Gene Pennello

According to our database, Gene Pennello authored at least 9 papers between 2012 and 2024.

Collaborative distances:

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

2024

Designing monitoring strategies for deployed machine learning algorithms: navigating performativity through a causal lens.

Jean Feng, Adarsh Subbaswamy, Alexej Gossmann, Harvineet Singh, Berkman Sahiner, Mi-Ok Kim, Gene Anthony Pennello, Nicholas Petrick, Romain Pirracchio, Fan Xia

Proceedings of the Causal Learning and Reasoning, 2024

Monitoring machine learning-based risk prediction algorithms in the presence of performativity.

Jean Feng, Alexej Gossmann, Gene Pennello, Nicholas Petrick, Berkman Sahiner, Romain Pirracchio

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

Is this model reliable for everyone? Testing for strong calibration.

Jean Feng, Alexej Gossmann, Romain Pirracchio, Nicholas Petrick, Gene Pennello, Berkman Sahiner

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Towards a Post-Market Monitoring Framework for Machine Learning-based Medical Devices: A case study.

Jean Feng, Adarsh Subbaswamy, Alexej Gossmann, Harvineet Singh, Berkman Sahiner, Mi-Ok Kim, Gene Pennello, Nicholas Petrick, Romain Pirracchio, Fan Xia

CoRR, 2023

Decision region analysis to deconstruct the subgroup influence on AI/ML predictions.

Alexis Burgon, Nicholas Petrick, Berkman Sahiner, Gene Pennello, Ravi K. Samala

Proceedings of the Medical Imaging 2023: Computer-Aided Diagnosis, San Diego, 2023

2022

Monitoring machine learning (ML)-based risk prediction algorithms in the presence of confounding medical interventions.

Jean Feng, Alexej Gossmann, Gene Pennello, Nicholas Petrick, Berkman Sahiner, Romain Pirracchio

CoRR, 2022

Sequential algorithmic modification with test data reuse.

Jean Feng, Gene Pennello, Nicholas Petrick, Berkman Sahiner, Romain Pirracchio, Alexej Gossmann

Proceedings of the Uncertainty in Artificial Intelligence, 2022

Deciphering deep ensembles for lung nodule analysis.

Ravi K. Samala, Berkman Sahiner, Gene Pennello, Kenny H. Cha, Mohammad Mehdi Farhangi, Nicholas Petrick

Proceedings of the Medical Imaging 2022: Computer-Aided Diagnosis, San Diego, 2022

2012

Uncertainty estimation with a finite dataset in the assessment of classification models.

Weijie Chen, Waleed A. Yousef, Brandon D. Gallas, Elizabeth R. Hsu, Samir Lababidi, Rong Tang, Gene A. Pennello, W. Fraser Symmans, Lajos Pusztai

Comput. Stat. Data Anal., 2012