| On the linear convergence of the ADMM in decentralized consensus optimization W Shi, Q Ling, K Yuan, G Wu, W Yin IEEE Transactions on Signal Processing 62 (7), 1750-1761, 2014 | 793 | 2014 |
| On the convergence of decentralized gradient descent K Yuan, Q Ling, W Yin SIAM Journal on Optimization 26 (3), 1835-1854, 2016 | 609 | 2016 |
| Exact diffusion for distributed optimization and learning—Part I: Algorithm development K Yuan, B Ying, X Zhao, AH Sayed IEEE Transactions on Signal Processing 67 (3), 708-723, 2019 | 179 | 2019 |
| Decentralized consensus optimization with asynchrony and delays T Wu, K Yuan, Q Ling, W Yin, AH Sayed IEEE Transactions on Signal and Information Processing over Networks 4 (2 …, 2017 | 131 | 2017 |
| Variance-reduced stochastic learning by networked agents under random reshuffling K Yuan, B Ying, J Liu, AH Sayed IEEE Transactions on Signal Processing 67 (2), 351-366, 2019 | 88* | 2019 |
| Exact diffusion for distributed optimization and learning—Part II: Convergence analysis K Yuan, B Ying, X Zhao, AH Sayed IEEE Transactions on Signal Processing 67 (3), 724-739, 2019 | 82 | 2019 |
| Decentralized proximal gradient algorithms with linear convergence rates SA Alghunaim, EK Ryu, K Yuan, AH Sayed IEEE Transactions on Automatic Control 66 (6), 2787-2794, 2020 | 81 | 2020 |
| A linearly convergent proximal gradient algorithm for decentralized optimization S Alghunaim, K Yuan, AH Sayed Advances in Neural Information Processing Systems 32, 2019 | 65 | 2019 |
| Walkman: A communication-efficient random-walk algorithm for decentralized optimization X Mao, K Yuan, Y Hu, Y Gu, AH Sayed, W Yin IEEE Transactions on Signal Processing 68, 2513-2528, 2020 | 50 | 2020 |
| Supervised learning under distributed features B Ying, K Yuan, AH Sayed IEEE Transactions on Signal Processing 67 (4), 977-992, 2018 | 48 | 2018 |
| On the influence of momentum acceleration on online learning K Yuan, B Ying, AH Sayed The Journal of Machine Learning Research 17 (1), 6602-6667, 2016 | 44 | 2016 |
| On the Influence of Bias-Correction on Distributed Stochastic Optimization K Yuan, SA Alghunaim, B Ying, AH Sayed IEEE Transactions on Signal Processing 68, 4352 - 4367, 2020 | 40 | 2020 |
| Exponential Graph is Provably Efficient for Decentralized Deep Training B Ying*, K Yuan*, Y Chen*, H Hu, P Pan, W Yin NeurIPS 2021 - 35th Conference on Neural Information Processing Systems, 2021 | 37 | 2021 |
| Stochastic learning under random reshuffling with constant step-sizes B Ying, K Yuan, S Vlaski, AH Sayed IEEE Transactions on Signal Processing 67 (2), 474-489, 2018 | 33 | 2018 |
| Stochastic gradient descent with finite samples sizes K Yuan, B Ying, S Vlaski, AH Sayed 2016 IEEE 26th International Workshop on Machine Learning for Signal …, 2016 | 33 | 2016 |
| Multiagent fully decentralized value function learning with linear convergence rates L Cassano, K Yuan, AH Sayed IEEE Transactions on Automatic Control 66 (4), 1497-1512, 2020 | 29 | 2020 |
| A proximal diffusion strategy for multiagent optimization with sparse affine constraints SA Alghunaim, K Yuan, AH Sayed IEEE Transactions on Automatic Control 65 (11), 4554-4567, 2019 | 25 | 2019 |
| DecentLaM: Decentralized Momentum SGD for Large-batch Deep Training K Yuan*, Y Chen*, X Huang*, Y Zhang, P Pan, Y Xu, W Yin ICCV 2021 - International Conference on Computer Vision, 2021 | 24 | 2021 |
| Removing data heterogeneity influence enhances network topology dependence of decentralized sgd K Yuan, SA Alghunaim, X Huang arXiv preprint arXiv:2105.08023, 2021 | 23 | 2021 |
| Can Primal Methods Outperform Primal-dual Methods in Decentralized Dynamic Optimization? K Yuan, W Xu, Q Ling IEEE Transactions on Signal Processing 68, 4466 - 4480, 2020 | 23 | 2020 |
Ali H SayedDean of Engineering, EPFL, SwitzerlandVerified email at epfl.ch
