Biography

I am a postdoctoral researcher at Université Paris-Saclay and INRIA Saclay, in the TAU Team. My current research focuses on causal structure learning. I obtained my PhD in May 2021 at Université catholique de Louvain, Belgium, under the supervision of Pierre-Antoine Absil and Kyle A. Gallivan. I worked on matrix and tensor completion, graph-based machine learning, and optimization on matrix manifolds.

Publications and Preprints

• 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, 2023

• 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, 2022

• 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
  arXiv

• Alternating 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
  PDF

• Preconditioned conjugate gradient algorithms for graph regularized matrix completion
  Shuyu Dong, P.-A. Absil, and Kyle A. Gallivan
  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