| Oracle inequalities for sparse additive quantile regression in reproducing kernel Hilbert space S Lv, H Lin, H Lian, J Huang The Annals of Statistics 46 (2), 781-813, 2018 | 56 | 2018 |
| Model-free variable selection in reproducing kernel Hilbert space L Yang, S Lv, J Wang Journal of Machine Learning Research 17 (82), 1-24, 2016 | 47 | 2016 |
| Kernelized elastic net regularization: Generalization bounds, and sparse recovery Y Feng, SG Lv, H Hang, JAK Suykens Neural computation 28 (3), 525-562, 2016 | 36 | 2016 |
| Unified approach to coefficient-based regularized regression YL Feng, SG Lv Computers & Mathematics with Applications 62 (1), 506-515, 2011 | 24 | 2011 |
| Projected spline estimation of the nonparametric function in high-dimensional partially linear models for massive data H Lian, K Zhao, S Lv The Annals of Statistics 47 (5), 2922-2949, 2019 | 23 | 2019 |
| Using multi-class AdaBoost tree for prediction frequency of auto insurance Y Liu, BJ Wang, SG Lv Journal of Applied Finance and Banking 4 (5), 45, 2014 | 20 | 2014 |
| Integral operator approach to learning theory with unbounded sampling SG Lv, YL Feng Complex Analysis and Operator Theory 6, 533-548, 2012 | 17 | 2012 |
| Sharp learning rates of coefficient-based l q -regularized regression with indefinite kernels SG Lv, DM Shi, QW Xiao, MS Zhang Science China Mathematics 56, 1557-1574, 2013 | 12 | 2013 |
| Efficient kernel-based variable selection with sparsistency X He, J Wang, S Lv Statistica Sinica 31 (4), 2123-2151, 2021 | 11 | 2021 |
| Learning with kernelized elastic net regularization Y Feng, Y Yang, Y Zhao, S Lv, JA Suykens Leuven Belgium: KU Leuven, 2014 | 11 | 2014 |
| Generalization bounds for graph convolutional neural networks via rademacher complexity S Lv arXiv preprint arXiv:2102.10234, 2021 | 10 | 2021 |
| Debiased distributed learning for sparse partial linear models in high dimensions S Lv, H Lian Journal of Machine Learning Research 23 (2), 1-32, 2022 | 9 | 2022 |
| Optimal learning rates of lp-type multiple kernel learning under general conditions S Lv, F Zhou Information Sciences 294, 255-268, 2015 | 9 | 2015 |
| Scalable kernel-based variable selection with sparsistency X He, J Wang, S Lv arXiv preprint arXiv:1802.09246, 2018 | 8 | 2018 |
| Communication-efficient and Byzantine-robust distributed learning with statistical guarantee X Zhou, L Chang, P Xu, S Lv Pattern Recognition 137, 109312, 2023 | 7 | 2023 |
| Estimating high‐dimensional additive Cox model with time‐dependent covariate processes S Lv, J Jiang, F Zhou, J Huang, H Lin Scandinavian journal of statistics 45 (4), 900-922, 2018 | 7 | 2018 |
| On the sign consistency of the Lasso for the high-dimensional Cox model S Lv, M You, H Lin, H Lian, J Huang Journal of multivariate analysis 167, 79-96, 2018 | 7 | 2018 |
| Construction of uniform designs and complex-structured uniform designs via partitionable t-designs H Huang, H Yu, MQ Liu, D Wu Statistica Sinica 31 (4), 1689-1706, 2021 | 5 | 2021 |
| Variable selection for classification with derivative-induced regularization X He, S Lv, J Wang Statistica Sinica 30 (4), 2075-2103, 2020 | 5 | 2020 |
| Debiased distributed learning for sparse partial linear models in high dimensions S Lv, H Lian arXiv preprint arXiv:1708.05487, 2017 | 5 | 2017 |
