| Understanding how dimension reduction tools work: an empirical approach to deciphering t-SNE, UMAP, TriMAP, and PaCMAP for data visualization Y Wang, H Huang, C Rudin, Y Shaposhnik Journal of Machine Learning Research 22 (201), 1-73, 2021 | 420 | 2021 |
| Towards a comprehensive evaluation of dimension reduction methods for transcriptomic data visualization H Huang, Y Wang, C Rudin, EP Browne Communications biology 5 (1), 719, 2022 | 76 | 2022 |
| Approximate group fairness for clustering B Li, L Li, A Sun, C Wang, Y Wang International conference on machine learning, 6381-6391, 2021 | 24 | 2021 |
| Navigating the Effect of Parametrization for Dimensionality Reduction H Huang, Y Wang, C Rudin arXiv preprint arXiv:2411.15894, 2024 | 1 | 2024 |
| Machine learning approaches identify immunologic signatures of total and intact HIV DNA during long-term antiretroviral therapy L Semenova, Y Wang, S Falcinelli, N Archin, AD Cooper-Volkheimer, ... Elife 13, RP94899, 2024 | 1 | 2024 |
| Dimension Reduction with Locally Adjusted Graphs Y Wang, Y Sun, H Huang, C Rudin arXiv preprint arXiv:2412.15426, 2024 | | 2024 |
Cynthia RudinProfessor of Computer Science, ECE, Statistics, and Biostatistics & Bioinformatics, Duke UniversityVerified email at cs.duke.edu
