| Near-optimal coresets of kernel density estimates JM Phillips, WM Tai Discrete & Computational Geometry 63, 867-887, 2020 | 52 | 2020 |
| Improved coresets for kernel density estimates JM Phillips, WM Tai Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete …, 2018 | 33 | 2018 |
| Tracking the frequency moments at all times Z Huang, WM Tai, K Yi arXiv preprint arXiv:1412.1763, 2014 | 12* | 2014 |
| Finding an approximate mode of a kernel density estimate JCH Lee, J Li, C Musco, JM Phillips, WM Tai 29th Annual European Symposium on Algorithms (ESA 2021), 2021 | 7* | 2021 |
| Optimal Coreset for Gaussian Kernel Density Estimation WM Tai arXiv preprint arXiv:2007.08031, 2020 | 5* | 2020 |
| The gaussiansketch for almost relative error kernel distance JM Phillips, WM Tai arXiv preprint arXiv:1811.04136, 2018 | 4* | 2018 |
| Optimal estimation of Gaussian DAG models M Gao, WM Tai, B Aragam International Conference on Artificial Intelligence and Statistics, 8738-8757, 2022 | 2 | 2022 |
| Approximate Guarantees for Dictionary Learning A Bhaskara, WM Tai Conference on Learning Theory, 299-317, 2019 | 1 | 2019 |
| Learning in practice: Reasoning about quantization A Cherkaev, W Tai, J Phillips, V Srikumar arXiv preprint arXiv:1905.11478, 2019 | 1 | 2019 |
| Tight bounds on the hardness of learning simple nonparametric mixtures B Aragam, WM Tai arXiv preprint arXiv:2203.15150, 2022 | | 2022 |
| Geometry of Kernel Density Estimation WM Tai The University of Utah, 2021 | | 2021 |
