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ORCIDJiawei Zhang 0007
Person information
- affiliation: Chinese University of Hong Kong, School of Science and Engineering, Shenzhen, China
- affiliation: Massachusetts Institute of Technology, Department of Electrical Engineering and Computer Science, Cambridge, MA, USA
Other persons with the same name
- Jiawei Zhang (aka: Jia-Wei Zhang, Jia Wei Zhang) — disambiguation page
- Jiawei Zhang 0001
![[0000-0002-2111-7617]](https://dblp.org/img/orcid-mark.12x12.png "0000-0002-2111-7617")
— University of California, Davis, Department of Computer Science, IFM Lab, CA, USA (and 2 more) - Jiawei Zhang 0002 — SenseTime Research, Shenzhen, China (and 1 more)
- Jiawei Zhang 0003 — Purdue University, West Lafayette, IN, USA
- Jiawei Zhang 0004 — Beijing University of Posts and Telecommunication, State Key Laboratory of Information Photonics and Optical Communications, China
- Jiawei Zhang 0006 — New York University, Department of Information, Operations and Management Sciences, Stern School of Business, NY, USA (and 1 more)
- Jiawei Zhang 0008 — East China Jiaotong University, China
- Jia-Wei Zhang 0009 (aka: Jiawei Zhang 0009) — Guangxi University, School of Business Administration, Nanning, China
- Jia-Wei Zhang 0010 (aka: Jiawei Zhang 0010) — Northeast Forestry University, Electromechanical Engineering Academy, Harbin, China
- Jiawei Zhang 0011 — Xidian University, School of Cyber Engineering, Xi'an, China
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2020 – today
- 2024
[c14]Wenqiang Pu, Jiawei Zhang, Rui Zhou, Xiao Fu, Mingyi Hong:
A Smoothed Bregman Proximal Gradient Algorithm for Decentralized Nonconvex Optimization. ICASSP 2024: 8911-8915- [c13]Kihyun Kim, Jiawei Zhang, Asuman E. Ozdaglar, Pablo A. Parrilo:
A Unified Linear Programming Framework for Offline Reward Learning from Human Demonstrations and Feedback. ICML 2024 - [c12]Jiancong Xiao, Jiawei Zhang, Zhi-Quan Luo, Asuman E. Ozdaglar:
Uniformly Stable Algorithms for Adversarial Training and Beyond. ICML 2024 - [i14]Jiancong Xiao, Jiawei Zhang, Zhi-Quan Luo, Asuman E. Ozdaglar:
Uniformly Stable Algorithms for Adversarial Training and Beyond. CoRR abs/2405.01817 (2024) - [i13]Jiawei Zhang, Yuhong Yang, Jie Ding:
Additive-Effect Assisted Learning. CoRR abs/2405.08235 (2024) - [i12]Kihyun Kim, Jiawei Zhang, Asuman E. Ozdaglar, Pablo A. Parrilo:
A Unified Linear Programming Framework for Offline Reward Learning from Human Demonstrations and Feedback. CoRR abs/2405.12421 (2024) - [i11]Jin Du, Xinhe Zhang, Hao Shen, Xun Xian, Ganghua Wang, Jiawei Zhang, Yuhong Yang, Na Li, Jia Liu, Jie Ding:
Drift to Remember. CoRR abs/2409.13997 (2024) - 2023
- [c11]Enmao Diao, Ganghua Wang, Jiawei Zhang, Yuhong Yang, Jie Ding, Vahid Tarokh:
Pruning Deep Neural Networks from a Sparsity Perspective. ICLR 2023 - [c10]Prashant Khanduri, Ioannis C. Tsaknakis, Yihua Zhang, Jia Liu, Sijia Liu, Jiawei Zhang, Mingyi Hong:
Linearly Constrained Bilevel Optimization: A Smoothed Implicit Gradient Approach. ICML 2023: 16291-16325 - [c9]Asuman E. Ozdaglar, Sarath Pattathil, Jiawei Zhang, Kaiqing Zhang:
Revisiting the Linear-Programming Framework for Offline RL with General Function Approximation. ICML 2023: 26769-26791 - [c8]Cheng Chen, Jiawei Zhang, Jie Ding, Yi Zhou:
Assisted Unsupervised Domain Adaptation. ISIT 2023: 2482-2487 - [i10]Enmao Diao, Ganghua Wang, Jiawei Zhang, Yuhong Yang, Jie Ding, Vahid Tarokh:
