Preprints:
[P5] Barron space representations for elliptic PDEs with homogeneous boundary conditions, Ziang Chen and Liqiang Huang [ArXiv]
[P4] Randomized coordinate gradient descent almost surely escapes strict saddle points, Ziang Chen, Yingzhou Li, and Zihao Li [ArXiv]
[P3] On the expressive power of subgraph graph neural networks for graphs with bounded cycles, Ziang Chen, Qiao Zhang, and Runzhong Wang [ArXiv]
[P2] Residual connections provably mitigate oversmoothing in graph neural networks, Ziang Chen, Zhengjiang Lin, Shi Chen, Yury Polyanskiy, and Philippe Rigollet [ArXiv]
[P1] Exact and efficient representation of totally anti-symmetric functions, Ziang Chen and Jianfeng Lu [ArXiv]
Refereed Journal Papers:
[J8] Fully discretized Sobolev gradient flow for the Gross-Pitaevskii eigenvalue problem, Ziang Chen, Jianfeng Lu, Yulong Lu, and Xiangxiong Zhang, Mathematics of Computation, to appear [ArXiv]
[J7] One-dimensional tensor network recovery, Ziang Chen, Jianfeng Lu, and Anru R. Zhang, SIAM Journal on Matrix Analysis and Applications, 45(3), 1217 – 1244 (2024) [Journal] [ArXiv]
[J6] On the convergence of Sobolev gradient flow for the Gross-Pitaevskii eigenvalue problem, Ziang Chen, Jianfeng Lu, Yulong Lu, and Xiangxiong Zhang, SIAM Journal on Numerical Analysis, 62(2), 667-691 (2024) [Journal] [ArXiv]
[J5] Representation theorem for multivariable totally symmetric functions, Chongyao Chen, Ziang Chen, and Jianfeng Lu, Communications in Mathematical Sciences, 22(5), 1195-1201 (2024) [Journal] [ArXiv]
[J4] On the global convergence of randomized coordinate gradient descent for nonconvex optimization, Ziang Chen, Yingzhou Li, and Jianfeng Lu, SIAM Journal on Optimization, 33(2), 713-738 (2023) [Journal] [ArXiv]
[J3] A regularity theory for static Schr\”odinger equations on $\mathbb{R}^d$ in spectral Barron spaces, Ziang Chen, Jianfeng Lu, Yulong Lu, and Shengxuan Zhou, SIAM Journal on Mathematical Analysis, 55(1), 557-570 (2023) [Journal] [ArXiv]
[J2] A trust-region method for nonsmooth nonconvex optimization, Ziang Chen, Andre Milzarek, and Zaiwen Wen, Journal of Computational Mathematics, 41(4), 683-716 (2023) [Journal] [ArXiv]
[J1] Tensor ring decomposition: optimization landscape and one-loop convergence of alternating least squares, Ziang Chen, Yingzhou Li, and Jianfeng Lu, SIAM Journal on Matrix Analysis and Applications, 41(3), 1416-1442 (2020) [Journal] [ArXiv]
Refereed Conference Papers:
[C11] Expressive power of graph neural networks for (mixed-integer) quadratic programs, Ziang Chen, Xiaohan Chen, Jialin Liu, Xinshang Wang, and Wotao Yin, International Conference on Machine Learning (ICML) 2025 [ArXiv]
[C10] On designing general and expressive quantum graph neural networks with applications to MILP instance representation, Xinyu Ye, Hao Xiong, Jianhao Huang, Ziang Chen, Jia Wang, and Junchi Yan, International Conference on Learning Representations (ICLR) 2025 [Proceedings]
[C9] Rethinking the capacity of graph neural networks for branching strategy, Ziang Chen, Jialin Liu, Xiaohan Chen, Xinshang Wang, and Wotao Yin, Advances in Neural Information Processing Systems (NeurIPS) 2024 [Proceedings] [ArXiv]
[C8] Mean-field analysis for learning subspace-sparse polynomials with Gaussian input, Ziang Chen and Rong Ge, Advances in Neural Information Processing Systems (NeurIPS) 2024 [Proceedings] [ArXiv]
[C7] Certified machine unlearning via noisy stochastic gradient descent, Eli Chien, Haoyu Wang, Ziang Chen, and Pan Li, Advances in Neural Information Processing Systems (NeurIPS) 2024 [Proceedings] [ArXiv]
[C6] Langevin unlearning: a new perspective of noisy gradient descent for machine unlearning, Eli Chien, Haoyu Wang, Ziang Chen, and Pan Li, Advances in Neural Information Processing Systems (NeurIPS) 2024 (spotlight) [Proceedings] [ArXiv]
[C5] Efficient algorithms for sum-of-minimum optimization, Lisang Ding, Ziang Chen, Xinshang Wang, and Wotao Yin, International Conference on Machine Learning (ICML) 2024 [Proceedings] [ArXiv]
[C4] On representing mixed-integer linear programs by graph neural networks, Ziang Chen, Jialin Liu, Xinshang Wang, Jianfeng Lu, and Wotao Yin, International Conference on Learning Representations (ICLR) 2023 [Proceedings] [ArXiv]
[C3] On representing linear programs by graph neural networks, Ziang Chen, Jialin Liu, Xinshang Wang, Jianfeng Lu, and Wotao Yin, International Conference on Learning Representations (ICLR) 2023 (spotlight) [Proceedings] [ArXiv]
[C2] HeteRSGD: tackling heterogeneous sampling costs via optimal reweighted stochastic gradient descent, Ziang Chen, Jianfeng Lu, Huajie Qian, Xinshang Wang, and Wotao Yin, International Conference on Artificial Intelligence and Statistics (AISTATS) 2023 [Proceedings]
[C1] On the representation of solutions to elliptic PDEs in Barron spaces, Ziang Chen, Jianfeng Lu, and Yulong Lu, Advances in Neural Information Processing Systems (NeurIPS) 2021 (spotlight) [Proceedings] [ArXiv]
Others: