Wai Ming Tai

Publication Activity (10 Years)

Years Active: 2014-2024
Publications (10 Years): 26

Top Topics

Density Estimation

Kernel Density Estimates

Gaussian Kernel

Top Venues

Publications

Yuhao Wang, Ming Gao, Wai Ming Tai, Bryon Aragam, Arnab Bhattacharyya
Optimal estimation of Gaussian (poly)trees. CoRR (2024)
Yuhao Wang, Ming Gao, Wai Ming Tai, Bryon Aragam, Arnab Bhattacharyya
Optimal estimation of Gaussian (poly)trees. AISTATS (2024)
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 (2024)
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)
Zhao Lyu, Wai Ming Tai, Mladen Kolar, Bryon Aragam
Inconsistency of Cross-Validation for Structure Learning in Gaussian Graphical Models. AISTATS (2024)
Wai Ming Tai, Bryon Aragam
Learning Mixtures of Gaussians with Censored Data. CoRR (2023)
Benwei Shi, Aditya Bhaskara, Wai Ming Tai, Jeff M. Phillips
On Mergable Coresets for Polytope Distance. CoRR (2023)
Wai Ming Tai, Bryon Aragam
Tight Bounds on the Hardness of Learning Simple Nonparametric Mixtures. COLT (2023)
Wai Ming Tai, Bryon Aragam
Learning Mixtures of Gaussians with Censored Data. ICML (2023)
Zhao Lyu, Wai Ming Tai, Mladen Kolar, Bryon Aragam
Inconsistency of cross-validation for structure learning in Gaussian graphical models. CoRR (2023)
Ming Gao, Wai Ming Tai, Bryon Aragam
Optimal estimation of Gaussian DAG models. CoRR (2022)
Ming Gao, Wai Ming Tai, Bryon Aragam
Optimal estimation of Gaussian DAG models. AISTATS (2022)
Bryon Aragam, Wai Ming Tai
A super-polynomial lower bound for learning nonparametric mixtures. CoRR (2022)
Wai Ming Tai
Optimal Coreset for Gaussian Kernel Density Estimation. SoCG (2022)
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)
Jeff M. Phillips, Wai Ming Tai
Near-Optimal Coresets of Kernel Density Estimates. Discret. Comput. Geom. 63 (4) (2020)
Jeff M. Phillips, Wai Ming Tai
The GaussianSketch for Almost Relative Error Kernel Distance. APPROX-RANDOM (2020)
Wai Ming Tai
New Nearly-Optimal Coreset for Kernel Density Estimation. CoRR (2020)
Jasper C. H. Lee, Jerry Li, Christopher Musco, Jeff M. Phillips, Wai Ming Tai
Finding the Mode of a Kernel Density Estimate. CoRR (2019)
Aditya Bhaskara, Wai Ming Tai
Approximate Guarantees for Dictionary Learning. COLT (2019)
Aditya Bhaskara, Wai Ming Tai
Approximate Guarantees for Dictionary Learning. CoRR (2019)
Jeff M. Phillips, Wai Ming Tai
Relative Error RKHS Embeddings for Gaussian Kernels. CoRR (2018)
Jeff M. Phillips, Wai Ming Tai
Improved Coresets for Kernel Density Estimates. SODA (2018)
Jeff M. Phillips, Wai Ming Tai
Near-Optimal Coresets of Kernel Density Estimates. SoCG (2018)
Jeff M. Phillips, Wai Ming Tai
Near-Optimal Coresets of Kernel Density Estimates. CoRR (2018)
Jeff M. Phillips, Wai Ming Tai
Improved Coresets for Kernel Density Estimates. CoRR (2017)
Zengfeng Huang, Wai Ming Tai, Ke Yi
Tracking the Frequency Moments at All Times. CoRR (2014)