| Wasserstein generative adversarial uncertainty quantification in physics-informed neural networks Y Gao, MK Ng Journal of Computational Physics 463, 111270, 2022 | 18 | 2022 |
| Gradient Descent Finds the Global Optima of Two-Layer Physics-Informed Neural Networks Y Gao, Y Gu, M Ng The 40th International Conference on Machine Learning (ICML 2023), 2023 | 6 | 2023 |
| SVD-PINNs: Transfer Learning of Physics-Informed Neural Networks via Singular Value Decomposition Y Gao, KC Cheung, MK Ng 2022 IEEE Symposium Series on Computational Intelligence (SSCI), 2022 | 6 | 2022 |
| Approximate secular equations for the cubic regularization subproblem Y Gao, MC Yue, M Ng Advances in Neural Information Processing Systems (NeurIPS) 35, 14250-14260, 2022 | 2 | 2022 |
| Approximating Probability Distributions by Using Wasserstein Generative Adversarial Networks Y Gao, MK Ng, M Zhou SIAM Journal on Mathematics of Data Science 5 (4), 949-976, 2023 | 1 | 2023 |
| HessianFR: An Efficient Hessian-based Follow-the-Ridge Algorithm for Minimax Optimization Y Gao, H Liu, MK Ng, M Zhou arXiv preprint arXiv:2205.11030, 2022 | 1 | 2022 |
| Blind Deconvolution for Multiple Observed Images with Missing Values Y Gao, X Lin, MK Ng Pacific Journal of Optimization 19, 69-96, 2023 | | 2023 |
| A Momentum Accelerated Adaptive Cubic Regularization Method for Nonconvex Optimization Y Gao, MK Ng arXiv preprint arXiv:2210.05987, 2022 | | 2022 |
Michael Kwok-Po NgSIAM Fellow, AAIA Fellow, Dean of Science, Chair Professor in Mathematics & in Data Science, HKBUVerified email at hkbu.edu.hk
