| 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 | 636* | 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 | 206 | 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 | 119* | 2019 |
| Understanding Gradient Clipping in Private SGD: A Geometric Perspective X Chen, ZS Wu, M Hong Advances in Neural Information Processing Systems 33, 2020 | 57 | 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 | 50 | 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 | 47* | 2020 |
| signSGD via zeroth-order oracle S Liu, PY Chen, X Chen, M Hong International Conference on Learning Representations, 2018 | 39 | 2018 |
| 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 | 30 | 2020 |
| 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 | 12 | 2020 |
| 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 | 11 | 2020 |
| 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 | 8* | 2019 |
| 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 | 6 | 2018 |
| On the Convergence of Decentralized Adaptive Gradient Methods X Chen, B Karimi, W Zhao, P Li arXiv preprint arXiv:2109.03194, 2021 | 3 | 2021 |
| Dynamic Differential-Privacy Preserving SGD J Du, S Li, M Feng, S Chen arXiv preprint arXiv:2111.00173, 2021 | 2 | 2021 |
| Understanding Clipping for Federated Learning: Convergence and Client-Level Differential Privacy X Zhang, X Chen, M Hong, ZS Wu, J Yi arXiv preprint arXiv:2106.13673, 2021 | | 2021 |
| Distributed Adversarial Training to Robustify Deep Neural Networks at Scale G Zhang, S Lu, S Liu, X Chen, PY Chen, L Martie, L Horesh, M Hong | | 2020 |
Mingyi HongAssociate Professor, University of MinnesotaVerified email at umn.edu
