| 2025 | ICLR | Near-optimal Active Regression of Single-Index Models. | Yi Li, Wai Ming Tai |
| 2025 | ICML | Dimension-Independent Rates for Structured Neural Density Estimation. | Robert A. Vandermeulen, Wai Ming Tai, Bryon Aragam |
| 2024 | AISTATS | Inconsistency of Cross-Validation for Structure Learning in Gaussian Graphical Models. | Zhao Lyu, Wai Ming Tai, Mladen Kolar, Bryon Aragam |
| 2024 | AISTATS | Optimal estimation of Gaussian (poly)trees. | Yuhao Wang, Ming Gao, Wai Ming Tai, Bryon Aragam, Arnab Bhattacharyya |
| 2024 | COLT | Agnostic Active Learning of Single Index Models with Linear Sample Complexity. | Aarshvi Gajjar, Wai Ming Tai, Xingyu Xu, Chinmay Hegde, Christopher Musco, Yi Li |
| 2023 | COLT | Tight Bounds on the Hardness of Learning Simple Nonparametric Mixtures. | Wai Ming Tai, Bryon Aragam |
| 2023 | ICML | Learning Mixtures of Gaussians with Censored Data. | Wai Ming Tai, Bryon Aragam |
| 2022 | AISTATS | Optimal estimation of Gaussian DAG models. | Ming Gao, Wai Ming Tai, Bryon Aragam |
| 2021 | ESA | Finding an Approximate Mode of a Kernel Density Estimate. | Jasper C. H. Lee, Jerry Li, Christopher Musco, Jeff M. Phillips, Wai Ming Tai |
| 2019 | COLT | Approximate Guarantees for Dictionary Learning. | Aditya Bhaskara, Wai Ming Tai |
| 2018 | SODA | Improved Coresets for Kernel Density Estimates. | Jeff M. Phillips, Wai Ming Tai |