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Wai Ming Tai
Wai Ming Tai
Huawei Singapore Research Center
Verified email at huawei.com - Homepage
Title
Cited by
Cited by
Year
Near-optimal coresets of kernel density estimates
JM Phillips, WM Tai
Discrete & Computational Geometry 63, 867-887, 2020
762020
Improved coresets for kernel density estimates
JM Phillips, WM Tai
Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete …, 2018
452018
Tracking the frequency moments at all times
Z Huang, WM Tai, K Yi
arXiv preprint arXiv:1412.1763, 2014
16*2014
Optimal estimation of Gaussian DAG models
M Gao, WM Tai, B Aragam
International Conference on Artificial Intelligence and Statistics, 8738-8757, 2022
142022
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
12*2021
Optimal Coreset for Gaussian Kernel Density Estimation
WM Tai
arXiv preprint arXiv:2007.08031, 2020
10*2020
The gaussiansketch for almost relative error kernel distance
P Jeff, T Wai Ming
International Conference on Randomization and Computation (RANDOM), 2020
7*2020
Agnostic active learning of single index models with linear sample complexity
A Gajjar, WM Tai, X Xingyu, C Hegde, C Musco, Y Li
The Thirty Seventh Annual Conference on Learning Theory, 1715-1754, 2024
42024
Tight bounds on the hardness of learning simple nonparametric mixtures
WM Tai, B Aragam
The Thirty Sixth Annual Conference on Learning Theory, 2849-2849, 2023
22023
Approximate Guarantees for Dictionary Learning
A Bhaskara, WM Tai
Conference on Learning Theory, 299-317, 2019
22019
Optimal estimation of Gaussian (poly) trees
Y Wang, M Gao, WM Tai, B Aragam, A Bhattacharyya
International Conference on Artificial Intelligence and Statistics, 3619-3627, 2024
12024
Learning mixtures of Gaussians with censored data
WM Tai, B Aragam
International Conference on Machine Learning, 33396-33415, 2023
12023
Learning in practice: Reasoning about quantization
A Cherkaev, W Tai, J Phillips, V Srikumar
arXiv preprint arXiv:1905.11478, 2019
12019
Dimension-independent rates for structured neural density estimation
RA Vandermeulen, WM Tai, B Aragam
arXiv preprint arXiv:2411.15095, 2024
2024
Breaking the curse of dimensionality in structured density estimation
RA Vandermeulen, WM Tai, B Aragam
arXiv preprint arXiv:2410.07685, 2024
2024
Inconsistency of cross-validation for structure learning in Gaussian graphical models
Z Lyu, WM Tai, M Kolar, B Aragam
International Conference on Artificial Intelligence and Statistics, 3691-3699, 2024
2024
On Mergable Coresets for Polytope Distance
B Shi, A Bhaskara, WM Tai, JM Phillips
arXiv preprint arXiv:2311.05651, 2023
2023
Optimal neighbourhood selection in structural equation models
M Gao, WM Tai, B Aragam
arXiv preprint arXiv:2306.02244, 2023
2023
Geometry of Kernel Density Estimation
WM Tai
The University of Utah, 2021
2021
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Articles 1–19