| Deep Nonparametric Regression on Approximate Manifolds: Non-Asymptotic Error Bounds with Polynomial Prefactors Y Jiao, G Shen, Y Lin, J Huang Annals of Statistics, 2023 | 74* | 2023 |
| Deep quantile regression: Mitigating the curse of dimensionality through composition G Shen, Y Jiao, Y Lin, JL Horowitz, J Huang arXiv preprint arXiv:2107.04907, 2021 | 28 | 2021 |
| Approximation with CNNs in Sobolev Space: with Applications to Classification G Shen, Y Jiao, Y Lin, J Huang Neural Information Processing Systems 2022, 2022 | 15 | 2022 |
| Robust nonparametric regression with deep neural networks G Shen, Y Jiao, Y Lin, J Huang arXiv preprint arXiv:2107.10343, 2021 | 12 | 2021 |
| Robust post-selection inference of high-dimensional mean regression with heavy-tailed asymmetric or heteroskedastic errors D Han, J Huang, Y Lin, G Shen Journal of econometrics 230 (2), 416-431, 2022 | 9 | 2022 |
| Nonparametric Estimation of Non-Crossing Quantile Regression Process with Deep ReQU Neural Networks G Shen, Y Jiao, Y Lin, JL Horowitz, J Huang Journal of Machine Learning Research 25 (88), 1−75, 2024 | 8* | 2024 |
| Linearized Maximum Rank Correlation Estimation G Shen, K Chen, J Huang, Y Lin Biometrika, 2022 | 7 | 2022 |
| Deep neural newsvendor J Han, M Hu, G Shen arXiv preprint arXiv:2309.13830, 2023 | 4 | 2023 |
| Non-asymptotic excess risk bounds for classification with deep convolutional neural networks G Shen, Y Jiao, Y Lin, J Huang arXiv preprint arXiv:2105.00292, 2021 | 4 | 2021 |
| Conditional stochastic interpolation for generative learning D Huang, J Huang, T Li, G Shen arXiv preprint arXiv:2312.05579, 2023 | 3 | 2023 |
| Differentiable neural networks with repu activation: with applications to score estimation and isotonic regression G Shen, Y Jiao, Y Lin, J Huang arXiv preprint arXiv:2305.00608, 2023 | 3 | 2023 |
| Nonparametric quantile regression: Non-crossing constraints and conformal prediction W Tang, G Shen, Y Lin, J Huang arXiv preprint arXiv:2210.10161, 2022 | 3 | 2022 |
| Exploring the Complexity of Deep Neural Networks through Functional Equivalence G Shen International Conference on Machine Learning 2024, 2024 | 2* | 2024 |
| Wasserstein Generative Regression S Song, T Wang, G Shen, Y Lin, J Huang arXiv preprint arXiv:2306.15163, 2023 | 1 | 2023 |
| Nonparametric inference on smoothed quantile regression process M Hao, Y Lin, G Shen, W Su Computational Statistics & Data Analysis 179, 107645, 2023 | | 2023 |
| Efficient fused learning for distributed imbalanced data J Zhou, G Shen, X Chen, Y Lin Communications in Statistics-Theory and Methods 51 (5), 1306-1317, 2022 | | 2022 |
| From Linear Model to Nonparametric Regression: Estimation, Post-selection Inference and Deep Neural Regression G Shen PQDT-Global, 2022 | | 2022 |
| Supplementary to “Approximation with CNNs in Sobolev Space: with Applications to Classification G Shen, Y Jiao, Y Lin, J Huang | | |
| An Error Analysis of Deep Density-Ratio Estimation with Bregman Divergence S Zheng, G SHEN, Y Jiao, Y Lin, J Huang | | |
