Wai Ming Tai

35 papers A* 10A 4Journal 20Unranked 1
YearRankTypeTitle / Venue / Authors
2025 A* conf
Dimension-Independent Rates for Structured Neural Density Estimation.
ICML
Robert A. Vandermeulen, Wai Ming Tai, Bryon Aragam
2025 J jnl
Mathesis: Towards Formal Theorem Proving from Natural Languages.
CoRR
Xuejun Yu, Jianyuan Zhong, Zijin Feng, Pengyi Zhai, Roozbeh Yousefzadeh, Wei Chong Ng, Haoxiong Liu, Ziyi Shou, Jing Xiong, Yudong Zhou, Claudia Ong, Austen Jeremy Sugiarto, Yaoxi Zhang, Wai Ming Tai, Huan Cao, Dongcai Lu, Jiacheng Sun, Qiang Xu, Shen Xin, Zhenguo Li
2025 A* conf
Near-optimal Active Regression of Single-Index Models.
ICLR
Yi Li, Wai Ming Tai
2025 J jnl
Near-optimal Active Regression of Single-Index Models.
CoRR
Yi Li, Wai Ming Tai
2025 J jnl
ProofFlow: A Dependency Graph Approach to Faithful Proof Autoformalization.
CoRR
Rafael Cabral, Tuan Manh Do, Xuejun Yu, Wai Ming Tai, Zijin Feng, Shen Xin
2024 A* conf
Agnostic Active Learning of Single Index Models with Linear Sample Complexity.
COLT
Aarshvi Gajjar, Wai Ming Tai, Xingyu Xu, Chinmay Hegde, Christopher Musco, Yi Li
2024 J jnl
Agnostic Active Learning of Single Index Models with Linear Sample Complexity.
CoRR
Aarshvi Gajjar, Wai Ming Tai, Xingyu Xu, Chinmay Hegde, Christopher Musco, Yi Li
2024 A* conf
Breaking the curse of dimensionality in structured density estimation.
NeurIPS
Robert A. Vandermeulen, Wai Ming Tai, Bryon Aragam
2024 J jnl
Breaking the curse of dimensionality in structured density estimation.
CoRR
Robert A. Vandermeulen, Wai Ming Tai, Bryon Aragam
2024 J jnl
Dimension-independent rates for structured neural density estimation.
CoRR
Robert A. Vandermeulen, Wai Ming Tai, Bryon Aragam
2024 A conf
Inconsistency of Cross-Validation for Structure Learning in Gaussian Graphical Models.
AISTATS
Zhao Lyu, Wai Ming Tai, Mladen Kolar, Bryon Aragam
2024 A conf
Optimal estimation of Gaussian (poly)trees.
AISTATS
Yuhao Wang, Ming Gao, Wai Ming Tai, Bryon Aragam, Arnab Bhattacharyya
2024 J jnl
Optimal estimation of Gaussian (poly)trees.
CoRR
Yuhao Wang, Ming Gao, Wai Ming Tai, Bryon Aragam, Arnab Bhattacharyya
2023 J jnl
Inconsistency of cross-validation for structure learning in Gaussian graphical models.
CoRR
Zhao Lyu, Wai Ming Tai, Mladen Kolar, Bryon Aragam
2023 A* conf
Learning Mixtures of Gaussians with Censored Data.
ICML
Wai Ming Tai, Bryon Aragam
2023 J jnl
Learning Mixtures of Gaussians with Censored Data.
CoRR
Wai Ming Tai, Bryon Aragam
2023 J jnl
On Mergable Coresets for Polytope Distance.
CoRR
Benwei Shi, Aditya Bhaskara, Wai Ming Tai, Jeff M. Phillips
2023 A* conf
Tight Bounds on the Hardness of Learning Simple Nonparametric Mixtures.
COLT
Wai Ming Tai, Bryon Aragam
2022 J jnl
A super-polynomial lower bound for learning nonparametric mixtures.
CoRR
Bryon Aragam, Wai Ming Tai
2022 A* conf
Optimal Coreset for Gaussian Kernel Density Estimation.
SoCG
Wai Ming Tai
2022 A conf
Optimal estimation of Gaussian DAG models.
AISTATS
Ming Gao, Wai Ming Tai, Bryon Aragam
2022 J jnl
Optimal estimation of Gaussian DAG models.
CoRR
Ming Gao, Wai Ming Tai, Bryon Aragam
2021 A conf
Finding an Approximate Mode of a Kernel Density Estimate.
ESA
Jasper C. H. Lee, Jerry Li, Christopher Musco, Jeff M. Phillips, Wai Ming Tai
2020 J jnl
Near-Optimal Coresets of Kernel Density Estimates.
Discret. Comput. Geom.
Jeff M. Phillips, Wai Ming Tai
2020 J jnl
New Nearly-Optimal Coreset for Kernel Density Estimation.
CoRR
Wai Ming Tai
2020 — conf
The GaussianSketch for Almost Relative Error Kernel Distance.
APPROX-RANDOM
Jeff M. Phillips, Wai Ming Tai
2019 A* conf
Approximate Guarantees for Dictionary Learning.
COLT
Aditya Bhaskara, Wai Ming Tai
2019 J jnl
Approximate Guarantees for Dictionary Learning.
CoRR
Aditya Bhaskara, Wai Ming Tai
2019 J jnl
Finding the Mode of a Kernel Density Estimate.
CoRR
Jasper C. H. Lee, Jerry Li, Christopher Musco, Jeff M. Phillips, Wai Ming Tai
2018 A* conf
Improved Coresets for Kernel Density Estimates.
SODA
Jeff M. Phillips, Wai Ming Tai
2018 A* conf
Near-Optimal Coresets of Kernel Density Estimates.
SoCG
Jeff M. Phillips, Wai Ming Tai
2018 J jnl
Near-Optimal Coresets of Kernel Density Estimates.
CoRR
Jeff M. Phillips, Wai Ming Tai
2018 J jnl
Relative Error RKHS Embeddings for Gaussian Kernels.
CoRR
Jeff M. Phillips, Wai Ming Tai
2017 J jnl
Improved Coresets for Kernel Density Estimates.
CoRR
Jeff M. Phillips, Wai Ming Tai
2014 J jnl
Tracking the Frequency Moments at All Times.
CoRR
Zengfeng Huang, Wai Ming Tai, Ke Yi