| Multitask learning over graphs: An approach for distributed, streaming machine learning R Nassif, S Vlaski, C Richard, J Chen, AH Sayed IEEE Signal Processing Magazine 37 (3), 14-25, 2020 | 73 | 2020 |
| Distributed learning in non-convex environments—Part I: Agreement at a linear rate S Vlaski, AH Sayed IEEE Transactions on Signal Processing 69, 1242-1256, 2021 | 57 | 2021 |
| Distributed learning in non-convex environments—Part II: Polynomial escape from saddle-points S Vlaski, AH Sayed IEEE Transactions on Signal Processing 69, 1257-1270, 2021 | 47 | 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 | 39* | 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 |
| Online graph learning from sequential data S Vlaski, HP Maretić, R Nassif, P Frossard, AH Sayed 2018 IEEE Data Science Workshop (DSW), 190-194, 2018 | 28 | 2018 |
| A blind Adaptive Stimulation Artifact Rejection (ASAR) engine for closed-loop implantable neuromodulation systems S Basir-Kazeruni, S Vlaski, H Salami, AH Sayed, D Marković 2017 8th International IEEE/EMBS Conference on Neural Engineering (NER), 186-189, 2017 | 23 | 2017 |
| Optimal importance sampling for federated learning E Rizk, S Vlaski, AH Sayed ICASSP 2021-2021 IEEE International Conference on Acoustics, Speech and …, 2021 | 21 | 2021 |
| Learning over multitask graphs—Part I: Stability analysis R Nassif, S Vlaski, C Richard, AH Sayed IEEE Open Journal of Signal Processing 1, 28-45, 2020 | 21 | 2020 |
| Federated learning under importance sampling E Rizk, S Vlaski, AH Sayed IEEE Transactions on Signal Processing 70, 5381-5396, 2022 | 20 | 2022 |
| Adaptation and Learning Over Networks Under Subspace Constraints—Part I: Stability Analysis R Nassif, S Vlaski, AH Sayed IEEE Transactions on Signal Processing 68, 1346-1360, 2020 | 20 | 2020 |
| A regularization framework for learning over multitask graphs R Nassif, S Vlaski, C Richard, AH Sayed IEEE Signal Processing Letters 26 (2), 297-301, 2018 | 19 | 2018 |
| Diffusion stochastic optimization with non-smooth regularizers S Vlaski, L Vandenberghe, AH Sayed 2016 IEEE International Conference on Acoustics, Speech and Signal …, 2016 | 18 | 2016 |
| Dif-MAML: Decentralized multi-agent meta-learning M Kayaalp, S Vlaski, AH Sayed IEEE Open Journal of Signal Processing 3, 71-93, 2022 | 17 | 2022 |
| Second-Order Guarantees of Stochastic Gradient Descent in Nonconvex Optimization S Vlaski, AH Sayed IEEE Transactions on Automatic Control 67 (12), 6489-6504, 2021 | 16 | 2021 |
| Adaptation and learning over networks under subspace constraints—Part II: Performance analysis R Nassif, S Vlaski, AH Sayed IEEE Transactions on Signal Processing 68, 2948-2962, 2020 | 14 | 2020 |
| Distributed inference over networks under subspace constraints R Nassif, S Vlaski, AH Sayed ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and …, 2019 | 12 | 2019 |
| Proximal diffusion for stochastic costs with non-differentiable regularizers S Vlaski, AH Sayed 2015 IEEE International Conference on Acoustics, Speech and Signal …, 2015 | 12 | 2015 |
| Learning over multitask graphs—Part II: Performance analysis R Nassif, S Vlaski, C Richard, AH Sayed IEEE Open Journal of Signal Processing 1, 46-63, 2020 | 11 | 2020 |
| On the performance of random reshuffling in stochastic learning B Ying, K Yuan, S Vlaski, AH Sayed 2017 Information Theory and Applications Workshop (ITA), 1-5, 2017 | 11 | 2017 |
Ali H SayedDean of Engineering, EPFL, SwitzerlandVerified email at epfl.ch
