Jon Donnelly

According to our database, Jon Donnelly authored at least 13 papers between 2022 and 2025.

Collaborative distances:

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

2025

Leveraging Predictive Equivalence in Decision Trees.

Hayden McTavish, Zachery Boner, Jon Donnelly, Margo I. Seltzer, Cynthia Rudin

CoRR, June, 2025

Rashomon Sets for Prototypical-Part Networks: Editing Interpretable Models in Real-Time.

Jon Donnelly, Zhicheng Guo, Alina Jade Barnett, Hayden McTavish, Chaofan Chen, Cynthia Rudin

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2025

How Your Location Relates to Health: Variable Importance and Interpretable Machine Learning for Environmental and Sociodemographic Data.

Ishaan Maitra, Raymond Lin, Eric Chen, Jon Donnelly, Sanja Scepanovic, Cynthia Rudin

Proceedings of the AAAI-25, Sponsored by the Association for the Advancement of Artificial Intelligence, February 25, 2025

2024

Amazing Things Come From Having Many Good Models.

Cynthia Rudin, Chudi Zhong, Lesia Semenova, Margo I. Seltzer, Ronald Parr, Jiachang Liu, Srikar Katta, Jon Donnelly, Harry Chen, Zachery Boner

CoRR, 2024

This Looks Better than That: Better Interpretable Models with ProtoPNeXt.

Frank Willard, Luke Moffett, Emmanuel Mokel, Jon Donnelly, Stark Guo, Julia Yang, Giyoung Kim, Alina Jade Barnett, Cynthia Rudin

CoRR, 2024

Interpretable Generalized Additive Models for Datasets with Missing Values.

Hayden McTavish, Jon Donnelly, Margo I. Seltzer, Cynthia Rudin

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Interpretable Image Classification with Adaptive Prototype-based Vision Transformers.

Chiyu Ma, Jon Donnelly, Wenjun Liu, Soroush Vosoughi, Cynthia Rudin, Chaofan Chen

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024

Position: Amazing Things Come From Having Many Good Models.

Cynthia Rudin, Chudi Zhong, Lesia Semenova, Margo I. Seltzer, Ronald Parr, Jiachang Liu, Srikar Katta, Jon Donnelly, Harry Chen, Zachery Boner

Proceedings of the Forty-first International Conference on Machine Learning, 2024

FPN-IAIA-BL: A Multi-Scale Interpretable Deep Learning Model for Classification of Mass Margins in Digital Mammography.

Julia Yang, Alina Jade Barnett, Jon Donnelly, Satvik Kishore, Jerry Fang, Fides Regina Schwartz, Chaofan Chen, Joseph Y. Lo, Cynthia Rudin

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2024

2023

Analyzing the Influence of Agents in Trust Networks: Applying Nonsmooth Eigensensitivity Theory to a Graph Centrality Problem.

Jon Donnelly, Peter Stechlinski

SIAM J. Matrix Anal. Appl., September, 2023

ProtoEEGNet: An Interpretable Approach for Detecting Interictal Epileptiform Discharges.

Dennis Tang, Frank Willard, Ronan Tegerdine, Luke Triplett, Jon Donnelly, Luke Moffett, Lesia Semenova, Alina Jade Barnett, Jin Jing, Cynthia Rudin, M. Brandon Westover

CoRR, 2023

The Rashomon Importance Distribution: Getting RID of Unstable, Single Model-based Variable Importance.

Jon Donnelly, Srikar Katta, Cynthia Rudin, Edward P. Browne

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

2022

Deformable ProtoPNet: An Interpretable Image Classifier Using Deformable Prototypes.

Jon Donnelly, Alina Jade Barnett, Chaofan Chen

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2022