Tracking the Frequency Moments at All Times. - Zengfeng Huang; Wai Ming Tai; Ke Yi 001 (CoRR, 2014)

Improved Coresets for Kernel Density Estimates. - Jeff M. Phillips; Wai Ming Tai (SODA, 2018)

Relative Error RKHS Embeddings for Gaussian Kernels. - Jeff M. Phillips; Wai Ming Tai (CoRR, 2018)

Approximate Guarantees for Dictionary Learning. - Aditya Bhaskara; Wai Ming Tai (COLT, 2019)

Learning In Practice: Reasoning About Quantization. - Annie Cherkaev; Waiming Tai; Jeff M. Phillips; Vivek Srikumar (CoRR, 2019)

Finding the Mode of a Kernel Density Estimate. - Jasper C. H. Lee; Jerry Li 001; Christopher Musco; Jeff M. Phillips; Wai Ming Tai (CoRR, 2019)

The GaussianSketch for Almost Relative Error Kernel Distance. - Jeff M. Phillips; Wai Ming Tai (APPROX-RANDOM, 2020)

New Nearly-Optimal Coreset for Kernel Density Estimation. - Wai Ming Tai (CoRR, 2020)

Near-Optimal Coresets of Kernel Density Estimates. - Jeff M. Phillips; Wai Ming Tai (Discret. Comput. Geom., 2020)

Finding an Approximate Mode of a Kernel Density Estimate. - Jasper C. H. Lee; Jerry Li 001; Christopher Musco; Jeff M. Phillips; Wai Ming Tai (ESA, 2021)

Geometry of Kernel Density Estimation. - Waiming Tai (2021)

Optimal estimation of Gaussian DAG models. - Ming Gao; Wai Ming Tai; Bryon Aragam (AISTATS, 2022)

Optimal Coreset for Gaussian Kernel Density Estimation. - Wai Ming Tai (SoCG, 2022)

A super-polynomial lower bound for learning nonparametric mixtures. - Bryon Aragam; Wai Ming Tai (CoRR, 2022)

Tight Bounds on the Hardness of Learning Simple Nonparametric Mixtures. - Wai Ming Tai; Bryon Aragam (COLT, 2023)

Learning Mixtures of Gaussians with Censored Data. - Wai Ming Tai; Bryon Aragam (ICML, 2023)

On Mergable Coresets for Polytope Distance. - Benwei Shi; Aditya Bhaskara; Wai Ming Tai; Jeff M. Phillips (CoRR, 2023)

Inconsistency of Cross-Validation for Structure Learning in Gaussian Graphical Models. - Zhao Lyu; Wai Ming Tai; Mladen Kolar; Bryon Aragam (AISTATS, 2024)

Optimal estimation of Gaussian (poly)trees. - Yuhao Wang; Ming Gao; Wai Ming Tai; Bryon Aragam; Arnab Bhattacharyya 001 (AISTATS, 2024)

Agnostic Active Learning of Single Index Models with Linear Sample Complexity. - Aarshvi Gajjar; Wai Ming Tai; Xingyu Xu 001; Chinmay Hegde; Christopher Musco; Yi Li (COLT, 2024)

Breaking the curse of dimensionality in structured density estimation. - Robert A. Vandermeulen; Wai Ming Tai; Bryon Aragam (NeurIPS, 2024)

Dimension-independent rates for structured neural density estimation. - Robert A. Vandermeulen; Wai Ming Tai; Bryon Aragam (CoRR, 2024)

Near-optimal Active Regression of Single-Index Models. - Yi Li; Wai Ming Tai (ICLR, 2025)

Mathesis: Towards Formal Theorem Proving from Natural Languages. - Yu Xuejun; Jianyuan Zhong; Zijin Feng; Pengyi Zhai; Roozbeh Yousefzadeh; Wei Chong Ng; Haoxiong Liu; Ziyi Shou; Jing Xiong; Yudong Zhou; Claudia Ong; Austen Jeremy Sugiarto; et al. (CoRR, 2025)