| The expressive power of neural networks: A view from the width Z Lu, H Pu, F Wang, Z Hu, L Wang Advances in neural information processing systems 30, 2017 | 1254 | 2017 |
| Semiparametric proximal causal inference Y Cui, H Pu, X Shi, W Miao, E Tchetgen Tchetgen Journal of the American Statistical Association 119 (546), 1348-1359, 2024 | 97 | 2024 |
| Estimating optimal treatment rules with an instrumental variable: A partial identification learning approach H Pu, B Zhang Journal of the Royal Statistical Society Series B: Statistical Methodology …, 2021 | 36 | 2021 |
| Transfer learning for nonparametric regression: Non-asymptotic minimax analysis and adaptive procedure TT Cai, H Pu arXiv preprint arXiv:2401.12272, 2022 | 20 | 2022 |
| Transfer learning for functional mean estimation: Phase transition and adaptive algorithms TT Cai, D Kim, H Pu The Annals of Statistics 52 (2), 654-678, 2024 | 5 | 2024 |
| Stochastic continuum-armed bandits with additive models: Minimax regrets and adaptive algorithm TT Cai, H Pu The Annals of Statistics 50 (4), 2179-2204, 2022 | 5 | 2022 |
| Discussion of Cui and Tchetgen Tchetgen (2020) and Qiu et al.(2020) B Zhang, H Pu Journal of the American Statistical Association 116 (533), 196-199, 2021 | 5 | 2021 |
| Supplement to “Stochastic continuum-armed bandits with additive models: Minimax regrets and adaptive algorithm.” TT Cai, H Pu | 2 | 2022 |
| Supplement to “Transfer learning for functional mean estimation: Phase transition and adaptive algorithms.” TT CAI, D KIM, H PU | 1 | 2024 |
| Topics in Statistical Machine Learning H Pu University of Pennsylvania, 2022 | | 2022 |
