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ORCIDWaiming Tai
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- affiliation: University of Chicago, IL, USA
- affiliation (former): University of Utah, School of Computing, Salt Lake City, UT, USA
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2020 – today
- 2024
[c12]Yuhao Wang, Ming Gao, Wai Ming Tai, Bryon Aragam, Arnab Bhattacharyya:
Optimal estimation of Gaussian (poly)trees. AISTATS 2024: 3619-3627- [c11]Zhao Lyu, Wai Ming Tai, Mladen Kolar, Bryon Aragam:
Inconsistency of Cross-Validation for Structure Learning in Gaussian Graphical Models. AISTATS 2024: 3691-3699 - [c10]Aarshvi Gajjar, Wai Ming Tai, Xingyu Xu, Chinmay Hegde, Christopher Musco, Yi Li:
Agnostic Active Learning of Single Index Models with Linear Sample Complexity. COLT 2024: 1715-1754 - [i15]Yuhao Wang, Ming Gao, Wai Ming Tai, Bryon Aragam, Arnab Bhattacharyya:
Optimal estimation of Gaussian (poly)trees. CoRR abs/2402.06380 (2024) - [i14]Aarshvi Gajjar, Wai Ming Tai, Xingyu Xu, Chinmay Hegde, Christopher Musco, Yi Li:
Agnostic Active Learning of Single Index Models with Linear Sample Complexity. CoRR abs/2405.09312 (2024) - 2023
- [c9]Wai Ming Tai, Bryon Aragam:
Tight Bounds on the Hardness of Learning Simple Nonparametric Mixtures. COLT 2023: 2849 - [c8]Wai Ming Tai, Bryon Aragam:
Learning Mixtures of Gaussians with Censored Data. ICML 2023: 33396-33415 - [i13]Wai Ming Tai, Bryon Aragam:
Learning Mixtures of Gaussians with Censored Data. CoRR abs/2305.04127 (2023) - [i12]Benwei Shi, Aditya Bhaskara, Wai Ming Tai, Jeff M. Phillips:
On Mergable Coresets for Polytope Distance. CoRR abs/2311.05651 (2023) - [i11]Zhao Lyu, Wai Ming Tai, Mladen Kolar, Bryon Aragam:
Inconsistency of cross-validation for structure learning in Gaussian graphical models. CoRR abs/2312.17047 (2023) - 2022
- [c7]Ming Gao, Wai Ming Tai, Bryon Aragam:
Optimal estimation of Gaussian DAG models. AISTATS 2022: 8738-8757 - [c6]Wai Ming Tai:
Optimal Coreset for Gaussian Kernel Density Estimation. SoCG 2022: 63:1-63:15 - [i10]Ming Gao, Wai Ming Tai, Bryon Aragam:
Optimal estimation of Gaussian DAG models. CoRR abs/2201.10548 (2022) - [i9]Bryon Aragam, Wai Ming Tai:
A super-polynomial lower bound for learning nonparametric mixtures. CoRR abs/2203.15150 (2022) - 2021
- [b1]Waiming Tai:
Geometry of Kernel Density Estimation. University of Utah, USA, 2021 - [c5]Jasper C. H. Lee, Jerry Li, Christopher Musco, Jeff M. Phillips, Wai Ming Tai:
Finding an Approximate Mode of a Kernel Density Estimate. ESA 2021: 61:1-61:19 - 2020
- [j1]Jeff M. Phillips, Wai Ming Tai
:
Near-Optimal Coresets of Kernel Density Estimates. Discret. Comput. Geom. 63(4): 867-887 (2020) - [c4]Jeff M. Phillips, Wai Ming Tai:
The GaussianSketch for Almost Relative Error Kernel Distance. APPROX-RANDOM 2020: 12:1-12:20 - [i8]Wai Ming Tai:
New Nearly-Optimal Coreset for Kernel Density Estimation. CoRR abs/2007.08031 (2020)
2010 – 2019
- 2019
- [c3]Aditya Bhaskara, Wai Ming Tai:
Approximate Guarantees for Dictionary Learning. COLT 2019: 299-317 - [i7]Annie Cherkaev, Waiming Tai, Jeff M. Phillips, Vivek Srikumar:
Learning In Practice: Reasoning About Quantization. CoRR abs/1905.11478 (2019) - [i6]Aditya Bhaskara, Wai Ming Tai:
Approximate Guarantees for Dictionary Learning. CoRR abs/1905.12091 (2019) - [i5]Jasper C. H. Lee, Jerry Li, Christopher Musco, Jeff M. Phillips, Wai Ming Tai:
Finding the Mode of a Kernel Density Estimate. CoRR abs/1912.07673 (2019) - 2018
- [c2]Jeff M. Phillips, Wai Ming Tai:
Near-Optimal Coresets of Kernel Density Estimates. SoCG 2018: 66:1-66:13 - [c1]Jeff M. Phillips, Wai Ming Tai:
Improved Coresets for Kernel Density Estimates. SODA 2018: 2718-2727 - [i4]Jeff M. Phillips, Wai Ming Tai:
Near-Optimal Coresets of Kernel Density Estimates. CoRR abs/1802.01751 (2018) - [i3]Jeff M. Phillips, Wai Ming Tai:
Relative Error RKHS Embeddings for Gaussian Kernels. CoRR abs/1811.04136 (2018) - 2017
- [i2]Jeff M. Phillips, Wai Ming Tai:
Improved Coresets for Kernel Density Estimates. CoRR abs/1710.04325 (2017) - 2014
- [i1]Zengfeng Huang, Wai Ming Tai, Ke Yi:
Tracking the Frequency Moments at All Times. CoRR abs/1412.1763 (2014)
Coauthor Index
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