| Theoretical Analysis of Adversarial Learning: A Minimax Approach Z Tu, J Zhang, D Tao Advances in Neural Information Processing Systems 32, 2019. (Spotlight), 2019 | 68 | 2019 |
| Generalization Bounds for Convolutional Neural Networks S Lin, J Zhang Shan Lin's MPhil Degree Thesis at USyd (arXiv:1910.01487), 2020 | 33 | 2020 |
| Going Deeper, Generalizing Better: An Information-Theoretic View for Deep Learning J Zhang, T Liu, D Tao IEEE Transactions on Neural Networks and Learning Systems, 2023 | 26* | 2023 |
| An optimal transport analysis on generalization in deep learning J Zhang, T Liu, D Tao IEEE Transactions on Neural Networks and Learning Systems 34 (6), 2842-2853, 2021 | 22* | 2021 |
| On the Rates of Convergence from Surrogate Risk Minimizers to the Bayes Optimal Classifier J Zhang, T Liu, D Tao IEEE Transactions on Neural Networks and Learning Systems, 2021 | 8 | 2021 |
| DiffFlow: A Unified SDE Framework for Score-Based Diffusion Models and Generative Adversarial Networks J Zhang, H Shi, J Yu, E Xie, Z Li arXiv preprint arXiv:2307.02159, 2023 | 2 | 2023 |
| Mean-Field Analysis of Two-Layer Neural Networks: Global Optimality with Linear Convergence Rates J Zhang, X Huang, J Yu arXiv preprint arXiv:2205.09860, 2022 | 2 | 2022 |
| Provably Sample Efficient Reinforcement Learning in Competitive Linear Quadratic Systems J Zhang, Z Yang, Z Zhou, Z Wang Learning for Dynamics and Control, 597-598, 2021 | 2 | 2021 |
