| Loss landscapes and optimization in over-parameterized non-linear systems and neural networks C Liu, L Zhu, M Belkin Applied and Computational Harmonic Analysis 59, 85-116, 2022 | 224 | 2022 |
| 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 | 161 | 2020 |
| Accelerating sgd with momentum for over-parameterized learning C Liu, M Belkin International Conference on Learning Representations, 2020 | 113* | 2020 |
| 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 7, 2020 | 90 | 2020 |
| Quadratic models for understanding catapult dynamics of neural networks L Zhu, C Liu, A Radhakrishnan, M Belkin The Twelfth International Conference on Learning Representations, 2024 | 19* | 2024 |
| Clustering with Bregman divergences: an asymptotic analysis C Liu, M Belkin Advances in neural information processing systems 29, 2016 | 18 | 2016 |
| Catapults in SGD: spikes in the training loss and their impact on generalization through feature learning L Zhu, C Liu, A Radhakrishnan, M Belkin The Forty-first International Conference on Machine Learning (ICML), 2024 | 11 | 2024 |
| Aiming towards the minimizers: fast convergence of SGD for overparametrized problems C Liu, D Drusvyatskiy, M Belkin, D Davis, YA Ma Conference on Neural Information Processing Systems (NIPS), 2023 | 8 | 2023 |
| Transition to linearity of general neural networks with directed acyclic graph architecture L Zhu, C Liu, M Belkin Advances in neural information processing systems 35, 5363-5375, 2022 | 6 | 2022 |
| Transition to Linearity of Wide Neural Networks is an Emerging Property of Assembling Weak Models C Liu, L Zhu, M Belkin International Conference on Learning Representations, 2022 | 5 | 2022 |
| Two-Sided Wasserstein Procrustes Analysis. K Jin, C Liu, C Xia IJCAI, 3515-3521, 2021 | 5 | 2021 |
| Parametrized accelerated methods free of condition number C Liu, M Belkin arXiv preprint arXiv:1802.10235, 2018 | 5 | 2018 |
| ReLU soothes the NTK condition number and accelerates optimization for wide neural networks C Liu, L Hui arXiv preprint arXiv:2305.08813, 2023 | 4 | 2023 |
| On emergence of clean-priority learning in early stopped neural networks C Liu, A Abedsoltan, M Belkin arXiv preprint arXiv:2306.02533, 2023 | 2 | 2023 |
| 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 |
| On the Predictability of Fine-grained Cellular Network Throughput using Machine Learning Models O Basit, P Dinh, I Khan, ZJ Kong, YC Hu, D Koutsonikolas, M Lee, C Liu IEEE MASS, 2024 | | 2024 |
| SGD batch saturation for training wide neural networks C Liu, D Drusvyatskiy, M Belkin, D Davis, Y Ma NeurIPS Optimization for Machine Learning workshop, 2023 | | 2023 |
| Understanding and Accelerating the Optimization of Modern Machine Learning C Liu The Ohio State University, 2021 | | 2021 |
Mikhail (Misha) BelkinProfessor of Data Science, Halıcıoğlu Data Science Institute, CSE, UCSD, Amazon ScholarVerified email at ucsd.edu
