| A continuous-time perspective for modeling acceleration in Riemannian optimization F Alimisis, A Orvieto, G Bécigneul, A Lucchi International Conference on Artificial Intelligence and Statistics, 1297-1307, 2020 | 58 | 2020 |
| Momentum improves optimization on Riemannian manifolds F Alimisis, A Orvieto, G Becigneul, A Lucchi International conference on artificial intelligence and statistics, 1351-1359, 2021 | 46* | 2021 |
| Communication-efficient distributed optimization with quantized preconditioners F Alimisis, P Davies, D Alistarh International Conference on Machine Learning, 196-206, 2021 | 21 | 2021 |
| Distributed principal component analysis with limited communication F Alimisis, P Davies, B Vandereycken, D Alistarh Advances in Neural Information Processing Systems 34, 2823-2834, 2021 | 10 | 2021 |
| Geodesic convexity of the symmetric eigenvalue problem and convergence of Riemannian steepest descent F Alimisis, B Vandereycken arXiv preprint arXiv:2209.03480, 2022 | 6 | 2022 |
| Characterization of optimization problems that are solvable iteratively with linear convergence F Alimisis arXiv preprint arXiv:2402.12090, 2024 | | 2024 |
| Gradient-type subspace iteration methods for the symmetric eigenvalue problem F Alimisis, Y Saad, B Vandereycken arXiv preprint arXiv:2306.10379, 2023 | | 2023 |
Aurelien LucchiAssistant Professor, Department of Mathematics and Computer Science, University of Basel在 inf.ethz.ch 的电子邮件经过验证
