| Highly accurate model for prediction of lung nodule malignancy with CT scans JL Causey, J Zhang, S Ma, B Jiang, JA Qualls, DG Politte, F Prior, ... Scientific reports 8 (1), 9286, 2018 | 257 | 2018 |
| Variational policy gradient method for reinforcement learning with general utilities J Zhang, A Koppel, AS Bedi, C Szepesvari, M Wang Advances in Neural Information Processing Systems 33, 4572--4583, 2020 | 202 | 2020 |
| On lower iteration complexity bounds for the convex concave saddle point problems J Zhang, M Hong, S Zhang Mathematical Programming 194 (1), 901-935, 2022 | 162 | 2022 |
| On the convergence and sample efficiency of variance-reduced policy gradient method J Zhang, C Ni, Z Yu, C Szepesvari, M Wang Advances in Neural Information Processing Systems 34, 2228-2240, 2021 | 104 | 2021 |
| A stochastic composite gradient method with incremental variance reduction J Zhang, L Xiao Advances in Neural Information Processing Systems 32, 2019 | 79 | 2019 |
| Multilevel composite stochastic optimization via nested variance reduction J Zhang, L Xiao SIAM Journal on Optimization 31 (2), 1131-1157, 2021 | 73 | 2021 |
| From low probability to high confidence in stochastic convex optimization D Davis, D Drusvyatskiy, L Xiao, J Zhang Journal of machine learning research 22 (49), 1-38, 2021 | 67 | 2021 |
| Primal-Dual Optimization Algorithms over Riemannian Manifolds: an Iteration Complexity Analysis J Zhang, S Ma, S Zhang Mathematical Programming. 184, 445–490, 2019 | 51 | 2019 |
| Generalization bounds for stochastic saddle point problems J Zhang, M Hong, M Wang, S Zhang International Conference on Artificial Intelligence and Statistics, 568-576, 2021 | 46 | 2021 |
| Cautious Reinforcement Learning via Distributional Risk in the Dual Domain J Zhang, AS Bedi, M Wang, A Koppel IEEE Journal on Selected Areas in Information Theory, 2021 | 44 | 2021 |
| A cubic regularized Newton's method over Riemannian manifolds J Zhang, S Zhang arXiv preprint arXiv:1805.05565, 2018 | 40 | 2018 |
| Cubic regularized newton method for the saddle point models: A global and local convergence analysis K Huang, J Zhang, S Zhang Journal of Scientific Computing 91 (2), 60, 2022 | 39 | 2022 |
| A composite randomized incremental gradient method J Zhang, L Xiao International Conference on Machine Learning, 7454-7462, 2019 | 38 | 2019 |
| Stochastic variance-reduced prox-linear algorithms for nonconvex composite optimization J Zhang, L Xiao Mathematical Programming 195 (1), 649-691, 2022 | 34 | 2022 |
| FFT-based gradient sparsification for the distributed training of deep neural networks L Wang, W Wu, J Zhang, H Liu, G Bosilca, M Herlihy, R Fonseca Proceedings of the 29th International Symposium on High-Performance Parallel …, 2020 | 29 | 2020 |
| Adaptive stochastic variance reduction for subsampled Newton method with cubic regularization J Zhang, L Xiao, S Zhang INFORMS Journal on Optimization 4 (1), 45-64, 2022 | 23 | 2022 |
| First-order algorithms without Lipschitz gradient: A sequential local optimization approach J Zhang, M Hong INFORMS Journal on Optimization 6 (2), 118-136, 2024 | 19 | 2024 |
| On the sample complexity and metastability of heavy-tailed policy search in continuous control AS Bedi, A Parayil, J Zhang, M Wang, A Koppel Journal of Machine Learning Research 25 (39), 1-58, 2024 | 19 | 2024 |
| A sparse completely positive relaxation of the modularity maximization for community detection J Zhang, H Liu, Z Wen, S Zhang SIAM Journal on Scientific Computing 40 (5), A3091-A3120, 2018 | 18 | 2018 |
| A near-optimal primal-dual method for off-policy learning in cmdp F Chen, J Zhang, Z Wen Advances in Neural Information Processing Systems 35, 10521-10532, 2022 | 15 | 2022 |
Mengdi WangProfessor, Princeton AI Lab, CSML&ECE, Princeton UniversityVerified email at princeton.edu
