| Riemannian gradient descent methods for graph-regularized matrix completion S Dong, PA Absil, KA Gallivan Linear Algebra and its Applications 623, 193-235, 2021 | 9 | 2021 |
| New Riemannian preconditioned algorithms for tensor completion via polyadic decomposition S Dong, B Gao, Y Guan, F Glineur SIAM Journal on Matrix Analysis and Applications 43 (2), 840-866, 2022 | 7 | 2022 |
| Alternating minimization algorithms for graph regularized tensor completion Y Guan, S Dong, B Gao, PA Absil, F Glineur arXiv preprint arXiv:2008.12876, 2020 | 7 | 2020 |
| Graph learning for regularized low-rank matrix completion S Dong, PA Absil, KA Gallivan Proc. 23rd Int. Symp. Math. Theory Netw. Syst.(MTNS), 1-8, 2018 | 5 | 2018 |
| On the analysis of optimization with fixed-rank matrices: a quotient geometric view S Dong, B Gao, W Huang, KA Gallivan arXiv preprint arXiv:2203.06765, 2022 | 3 | 2022 |
| Preconditioned conjugate gradient algorithms for graph regularized matrix completion S Dong, PA Absil, KA Gallivan 27th European Symposium on Artificial Neural Networks, Computational …, 2019 | 3 | 2019 |
| From graphs to DAGs: a low-complexity model and a scalable algorithm S Dong, M Sebag Joint European Conference on Machine Learning and Knowledge Discovery in …, 2022 | 2 | 2022 |
| Learning sparse models of diffusive graph signals. S Dong, D Thanou, PA Absil, P Frossard ESANN, 2017 | 1 | 2017 |
| Learning Large Causal Structures from Inverse Covariance Matrix via Matrix Decomposition S Dong, K Uemura, A Fujii, S Chang, Y Koyanagi, K Maruhashi, M Sebag | | 2023 |
| High-Dimensional Causal Discovery: Learning from Inverse Covariance via Independence-based Decomposition S Dong, K Uemura, A Fujii, S Chang, Y Koyanagi, K Maruhashi, M Sebag arXiv preprint arXiv:2211.14221, 2022 | | 2022 |
| Learning sparse models of diffusive graph signals S Dong, D Thanou, PA Absil, P Frossard 25th European Symposium on Artificial Neural Networks, 2017 | | 2017 |
