| 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 | 888 | 2022 |
| Adversarial feature matching for text generation Y Zhang, Z Gan, K Fan, Z Chen, R Henao, D Shen, L Carin International conference on machine learning, 4006-4015, 2017 | 482 | 2017 |
| Concept whitening for interpretable image recognition Z Chen, Y Bei, C Rudin Nature Machine Intelligence 2 (12), 772-782, 2020 | 409 | 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 | 67 | 2022 |
| How to see hidden patterns in metamaterials with interpretable machine learning Z Chen, A Ogren, C Daraio, LC Brinson, C Rudin Extreme Mechanics Letters 57, 101895, 2022 | 25 | 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 |
| Missing values and imputation in healthcare data: Can interpretable machine learning help? Z Chen, S Tan, U Chajewska, C Rudin, R Caruna Conference on Health, Inference, and Learning, 86-99, 2023 | 17 | 2023 |
| Using explainable boosting machines (ebms) to detect common flaws in data Z Chen, S Tan, H Nori, K Inkpen, Y Lou, R Caruana Joint European Conference on Machine Learning and Knowledge Discovery in …, 2021 | 14 | 2021 |
| 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 | 12* | 2024 |
| Segdiscover: Visual concept discovery via unsupervised semantic segmentation H Huang*, Z Chen*, C Rudin arXiv preprint arXiv:2204.10926, 2022 | 10 | 2022 |
| Sparse and Faithful Explanations Without Sparse Models Y Sun*, Z Chen*, V Orlandi, T Wang, C Rudin arXiv preprint arXiv:2402.09702, 2024 | 1 | 2024 |
| Human-in-the-loop Machine Learning System via Model Interpretability Z Chen Duke University, 2023 | 1 | 2023 |
| Designing 2D mechanical metamaterials with printability constraints using interpretable machine learning M Bastawrous, Z Chen, LC Brinson APS March Meeting Abstracts 2022, W21. 001, 2022 | | 2022 |
Cynthia RudinProfessor of Computer Science, ECE, Statistics, and Biostatistics & Bioinformatics, Duke UniversityEmail verificata su cs.duke.edu