Publications

See also Google Scholar and Semantic Scholar.

Preprints

1. Sparse factorization of the square all-ones matrix of arbitrary order
   X. Jiang, E. D. H. Nguyen, C. A. Uribe, and B. Ying
   Preprint, 2024. [arXiv]

2. On graphs with finite-time consensus and their use in gradient tracking
   E. D. H. Nguyen, X. Jiang, B. Ying, and C. A. Uribe
   Preprint, 2023. [arXiv]

3. Almost-sure convergence of iterates and multipliers in stochastic sequential quadratic optimization
   F. E. Curtis, X. Jiang, and Q. Wang
   Preprint, 2023. [arXiv]

4. A globally convergent difference-of-convex algorithmic framework and application to log-determinant optimization problems
   C. Yao, and X. Jiang
   Preprint, 2023. [arXiv]

5. Chordal-GCN: Exploiting sparsity in training large-scale graph convolutional networks
   X. Jiang, K. Cheng, S. Jiang, and Y. Sun
   Preprint, 2019.

Papers

1. Inexact proximal splitting methods for Euclidean distance matrix optimization
   X. Jiang, and C. Yao
   In 2024 INFORMS Optimization Society (IOS) Conference, 2024. [conference website] [author's longer version]

2. Measuring task similarity and its implication in fine-tuning graph neural networks
   R. Huang, J. Xu, X. Jiang, C. Pan, Z. Yang, C. Wang, and Y. Yang
   In Proceedings of the 38th Conference on Artificial Intelligence (AAAI), 2024. [code]

3. Better with less: A data-active perspective on pre-training graph neural networks
   J. Xu, R. Huang, X. Jiang, Y. Cao, C. Yang, C. Wang, and Y. Yang
   In Proceedings of the 37th Conference on Neural Information Processing Systems (NeurIPS), 2023. [arXiv] [code]

4. Minimum-rank positive semidefinite matrix completion with chordal patterns and applications to semidefinite relaxations
   X. Jiang, Y. Sun, M. S. Andersen, and L. Vandenberghe
   Applied Set-Valued Analysis and Optimization. 2023. [preprint]

5. Bregman three-operator splitting methods
   X. Jiang, and L. Vandenberghe
   Journal of Optimization Theory and Applications, 2023. [doi] [preprint] [arXiv]

6. Bregman primal-dual first-order method and application to sparse semidefinite programming
   X. Jiang, and L. Vandenberghe
   Computational Optimization and Applications, 2022. [doi] [preprint]

7. Blindfolded attackers still threatening: Strict black-box adversarial attacks on graphs
   J. Xu, Y. Sun, X. Jiang, Y. Wang, C. Wang, J. Lu, and Y. Yang
   In Proceedings of the 36th Conference on Artificial Intelligence (AAAI), 2022. [doi] [arXiv] [code]

8. Unsupervised adversarially robust representation learning on graphs
   J. Xu, Y. Yang, J. Chen, X. Jiang, C. Wang, J. Lu, and Y. Yang
   In Proceedings of the 36th Conference on Artificial Intelligence (AAAI), 2022. [doi] [arXiv] [code]

9. Consolidating kinematic models to promote coordinated mobile manipulations
   Z. Jiao*, Z. Zhang*, X. Jiang, D. Han, S.-C. Zhu, Y. Zhu, and H. Liu
   In IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2021. [arXiv]

Thesis

• Primal–dual proximal optimization algorithms with Bregman divergences (PhD dissertation)
  X. Jiang, Department of Electrical and Computer Engineering, UCLA, 2022.

• Minimum rank positive semidefinite matrix completion with chordal sparsity pattern (Master thesis)
  X. Jiang, Department of Electrical and Computer Engineering, UCLA, 2017.

Technical reports

1. Power optimization in hybrid localization mechanism for logistics applications
   X. Jiang, and V. O. K. Li
   Technical report for HKU EEE Senior Design Project, 2015.
   Winner of URFP Research Internship Award. [slides] [poster]