| 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 | 708 | 2014 |
| On the convergence of decentralized gradient descent K Yuan, Q Ling, W Yin SIAM Journal on Optimization 26 (3), 1835-1854, 2016 | 505 | 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, 2018 | 149 | 2018 |
| 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 | 110 | 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, 2018 | 77* | 2018 |
| 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, 2018 | 71 | 2018 |
| 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 | 56 | 2020 |
| A linearly convergent proximal gradient algorithm for decentralized optimization S Alghunaim, K Yuan, AH Sayed Advances in Neural Information Processing Systems 32, 2019 | 52 | 2019 |
| Supervised learning under distributed features B Ying, K Yuan, AH Sayed IEEE Transactions on Signal Processing 67 (4), 977-992, 2018 | 38 | 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 | 37 | 2016 |
| 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 | 35 | 2020 |
| 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 | 32 | 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 | 32 | 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 | 25 | 2020 |
| 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 | 25 | 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 | 19 | 2019 |
| Ode analysis of stochastic gradient methods with optimism and anchoring for minimax problems EK Ryu, K Yuan, W Yin arXiv preprint arXiv:1905.10899, 2019 | 18 | 2019 |
| Convergence of variance-reduced stochastic learning under random reshuffling B Ying, K Yuan, AH Sayed arXiv preprint arXiv:1708.01383 2 (3), 6, 2017 | 18 | 2017 |
| A linearized Bregman algorithm for decentralized basis pursuit K Yuan, Q Ling, W Yin, A Ribeiro 21st European Signal Processing Conference (EUSIPCO 2013), 1-5, 2013 | 17 | 2013 |
| On the performance of exact diffusion over adaptive networks K Yuan, SA Alghunaim, B Ying, AH Sayed 2019 IEEE 58th Conference on Decision and Control (CDC), 4898-4903, 2019 | 16 | 2019 |
Ali H SayedDean of Engineering, EPFL, SwitzerlandVerified email at epfl.ch
