| Interpretable machine learning: Fundamental principles and 10 grand challenges C Rudin, C Chen, Z Chen, H Huang, L Semenova, C Zhong Statistic Surveys 16, 1-85, 2022 | 911 | 2022 |
| Generalized and scalable optimal sparse decision trees J Lin*, C Zhong*, D Hu, C Rudin, M Seltzer International Conference on Machine Learning, 6150-6160, 2020 | 202 | 2020 |
| Exploring the whole rashomon set of sparse decision trees R Xin*, C Zhong*, Z Chen*, T Takagi, M Seltzer, C Rudin Advances in Neural Information Processing Systems 35, 14071-14084, 2022 | 70 | 2022 |
| Fast sparse decision tree optimization via reference ensembles H McTavish*, C Zhong*, R Achermann, I Karimalis, J Chen, C Rudin, ... Proceedings of the AAAI Conference on Artificial Intelligence 36 (9), 9604-9613, 2022 | 52 | 2022 |
| TimberTrek: exploring and curating sparse decision trees with interactive visualization ZJ Wang, C Zhong, R Xin, T Takagi, Z Chen, DH Chau, C Rudin, ... 2022 IEEE Visualization and Visual Analytics (VIS), 60-64, 2022 | 24 | 2022 |
| Fast sparse classification for generalized linear and additive models J Liu, C Zhong, M Seltzer, C Rudin Proceedings of machine learning research 151, 9304, 2022 | 22 | 2022 |
| FasterRisk: Fast and Accurate Interpretable Risk Scores J Liu*, C Zhong*, B Li, M Seltzer, C Rudin Advances in Neural Information Processing Systems 35, 17760-17773, 2022 | 20 | 2022 |
| Exploring and Interacting with the Set of Good Sparse Generalized Additive Models C Zhong*, Z Chen*, J Liu, M Seltzer, C Rudin Advances in Neural Information Processing Systems 36, 2024 | 13* | 2024 |
| Amazing things come from having many good models C Rudin, C Zhong, L Semenova, M Seltzer, R Parr, J Liu, S Katta, ... International Conference on Machine Learning, 42783-42795, 2024 | 12 | 2024 |
| Okridge: Scalable optimal k-sparse ridge regression J Liu, S Rosen, C Zhong, C Rudin Advances in neural information processing systems 36, 41076-41258, 2023 | 4* | 2023 |
| Interpretability and Multiplicity: a Path to Trustworthy Machine Learning C Zhong Duke University, 2024 | 1 | 2024 |
| Models That Are Interpretable But Not Transparent C Zhong, P Chen, C Rudin The 28th International Conference on Artificial Intelligence and Statistics, 0 | | |
Cynthia RudinProfessor of Computer Science, ECE, Statistics, and Biostatistics & Bioinformatics, Duke UniversityVerifierad e-postadress på cs.duke.edu
