Full list of papers

Journal Paper:

• Zhang, J., Wang, M., Hong, M., & Zhang, S. 2023. Primal-dual first-order methods for affinely constrained multi-block saddle point problems. SIAM Journal on Optimization, Accepted, to appear soon. [paper]

• Huang, K., Zhang, J., & Zhang, S., 2022. Cubic regularized newton method for saddle point models: a global and local convergence analysis. Journal of Scientific Computing, 2022, 91(2): 60. [paper]

• Hong, M., Zeng, S., Zhang, J., & Sun, H., 2022. On the Divergence of Decentralized Non-Convex Optimization. SIAM Journal on Optimization, 2022, 32(4): 2879-2908. [paper]

• Zhang, J., Hong, M. and Zhang, S., 2022. On lower iteration complexity bounds for the saddle point problems. Mathematical Programming. 2022, 194(1-2): 901-935. [paper]

• Zhang, J., & Xiao, L., 2021. Stochastic variance-reduced prox-linear algorithms for nonconvex composite optimization. Mathematical Programming. 2021, 1-43. [paper]

• Davis, D., Drusvyatskiy, D., Xiao, L. and Zhang, J., 2021. From Low Probability to High Confidence in Stochastic Convex Optimization. Journal of Machine Learning Research, 22, pp.49-1. [paper]

• Zhang, J., Bedi, A. S., Wang, M., & Koppel, A., 2021. Cautious Reinforcement Learning via Distributional Risk in the Dual Domain. IEEE Journal on Selected Areas in Information Theory. [paper]

• Zhang, J. and Xiao, L., 2021. Adaptive stochastic variance reduction for subsampled Newton method with cubic regularization. Informs Journal on Optimization. To appear. [paper]

• Zhang, J. and Xiao, L., 2021. MultiLevel Composite Stochastic Optimization via Nested Variance Reduction. SIAM Journal on Optimization, 31(2), pp.1131-1157. [paper]

• Zhang, J., Ma, S. and Zhang, S., 2020. Primal-dual optimization algorithms over Riemannian manifolds: an iteration complexity analysis. Mathematical Programming, 184(1), pp.445-490. [paper]

• Zhang, J., Liu, H., Wen, Z. and Zhang, S., 2018. A sparse completely positive relaxation of the modularity maximization for community detection. SIAM Journal on Scientific Computing, 40(5), pp.A3091-A3120. [paper]

• Causey, J. L., Zhang, J., Ma, S., Jiang, B., Qualls, J. A., Politte, D. G., ... & Huang, X. (2018). Highly accurate model for prediction of lung nodule malignancy with CT scans. Scientific reports, 8(1), 1-12. [paper] (Equal contribution 1st author).

• Zhang, J., Wen, Z., & Zhang, Y., 2017. Subspace methods with local refinements for eigenvalue computation using low-rank tensor-train format. Journal of Scientific Computing, 70(2), 478-499. [paper]

Conference Proceedings:

• Chen, F., Zhang, J., Wen Z., 2022, A Near-Optimal Primal-Dual Method for Off-Policy Learning in CMDP. Advances in Neural Information Processing Systems (NeurIPS). [paper]

• Zhang, J., Bedi, A. S., Wang, M., & Koppel, A. 2022. MARL with General Utilities via Decentralized Shadow Reward Actor-Critic. AAAI Conference on Artificial Intelligence (AAAI). [paper]

• Zhang, J., Ni, C., Yu, Z., Szepesvari, C., & Wang, M., 2021. On the convergence and sample efficiency of variance-reduced policy gradient method. Advances in Neural Information Processing Systems (NeurIPS). [paper] (selected as spotlight paper)

• Zhang, J., Hong, M., Wang, M. and Zhang, S., 2021, March. Generalization bounds for stochastic saddle point problems. International Conference on Artificial Intelligence and Statistics (AISTATS), pp. 568-576. PMLR. [paper]

• Zhang, J., Koppel, A., Bedi, A.S., Szepesvari, C. and Wang, M., 2020. Variational policy gradient method for reinforcement learning with general utilities. Advances in Neural Information Processing Systems (NeurIPS), 33, pp.4572-4583. [paper]  (selected as spotlight paper)

• Wang, L., Wu, W., Zhang, J., Liu, H., Bosilca, G., Herlihy, M., & Fonseca, R. (2020, June). FFT-based Gradient Sparsification for the Distributed Training of Deep Neural Networks. International Symposium on High-Performance Parallel and Distributed Computing, pp. 113-124. [paper]

• Zhang, J. and Xiao, L., 2019, May. A composite randomized incremental gradient method. International Conference on Machine Learning (ICML), pp. 7454-7462, PMLR. [paper]

• Zhang, J. and Xiao, L., 2019. A stochastic composite gradient method with incremental variance reduction. Advances in Neural Information Processing Systems (NeurIPS), 32, pp.9078-9088. [paper]

Under Review: 

• Zhu, Z., Chen, F., Zhang, J., & Wen, Z. (2022). A Unified Primal-Dual Algorithm Framework for Inequality Constrained Problems. arXiv preprint arXiv:2208.14196.

• Ke, Z., Wen, Z., & Zhang, J., 2023. Provably Efficient Gauss-Newton Temporal Difference Learning Method with Function Approximation. arXiv preprint arXiv:2302.13087 [paper]

• Zhang, J., & Hong, M., 2020. First-Order Algorithms Without Lipschitz Gradient: A Sequential Local Optimization Approach. arXiv preprint arXiv:2010.03194. [paper]

Unpublished Technical Notes:

• Zhang, J., & Zhang, S. (2018). A cubic regularized newton's method over Riemannian manifolds. arXiv preprint arXiv:1805.05565. [paper]