Deformable ProtoPNet: An Interpretable Image Classifier Using Deformable Prototypes. - Jon Donnelly; Alina Jade Barnett; Chaofan Chen (CVPR, 2022)

The Rashomon Importance Distribution: Getting RID of Unstable, Single Model-based Variable Importance. - Jon Donnelly; Srikar Katta; Cynthia Rudin; Edward P. Browne (NeurIPS, 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)

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., 2023)

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 (CVPR Workshops, 2024)

Position: Amazing Things Come From Having Many Good Models. - Cynthia Rudin; Chudi Zhong; Lesia Semenova; Margo I. Seltzer; Ronald Parr; Jiachang Liu 001; Srikar Katta; Jon Donnelly; Harry Chen; Zachery Boner (ICML, 2024)

Interpretable Image Classification with Adaptive Prototype-based Vision Transformers. - Chiyu Ma; Jon Donnelly; Wenjun Liu; Soroush Vosoughi; Cynthia Rudin; Chaofan Chen (NeurIPS, 2024)

Interpretable Generalized Additive Models for Datasets with Missing Values. - Hayden McTavish; Jon Donnelly; Margo I. Seltzer; Cynthia Rudin (NeurIPS, 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)

Amazing Things Come From Having Many Good Models. - Cynthia Rudin; Chudi Zhong; Lesia Semenova; Margo I. Seltzer; Ronald Parr; Jiachang Liu 001; Srikar Katta; Jon Donnelly; Harry Chen; Zachery Boner (CoRR, 2024)

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 (AAAI, 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 (CVPR, 2025)

Leveraging Predictive Equivalence in Decision Trees. - Hayden McTavish; Zachery Boner; Jon Donnelly; Margo I. Seltzer; Cynthia Rudin (CoRR, 2025)