| Uniform convergence of interpolators: Gaussian width, norm bounds and benign overfitting F Koehler, L Zhou, DJ Sutherland, N Srebro Advances in Neural Information Processing Systems 34, 20657-20668, 2021 | 74 | 2021 |
| On uniform convergence and low-norm interpolation learning L Zhou, DJ Sutherland, N Srebro Advances in Neural Information Processing Systems 33, 6867-6877, 2020 | 41 | 2020 |
| A non-asymptotic moreau envelope theory for high-dimensional generalized linear models L Zhou, F Koehler, P Sur, DJ Sutherland, N Srebro Advances in Neural Information Processing Systems 35, 21286-21299, 2022 | 23 | 2022 |
| Optimistic rates: A unifying theory for interpolation learning and regularization in linear regression L Zhou, F Koehler, DJ Sutherland, N Srebro arXiv preprint arXiv:2112.04470, 2021 | 21 | 2021 |
| An agnostic view on the cost of overfitting in (kernel) ridge regression L Zhou, JB Simon, G Vardi, N Srebro arXiv preprint arXiv:2306.13185, 2023 | 7 | 2023 |
| Optimistic rates: A unifying theory for interpolation learning and regularization in linear regression L Zhou, F Koehler, DJ Sutherland, N Srebro ACM/JMS Journal of Data Science 1 (2), 1-51, 2024 | 6 | 2024 |
| Uniform convergence with square-root lipschitz loss L Zhou, Z Dai, F Koehler, N Srebro Advances in Neural Information Processing Systems 36, 2024 | 1 | 2024 |
| ACM/IMS W Miao, L Liu, Y Li, EJT Tchetgen, Z Geng, L Zhou, F Koehler, ... | | 2024 |
| A Statistical Learning Theory for Models with High Complexity L Zhou The University of Chicago, 2023 | | 2023 |
