| Hybrid Block Successive Approximation for One-Sided Non-Convex Min-max Problems: Algorithms and Applications S Lu, I Tsaknakis, M Hong, Y Chen IEEE Transactions on Signal Processing 68, 3676-3691, 2020 | 214 | 2020 |
| Nonconvex min-max optimization: Applications, challenges, and recent theoretical advances M Razaviyayn, T Huang, S Lu, M Nouiehed, M Sanjabi, M Hong IEEE Signal Processing Magazine 37 (5), 55-66, 2020 | 130 | 2020 |
| Decentralized federated learning for electronic health records S Lu, Y Zhang, Y Wang 2020 54th Annual Conference on Information Sciences and Systems (CISS), 1-5, 2020 | 118* | 2020 |
| Distributed Learning in the Nonconvex World: From batch data to streaming and beyond TH Chang, M Hong, HT Wai, X Zhang, S Lu IEEE Signal Processing Magazine 37 (3), 26-38, 2020 | 104 | 2020 |
| 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 (ICML), 6282-6293, 2020 | 98 | 2020 |
| GNSD: a Gradient-Tracking Based Nonconvex Stochastic Algorithm for Decentralized Optimization S Lu, X Zhang, H Sun, M Hong 2019 IEEE Data Science Workshop (DSW), 315-321, 2019 | 94 | 2019 |
| Improving the Sample and Communication Complexity for Decentralized Non-Convex Optimization: Joint Gradient Estimation and Tracking H Sun, S Lu, M Hong International Conference on Machine Learning (ICML), 9217-9228, 2020 | 85 | 2020 |
| ScaleCom: Scalable Sparsified Gradient Compression for Communication-Efficient Distributed Training CY Chen, J Ni, S Lu, X Cui, PY Chen, X Sun, N Wang, S Venkataramani, ... Advances in Neural Information Processing Systems (NeurIPS) 33, 2020 | 77 | 2020 |
| Decentralized Policy Gradient Descent Ascent for Safe Multi-Agent Reinforcement Learning S Lu, K Zhang, T Chen, T Basar, L Horesh Proceedings of the AAAI Conference on Artificial Intelligence 35 (10), 8767-8775, 2021 | 73 | 2021 |
| A Nonconvex Splitting Method for Symmetric Nonnegative Matrix Factorization: Convergence Analysis and Optimality S Lu, M Hong, Z Wang IEEE Transactions on Signal Processing 65 (12), 3120–3135, 2017 | 59 | 2017 |
| Federated acoustic modeling for automatic speech recognition X Cui, S Lu, B Kingsbury ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 51 | 2021 |
| Rate-improved inexact augmented Lagrangian method for constrained nonconvex optimization Z Li, PY Chen, S Liu, S Lu, Y Xu International Conference on Artificial Intelligence and Statistics (AISTATS …, 2021 | 51 | 2021 |
| Finding Second-Order Stationary Points Efficiently in Smooth Nonconvex Linearly Constrained Optimization Problems S Lu, M Razaviyayn, B Yang, K Huang, M Hong Advances in Neural Information Processing Systems (NeurIPS) 33, 2020 | 42* | 2020 |
| Block alternating optimization for non-convex min-max problems: algorithms and applications in signal processing and communications S Lu, I Tsaknakis, M Hong ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019 | 42 | 2019 |
| A Stochastic Linearized Augmented Lagrangian Method for Decentralized Bilevel Optimization S Lu, S Zeng, X Cui, M Squillante, L Horesh, B Kingsbury, J Liu, M Hong Advances in Neural Information Processing Systems (NeurIPS), 2022 | 40* | 2022 |
| Understanding latent correlation-based multiview learning and self-supervision: An identifiability perspective Q Lyu, X Fu, W Wang, S Lu International Conference on Learning Representations (ICLR), 2022 | 40 | 2022 |
| Throughput of Underwater Wireless Ad Hoc Networks with Random Access: A Physical Layer Perspective S Lu, Z Wang, Z Wang, S Zhou IEEE Transactions on Wireless Communications 14 (11), 6257–6268, 2015 | 40 | 2015 |
| An Efficient Learning Framework For Federated XGBoost Using Secret Sharing and Distributed Optimization L Xie, J Liu, S Lu, TH Chang, Q Shi ACM Transactions on Intelligent Systems and Technology (TIST) 13 (5), 2022 | 39 | 2022 |
| Decentralized TD Tracking with Linear Function Approximation and its Finite-Time Analysis G Wang, S Lu, G Giannakis, G Tesauro, J Sun Advances in Neural Information Processing Systems (NeurIPS) 33, 2020 | 36 | 2020 |
| PA-GD: On the Convergence of Perturbed Alternating Gradient Descent to Second-Order Stationary Points for Structured Nonconvex Optimization S Lu, M Hong, Z Wang International Conference on Machine Learning (ICML), 4134-4143, 2019 | 33* | 2019 |
Mingyi HongAssociate Professor, University of Minnesota; Amazon Scholar確認したメール アドレス: umn.edu