Junyu Zhang
Cited by
Cited by
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), 1-12, 2018
On lower iteration complexity bounds for the saddle point problems
J Zhang, M Hong, S Zhang
arXiv preprint arXiv:1912.07481, 2019
A stochastic composite gradient method with incremental variance reduction
J Zhang, L Xiao
arXiv preprint arXiv:1906.10186, 2019
Primal-Dual Optimization Algorithms over Riemannian Manifolds: an Iteration Complexity Analysis
J Zhang, S Ma, S Zhang
Mathematical Programming. 184, 445–490, 2019
A Cubic Regularized Newton's Method over Riemannian Manifolds
J Zhang, S Zhang
arXiv preprint arXiv:1805.05565, 2018
MultiLevel Composite Stochastic Optimization via Nested Variance Reduction
J Zhang, L Xiao
SIAM Journal on Optimization 31 (2), 1131-1157, 2021
A composite randomized incremental gradient method
J Zhang, L Xiao
International Conference on Machine Learning, 7454-7462, 2019
Variational policy gradient method for reinforcement learning with general utilities
J Zhang, A Koppel, AS Bedi, C Szepesvari, M Wang
arXiv preprint arXiv:2007.02151, 2020
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
Adaptive stochastic variance reduction for subsampled Newton method with cubic regularization
J Zhang, L Xiao, S Zhang
arXiv preprint arXiv:1811.11637, 2018
Stochastic variance-reduced prox-linear algorithms for nonconvex composite optimization
J Zhang, L Xiao
arXiv preprint arXiv:2004.04357, 2020
Subspace methods with local refinements for eigenvalue computation using low-rank tensor-train format
J Zhang, Z Wen, Y Zhang
Journal of Scientific Computing 70 (2), 478-499, 2017
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
Cautious reinforcement learning via distributional risk in the dual domain
J Zhang, AS Bedi, M Wang, A Koppel
arXiv preprint arXiv:2002.12475, 2020
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
On the Divergence of Decentralized Non-Convex Optimization
M Hong, S Zeng, J Zhang, H Sun
arXiv preprint arXiv:2006.11662, 2020
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
Cubic Regularized Newton Method for Saddle Point Models: a Global and Local Convergence Analysis
K Huang, J Zhang, S Zhang
arXiv preprint arXiv:2008.09919, 2020
On the Convergence and Sample Efficiency of Variance-Reduced Policy Gradient Method
J Zhang, C Ni, Z Yu, C Szepesvari, M Wang
arXiv preprint arXiv:2102.08607, 2021
First-Order Algorithms Without Lipschitz Gradient: A Sequential Local Optimization Approach
J Zhang, M Hong
arXiv preprint arXiv:2010.03194, 2020
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