| Sparse sliced inverse regression via lasso Q Lin, Z Zhao, JS Liu Journal of the American Statistical Association 114 (528), 1726-1739, 2019 | 115 | 2019 |
| On consistency and sparsity for sliced inverse regression in high dimensions Q Lin, Z Zhao, JS Liu The Annals of Statistics 46 (2), 580-610, 2018 | 107 | 2018 |
| Randomization inference for peer effects X Li, P Ding, Q Lin, D Yang, JS Liu Journal of the American Statistical Association, 2019 | 38 | 2019 |
| On the optimality of sliced inverse regression in high dimensions Q Lin, X Li, D Huang, JS Liu The Annals of Statistics 49 (1), 1-20, 2021 | 28* | 2021 |
| Generalization Ability of Wide Neural Networks on J Lai, M Xu, R Chen, Q Lin arXiv preprint arXiv:2302.05933, 2023 | 26 | 2023 |
| On the Saturation Effect of Kernel Ridge Regression Y Li, H Zhang, Q Lin International Conference on Learning Representations, 2023 | 25 | 2023 |
| On the asymptotic learning curves of kernel ridge regression under power-law decay Y Li, Q Lin Advances in Neural Information Processing Systems 36, 49341-49364, 2023 | 21 | 2023 |
| On the Optimality of Misspecified Kernel Ridge Regression H Zhang, Y Li, W Lu, Q Lin arXiv preprint arXiv:2305.07241, 2023 | 20 | 2023 |
| On the optimality of misspecified spectral algorithms H Zhang, Y Li, Q Lin Journal of Machine Learning Research 25 (188), 1-50, 2024 | 17 | 2024 |
| Signed support recovery for single index models in high-dimensions M Neykov, Q Lin, JS Liu arXiv preprint arXiv:1511.02270, 2015 | 17 | 2015 |
| Kernel interpolation generalizes poorly Y Li, H Zhang, Q Lin Biometrika 111 (2), 715-722, 2024 | 14 | 2024 |
| Quadratic Deformations of Lie–Poisson Structures Q Lin, Z Liu, Y Sheng Letters in Mathematical Physics 83 (3), 217-229, 2008 | 13 | 2008 |
| On the eigenvalue decay rates of a class of neural-network related kernel functions defined on general domains Y Li, Z Yu, G Chen, Q Lin Journal of Machine Learning Research 25 (82), 1-47, 2024 | 12 | 2024 |
| Isogeny orbits in a family of abelian varieties Q Lin, MX Wang arXiv preprint arXiv:1403.3976, 2014 | 10 | 2014 |
| Optimal rate of kernel regression in large dimensions W Lu, H Zhang, Y Li, M Xu, Q Lin arXiv preprint arXiv:2309.04268, 2023 | 9 | 2023 |
| Generalization error curves for analytic spectral algorithms under power-law decay Y Li, W Gan, Z Shi, Q Lin arXiv preprint arXiv:2401.01599, 2024 | 7 | 2024 |
| Statistical optimality of deep wide neural networks Y Li, Z Yu, G Chen, Q Lin arXiv preprint arXiv:2305.02657, 2023 | 7 | 2023 |
| Highest weight modules at the critical level and noncommutative Springer resolution R Bezrukavnikov, Q Lin arXiv preprint arXiv:1108.1906, 2011 | 7 | 2011 |
| The phase diagram of kernel interpolation in large dimensions H Zhang, W Lu, Q Lin Biometrika 112 (1), asae057, 2025 | 6 | 2025 |
| Optimal rates of kernel ridge regression under source condition in large dimensions H Zhang, Y Li, W Lu, Q Lin arXiv preprint arXiv:2401.01270, 2024 | 6 | 2024 |
Jun S LiuProfessor of statistics, Harvard UniversityVerified email at stat.harvard.edu
