| 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 |
| 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 |
| Distributed coupled multiagent stochastic optimization SA Alghunaim, AH Sayed IEEE Transactions on Automatic Control 65 (1), 175-190, 2019 | 36 | 2019 |
| Linear convergence of primal–dual gradient methods and their performance in distributed optimization SA Alghunaim, AH Sayed Automatica 117, 109003, 2020 | 35 | 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 |
| 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 |
| A unified and refined convergence analysis for non-convex decentralized learning SA Alghunaim, K Yuan IEEE Transactions on Signal Processing 70, 3264-3279, 2022 | 16 | 2022 |
| 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 |
| Dual coupled diffusion for distributed optimization with affine constraints SA Alghunaim, K Yuan, AH Sayed 2018 IEEE Conference on Decision and Control (CDC), 829-834, 2018 | 13 | 2018 |
| Decentralized exact coupled optimization SA Alghunaim, K Yuan, AH Sayed 2017 55th Annual Allerton Conference on Communication, Control, and …, 2017 | 9 | 2017 |
| Dual consensus proximal algorithm for multi-agent sharing problems SA Alghunaim, Q Lyu, M Yan, AH Sayed IEEE Transactions on Signal Processing 69, 5568-5579, 2021 | 6 | 2021 |
| A multi-agent primal-dual strategy for composite optimization over distributed features SA Alghunaim, M Yan, AH Sayed 2020 28th European Signal Processing Conference (EUSIPCO), 2095-2099, 2021 | 6 | 2021 |
| Distributed coupled learning over adaptive networks SA Alghunaim, AH Sayed 2018 IEEE International Conference on Acoustics, Speech and Signal …, 2018 | 5 | 2018 |
| Diffusion LMS filter for distributed estimation of systems with stochastic state transition and observation matrices JY Ishihara, SA Alghunaim 2017 American Control Conference (ACC), 5199-5204, 2017 | 5 | 2017 |
| Team policy learning for multi-agent reinforcement learning L Cassano, SA Alghunaim, AH Sayed ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019 | 4 | 2019 |
| On the Performance of Gradient Tracking with Local Updates EDH Nguyen, SA Alghunaim, K Yuan, CA Uribe arXiv preprint arXiv:2210.04757, 2022 | 2 | 2022 |
| Local Exact-Diffusion for Decentralized Optimization and Learning SA Alghunaim arXiv preprint arXiv:2302.00620, 2023 | | 2023 |
| An Enhanced Gradient-Tracking Bound for Distributed Online Stochastic Convex Optimization SA Alghunaim, K Yuan arXiv preprint arXiv:2301.02855, 2023 | | 2023 |
| On the Performance and Linear Convergence of Decentralized Primal-Dual Methods SA Alghunaim University of California, Los Angeles, 2020 | | 2020 |
Ali H SayedDean of Engineering, EPFL, SwitzerlandVerified email at epfl.ch
