Sparse sliced inverse regression via lasso Q Lin, Z Zhao, JS Liu Journal of the American Statistical Association, 2019 | 105 | 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 | 100 | 2018 |
Randomization inference for peer effects X Li, P Ding, Q Lin, D Yang, JS Liu Journal of the American Statistical Association, 2019 | 32 | 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), 2021 | 26* | 2021 |
Signed support recovery for single index models in high-dimensions M Neykov, Q Lin, JS Liu arXiv preprint arXiv:1511.02270, 2015 | 17 | 2015 |
Generalization Ability of Wide Neural Networks on J Lai, M Xu, R Chen, Q Lin arXiv preprint arXiv:2302.05933, 2023 | 16 | 2023 |
On the Saturation Effect of Kernel Ridge Regression Y Li, H Zhang, Q Lin International Conference on Learning Representations, 2023 | 16 | 2023 |
On the Optimality of Misspecified Kernel Ridge Regression H Zhang, Y Li, W Lu, Q Lin arXiv preprint arXiv:2305.07241, 2023 | 14 | 2023 |
Quadratic Deformations of Lie–Poisson Structures Q Lin, Z Liu, Y Sheng Letters in Mathematical Physics 83 (3), 217-229, 2008 | 12 | 2008 |
Isogeny orbits in a family of abelian varieties Q Lin, MX Wang arXiv preprint arXiv:1403.3976, 2014 | 10 | 2014 |
Kernel interpolation generalizes poorly Y Li, H Zhang, Q Lin Biometrika 111 (2), 715-722, 2024 | 9 | 2024 |
On the asymptotic learning curves of kernel ridge regression under power-law decay Y Li, Q Lin Advances in Neural Information Processing Systems 36, 2024 | 8 | 2024 |
On the optimality of misspecified spectral algorithms H Zhang, Y Li, Q Lin Journal of Machine Learning Research 25 (188), 1-50, 2024 | 8 | 2024 |
Highest weight modules at the critical level and noncommutative Springer resolution R Bezrukavnikov, Q Lin arXiv preprint arXiv:1108.1906, 2011 | 7 | 2011 |
Statistical optimality of deep wide neural networks Y Li, Z Yu, G Chen, Q Lin arXiv preprint arXiv:2305.02657, 2023 | 6 | 2023 |
Optimal rate of kernel regression in large dimensions W Lu, H Zhang, Y Li, M Xu, Q Lin arXiv preprint arXiv:2309.04268, 2023 | 3 | 2023 |
Generalization ability of wide residual networks J Lai, Z Yu, S Tian, Q Lin arXiv preprint arXiv:2305.18506, 2023 | 2 | 2023 |
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 | 1 | 2024 |
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 | 1 | 2024 |
On the optimality of functional sliced inverse regression R Chen, S Tian, D Huang, Q Lin, JS Liu arXiv preprint arXiv:2307.02777, 2023 | 1 | 2023 |