Junyu Zhang
Orcid: 0000-0003-2194-9664Affiliations:
- National University of Singapore, Industrial Systems Engineering and Management, Singapore
- Princeton University, Electrical and Computer Engineering, NJ, USA (2020-2021)
- University of Minnesota, Department of Industrial and System Engineering, Twin Cities, Minneapolis, MN, USA (PhD 2020)
According to our database1,
Junyu Zhang authored at least 38 papers
between 2017 and 2026.
Collaborative distances:
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Bibliography
2026
Automated Reformulation of Robust Optimization via Memory-Augmented Large Language Models.
CoRR, May, 2026
Shuffling the Stochastic Mirror Descent via Dual Lipschitz Continuity and Kernel Conditioning.
CoRR, March, 2026
A New Kernel Regularity Condition for Distributed Mirror Descent: Broader Coverage and Simpler Analysis.
CoRR, March, 2026
2025
2024
General Procedure to Provide High-Probability Guarantees for Stochastic Saddle Point Problems.
J. Sci. Comput., July, 2024
IEEE Trans. Mob. Comput., May, 2024
SIAM J. Optim., 2024
On the Sample Complexity and Metastability of Heavy-tailed Policy Search in Continuous Control.
J. Mach. Learn. Res., 2024
First-Order Algorithms Without Lipschitz Gradient: A Sequential Local Optimization Approach.
INFORMS J. Optim., 2024
An Improved Finite-time Analysis of Temporal Difference Learning with Deep Neural Networks.
Proceedings of the Forty-first International Conference on Machine Learning, 2024
2023
J. Sci. Comput., November, 2023
Primal-Dual First-Order Methods for Affinely Constrained Multi-block Saddle Point Problems.
SIAM J. Optim., June, 2023
Provably Efficient Gauss-Newton Temporal Difference Learning Method with Function Approximation.
CoRR, 2023
2022
SIAM J. Optim., December, 2022
Adaptive Stochastic Variance Reduction for Subsampled Newton Method with Cubic Regularization.
INFORMS J. Optim., January, 2022
Stochastic variance-reduced prox-linear algorithms for nonconvex composite optimization.
Math. Program., 2022
Math. Program., 2022
Cubic Regularized Newton Method for the Saddle Point Models: A Global and Local Convergence Analysis.
J. Sci. Comput., 2022
Proceedings of the Advances in Neural Information Processing Systems 35: Annual Conference on Neural Information Processing Systems 2022, 2022
Multi-Agent Reinforcement Learning with General Utilities via Decentralized Shadow Reward Actor-Critic.
Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence, 2022
2021
SIAM J. Optim., 2021
IEEE J. Sel. Areas Inf. Theory, 2021
J. Mach. Learn. Res., 2021
Proceedings of the Advances in Neural Information Processing Systems 34: Annual Conference on Neural Information Processing Systems 2021, 2021
Proceedings of the 2021 60th IEEE Conference on Decision and Control (CDC), 2021
Beyond Cumulative Returns via Reinforcement Learning over State-Action Occupancy Measures.
Proceedings of the 2021 American Control Conference, 2021
Proceedings of the 24th International Conference on Artificial Intelligence and Statistics, 2021
2020
Primal-dual optimization algorithms over Riemannian manifolds: an iteration complexity analysis.
Math. Program., 2020
Variational Policy Gradient Method for Reinforcement Learning with General Utilities.
Proceedings of the Advances in Neural Information Processing Systems 33: Annual Conference on Neural Information Processing Systems 2020, 2020
FFT-based Gradient Sparsification for the Distributed Training of Deep Neural Networks.
Proceedings of the HPDC '20: The 29th International Symposium on High-Performance Parallel and Distributed Computing, 2020
2019
Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019
Proceedings of the 36th International Conference on Machine Learning, 2019
2018
A Sparse Completely Positive Relaxation of the Modularity Maximization for Community Detection.
SIAM J. Sci. Comput., 2018
SuperNeurons: FFT-based Gradient Sparsification in the Distributed Training of Deep Neural Networks.
CoRR, 2018
CoRR, 2018
2017
Subspace Methods with Local Refinements for Eigenvalue Computation Using Low-Rank Tensor-Train Format.
J. Sci. Comput., 2017