| On the linearity of large non-linear models: when and why the tangent kernel is constant C Liu, L Zhu, M Belkin Advances in Neural Information Processing Systems 33, 15954-15964, 2020 | 79 | 2020 |
| Loss landscapes and optimization in over-parameterized non-linear systems and neural networks C Liu, L Zhu, M Belkin arXiv:2003.00307, 2021 | 66 | 2021 |
| Accelerating sgd with momentum for over-parameterized learning C Liu, M Belkin arXiv preprint arXiv:1810.13395, 2018 | 59 | 2018 |
| Toward a theory of optimization for over-parameterized systems of non-linear equations: the lessons of deep learning C Liu, L Zhu, M Belkin arXiv preprint arXiv:2003.00307, 2020 | 52 | 2020 |
| Mass: an accelerated stochastic method for over-parametrized learning C Liu, M Belkin arXiv preprint arXiv:1810.13395, 2018 | 15 | 2018 |
| Clustering with Bregman divergences: an asymptotic analysis C Liu, M Belkin Advances in Neural Information Processing Systems 29, 2016 | 14 | 2016 |
| Quadratic models for understanding neural network dynamics L Zhu, C Liu, A Radhakrishnan, M Belkin arXiv preprint arXiv:2205.11787, 2022 | 4 | 2022 |
| Parametrized accelerated methods free of condition number C Liu, M Belkin arXiv preprint arXiv:1802.10235, 2018 | 4 | 2018 |
| Two-Sided Wasserstein Procrustes Analysis. K Jin, C Liu, C Xia IJCAI, 3515-3521, 2021 | 2 | 2021 |
| Transition to Linearity of General Neural Networks with Directed Acyclic Graph Architecture L Zhu, C Liu, M Belkin arXiv preprint arXiv:2205.11786, 2022 | 1 | 2022 |
| Transition to Linearity of Wide Neural Networks is an Emerging Property of Assembling Weak Models C Liu, L Zhu, M Belkin arXiv preprint arXiv:2203.05104, 2022 | 1 | 2022 |
| Otda: a unsupervised optimal transport framework with discriminant analysis for keystroke inference K Jin, C Liu, C Xia 2020 IEEE Conference on Communications and Network Security (CNS), 1-9, 2020 | 1 | 2020 |
| Understanding and Accelerating the Optimization of Modern Machine Learning C Liu The Ohio State University, 2021 | | 2021 |
