Biography
I am a postdoctoral researcher at the Laboratory of Signals and Systems (L2S), CentraleSupélec, CNRS, Université Paris-Saclay since October 2024. From 2021 to 2024, I was a postdoc researcher within the TAU Team at INRIA, LISN, Université Paris-Saclay. My research focuses on causal learning, matrix and tensor decomposition and related topics in optimization. I obtained my PhD in May 2021 at Université catholique de Louvain, Belgium, under the supervision of Pierre-Antoine Absil and Kyle A. Gallivan. During my PhD I worked on matrix and tensor completion, graph-based machine learning, and optimization on matrix manifolds.
Publications and Preprints
Learning Measurement Models via Subspace Identification and Clustering
Shuyu Dong, Arthur Tenenhaus, Laurent Le Brusquet, Mohammed Nabil El Korso.
IEEE Statistical Signal Processing Workshop (SSP), 2025, pp. 211-215.DCILP: A distributed approach for large-scale causal structure learning
Shuyu Dong, Michèle Sebag, Kento Uemura, Akito Fujii, Shuang Chang, Yusuke Koyanagi, and Koji Maruhashi. arXiv:2406.10481, 2025. In the 39th Annual AAAI Conference on Artificial Intelligence (AAAI-25).Learning Large Causal Structures from Inverse Covariance Matrix via Matrix Decomposition
Shuyu Dong, Kento Uemura, Akito Fujii, Shuang Chang, Yusuke Koyanagi, Koji Maruhashi, and Michèle Sebag. arXiv:2211.14221, 2024.From graphs to DAGs: a low-complexity model and a scalable algorithm
Shuyu Dong and Michèle Sebag. European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), 2022
arXiv:2204.04644.On the analysis of optimization with fixed-rank matrices: a quotient geometric view
Shuyu Dong, Bin Gao, Wen Huang, and Kyle A. Gallivan. arXiv:2203.06765, 2024.New Riemannian preconditioned algorithms for tensor completion via polyadic decomposition
Shuyu Dong, Bin Gao, Yu Guan, and François Glineur. SIAM Journal on Matrix Analysis and Applications 43 (2) (2022), 840-866.
PDFAlternating minimization algorithms for graph regularized tensor completion
Yu Guan, Shuyu Dong, Bin Gao, P.-A. Absil, and François Glineur. arXiv:2008.12876, 2023.Riemannian gradient descent methods for graph-regularized matrix completion
Shuyu Dong, P.-A. Absil, and Kyle A. Gallivan. Linear Algebra and its Applications 623 (2021), 193-235
PDFPreconditioned conjugate gradient algorithms for graph regularized matrix completion
Shuyu Dong, P.-A. Absil, and Kyle A. Gallivan. The 27th European Symposium on Artificial Neural Networks, Computational Intelligence and Machine Learning (ESANN), 2019.Graph learning for regularized low-rank matrix completion
Shuyu Dong, P.-A. Absil, and Kyle A. Gallivan. 23rd International Symposium on Mathematical Theory of Networks and Systems (MTNS), 2018.Learning sparse models of diffusive graph signals
Shuyu Dong, Dorina Thanou, P.-A. Absil, and Pascal Frossard. 25th European Symposium on Artificial Neural Networks (ESANN), 2017.