| Learning to optimize: Training deep neural networks for interference management H Sun, X Chen, Q Shi, M Hong, X Fu, ND Sidiropoulos IEEE Transactions on Signal Processing 66 (20), 5438-5453, 2018 | 1050* | 2018 |
| On the convergence of a class of adam-type algorithms for non-convex optimization X Chen, S Liu, R Sun, M Hong International Conference on Learning Representations, 2018 | 341 | 2018 |
| A deep learning method for online capacity estimation of lithium-ion batteries S Shen, M Sadoughi, X Chen, M Hong, C Hu Journal of Energy Storage 25, 100817, 2019 | 314 | 2019 |
| Understanding Gradient Clipping in Private SGD: A Geometric Perspective X Chen, ZS Wu, M Hong Advances in Neural Information Processing Systems 33, 2020 | 181 | 2020 |
| Zo-adamm: Zeroth-order adaptive momentum method for black-box optimization X Chen, S Liu, K Xu, X Li, X Lin, M Hong, D Cox Advances in Neural Information Processing Systems 32, 2019 | 105 | 2019 |
| Min-Max Optimization without Gradients: Convergence and Applications to Black-Box Evasion and Poisoning Attacks S Liu, S Lu, X Chen, Y Feng, K Xu, A Al-Dujaili, M Hong, UM O’Reilly International Conference on Machine Learning, 6282-6293, 2020 | 86 | 2020 |
| Distributed Training with Heterogeneous Data: Bridging Median-and Mean-Based Algorithms X Chen, T Chen, H Sun, ZS Wu, M Hong Advances in Neural Information Processing Systems 33, 2020 | 71 | 2020 |
| Understanding clipping for federated learning: Convergence and client-level differential privacy X Zhang, X Chen, M Hong, ZS Wu, J Yi International Conference on Machine Learning, ICML 2022, 2022 | 70 | 2022 |
| signSGD via zeroth-order oracle S Liu, PY Chen, X Chen, M Hong International Conference on Learning Representations, 2019 | 68 | 2019 |
| Toward communication efficient adaptive gradient method X Chen, X Li, P Li Proceedings of the 2020 ACM-IMS on Foundations of Data Science Conference …, 2020 | 37 | 2020 |
| Dynamic differential-privacy preserving sgd J Du, S Li, X Chen, S Chen, M Hong arXiv preprint arXiv:2111.00173, 2021 | 29 | 2021 |
| Private stochastic non-convex optimization: Adaptive algorithms and tighter generalization bounds Y Zhou, X Chen, M Hong, ZS Wu, A Banerjee arXiv preprint arXiv:2006.13501, 2020 | 27 | 2020 |
| On the convergence of decentralized adaptive gradient methods X Chen, B Karimi, W Zhao, P Li Asian Conference on Machine Learning, 217-232, 2023 | 15 | 2023 |
| Alternating gradient descent ascent for nonconvex min-max problems in robust learning and GANs S Lu, R Singh, X Chen, Y Chen, M Hong 2019 53rd Asilomar Conference on Signals, Systems, and Computers, 680-684, 2019 | 13* | 2019 |
| Distributed adversarial training to robustify deep neural networks at scale G Zhang, S Lu, Y Zhang, X Chen, PY Chen, Q Fan, L Martie, L Horesh, ... Uncertainty in artificial intelligence, 2353-2363, 2022 | 11 | 2022 |
| Joint transmit beamforming and antenna selection in MIMO systems M Zhao, X Chen, Q Shi, W Xu IEEE wireless communications letters 7 (5), 716-719, 2018 | 7 | 2018 |
| Understanding Adaptivity in Machine Learning Optimization: Theories and Algorithms X Chen University of Minnesota, 2022 | | 2022 |
Mingyi HongAssociate Professor, University of Minnesota; Amazon Scholar在 umn.edu 的电子邮件经过验证
