeke, Daryela Cisneros, Omar Ochoa

Softw., 2026

A Survey of Machine Learning Lifecycle Provenance: Models, Approaches, and Tools.

Lynn Vonderhaar, Tyler Thomas Procko, Omar Ochoa

Proceedings of the 21st International Conference on Evaluation of Novel Approaches to Software Engineering, 2026

2025

Creating Robust Data Sets for AI by Leveraging Ontological Structures.

Lynn Vonderhaar, Timothy Elvira, Tyler Thomas Procko, Omar Ochoa

Proceedings of the 19th International Conference on Semantic Computing, 2025

Measuring the Impact of Scene Level Objects: A Novel Method for Quantitative Explanations.

Lynn Vonderhaar, Timothy Elvira, Omar Ochoa

Proceedings of the 38th International Florida Artificial Intelligence Research Society Conference, 2025

Exploring Requirements Engineering for Machine Learning via a Product Case Study.

Lynn Vonderhaar, Timothy Elvira, Juan Ortiz Couder, Omar Ochoa

Proceedings of the 38th International Florida Artificial Intelligence Research Society Conference, 2025

Generating and Verifying Synthetic Datasets with Requirements Engineering.

Lynn Vonderhaar, Timothy Elvira, Omar Ochoa

Proceedings of the 4th IEEE/ACM International Conference on AI Engineering, 2025

2024

Role, Needs, and State of Cognitive Assistants in Single-Pilot Operations.

Alexandra Davidoff, Lynn Vonderhaar, Alexis Caldwell, Tyler Procko, Omar Ochoa

J. Aerosp. Inf. Syst., 2024

Towards Robust Training Datasets for Machine Learning with Ontologies: A Case Study for Emergency Road Vehicle Detection.

Lynn Vonderhaar, Timothy Elvira, Tyler Procko, Omar Ochoa

CoRR, 2024

Measuring the Impact of Scene Level Objects on Object Detection: Towards Quantitative Explanations of Detection Decisions.

Lynn Vonder Haar, Timothy Elvira, Luke Newcomb, Omar Ochoa

CoRR, 2024

Exploring Testing Methods for Large Language Models.

Timothy Elvira, Tyler Thomas Procko, Lynn Vonderhaar, Omar Ochoa

Proceedings of the International Conference on Machine Learning and Applications, 2024

2023

An analysis of explainability methods for convolutional neural networks.

Lynn Vonder Haar, Timothy Elvira, Omar Ochoa

Eng. Appl. Artif. Intell., 2023

Validating Security Requirement Specifications through the use of a Knowledge Graph.

Lynn Vonder Haar, Sarah A. Reynolds, Tyler Procko, Omar Ochoa

Proceedings of the 17th IEEE International Conference on Semantic Computing, 2023