| A high-dimensional nonparametric multivariate test for mean vector L Wang, B Peng, R Li Journal of the American Statistical Association 110 (512), 1658-1669, 2015 | 139 | 2015 |
| An iterative coordinate descent algorithm for high-dimensional nonconvex penalized quantile regression B Peng, L Wang Journal of Computational and Graphical Statistics 24 (3), 676-694, 2015 | 94 | 2015 |
| A tuning-free robust and efficient approach to high-dimensional regression L Wang, B Peng, J Bradic, R Li, Y Wu Journal of the American Statistical Association 115 (532), 1700-1714, 2020 | 45 | 2020 |
| An error bound for l1-norm support vector machine coefficients in ultra-high dimension B Peng, L Wang, Y Wu Journal of Machine Learning Research 17 (233), 1-26, 2016 | 38 | 2016 |
| A Blended Deep Learning Approach for Predicting User Intended Actions F Tan, Z Wei, J He, X Wu, B Peng, H Liu, Z Yan 2018 IEEE International Conference on Data Mining (ICDM), 487-496, 2018 | 21 | 2018 |
| Hybrid Deep-Learning Action Prediction Architecture Z Yan, J He, F Tan, X Wu, B Peng, A Pani US Patent App. 16/152,227, 2020 | 1 | 2020 |
| Methodologies and algorithms on some non-convex penalized models for ultra high dimensional data B Peng | 1 | 2016 |
| Rejoinder to “A Tuning-Free Robust and Efficient Approach to High-Dimensional Regression” L Wang, B Peng, J Bradic, R Li, Y Wu Journal of the American Statistical Association 115 (532), 1726-1729, 2020 | | 2020 |
| GENERATING A PREDICTIVE BEHAVIOR MODEL FOR PREDICTING USER BEHAVIOR USING UNSUPERVISED FEATURE LEARNING AND A RECURRENT NEURAL NETWORK B Peng, J Viladomat, Z Yan, A Pani US Patent App. 15/812,568, 2019 | | 2019 |
| 1 Additional technical results for proving The-orems 1& 2 L Wang, B Peng, J Bradic, R Li, Y Wu | | |
| Fit a nonconvex penalized quantile regression model B Peng | | |
Runze LiPenn State UnviersityПотвърден имейл адрес: psu.edu