Pruning Deep Neural Networks from a Sparsity Perspective. CoRR abs/2302.05601 (2023) - 2022
- [j5]Jiawei Zhang, Zhi-Quan Luo:
A Global Dual Error Bound and Its Application to the Analysis of Linearly Constrained Nonconvex Optimization. SIAM J. Optim. 32(3): 2319-2346 (2022) - [j4]Jiawei Zhang, Songyang Ge, Tsung-Hui Chang, Zhi-Quan Luo:
Decentralized Non-Convex Learning With Linearly Coupled Constraints: Algorithm Designs and Application to Vertical Learning Problem. IEEE Trans. Signal Process. 70: 3312-3327 (2022) - [j3]Xinran Wang, Jiawei Zhang, Mingyi Hong, Yuhong Yang, Jie Ding:
Parallel Assisted Learning. IEEE Trans. Signal Process. 70: 5848-5858 (2022) - [c7]Asuman E. Ozdaglar, Sarath Pattathil, Jiawei Zhang, Kaiqing Zhang:
What is a Good Metric to Study Generalization of Minimax Learners? NeurIPS 2022 - [i9]Asuman E. Ozdaglar, Sarath Pattathil, Jiawei Zhang, Kaiqing Zhang:
What is a Good Metric to Study Generalization of Minimax Learners? CoRR abs/2206.04502 (2022) - [i8]Jiawei Zhang, Yushun Zhang, Mingyi Hong, Ruoyu Sun, Zhi-Quan Luo:
When Expressivity Meets Trainability: Fewer than n Neurons Can Work. CoRR abs/2210.12001 (2022) - [i7]Asuman E. Ozdaglar, Sarath Pattathil, Jiawei Zhang, Kaiqing Zhang:
Revisiting the Linear-Programming Framework for Offline RL with General Function Approximation. CoRR abs/2212.13861 (2022) - 2021
- [j2]Zhiguo Wang, Jiawei Zhang, Tsung-Hui Chang, Jian Li, Zhi-Quan Luo:
Distributed Stochastic Consensus Optimization With Momentum for Nonconvex Nonsmooth Problems. IEEE Trans. Signal Process. 69: 4486-4501 (2021) - [c6]Congliang Chen, Jiawei Zhang, Li Shen, Peilin Zhao, Zhi-Quan Luo:
Communication Efficient Primal-Dual Algorithm for Nonconvex Nonsmooth Distributed Optimization. AISTATS 2021: 1594-1602 - [c5]Jiawei Zhang, Yushun Zhang, Mingyi Hong, Ruoyu Sun, Zhi-Quan Luo:
When Expressivity Meets Trainability: Fewer than $n$ Neurons Can Work. NeurIPS 2021: 9167-9180 - [i6]Jiawei Zhang, Songyang Ge, Tsung-Hui Chang, Zhi-Quan Luo:
Decentralized Non-Convex Learning with Linearly Coupled Constraints. CoRR abs/2103.05378 (2021) - [i5]Jiawei Zhang, Jie Ding, Yuhong Yang:
Targeted Cross-Validation. CoRR abs/2109.06949 (2021) - 2020
- [j1]Jiawei Zhang, Zhi-Quan Luo:
A Proximal Alternating Direction Method of Multiplier for Linearly Constrained Nonconvex Minimization. SIAM J. Optim. 30(3): 2272-2302 (2020) - [c4]Jiawei Zhang, Songyang Ge, Tsung-Hui Chang, Zhi-Quan Luo:
A Proximal Dual Consensus Method for Linearly Coupled Multi-Agent Non-Convex Optimization. ICASSP 2020: 5740-5744 - [c3]Linning Xu, Yijue Dai, Jiawei Zhang, Ceyao Zhang, Feng Yin:
Exact O(N2) Hyper-Parameter Optimization for Gaussian Process Regression. MLSP 2020: 1-6 - [c2]Jiawei Zhang, Peijun Xiao, Ruoyu Sun, Zhi-Quan Luo:
A Single-Loop Smoothed Gradient Descent-Ascent Algorithm for Nonconvex-Concave Min-Max Problems. NeurIPS 2020 - [i4]Jiawei Zhang, Peijun Xiao, Ruoyu Sun, Zhi-Quan Luo:
A Single-Loop Smoothed Gradient Descent-Ascent Algorithm for Nonconvex-Concave Min-Max Problems. CoRR abs/2010.15768 (2020) - [i3]Zhiguo Wang, Jiawei Zhang, Tsung-Hui Chang, Jian Li, Zhi-Quan Luo:
Distributed Stochastic Consensus Optimization with Momentum for Nonconvex Nonsmooth Problems. CoRR abs/2011.05082 (2020)
2010 – 2019
- 2019
- [c1]Yue Xu, Feng Yin, Jiawei Zhang, Wenjun Xu, Shuguang Cui, Zhi-Quan Luo:
Scalable Gaussian Process Using Inexact Admm for Big Data. ICASSP 2019: 7495-7499 - [i2]Linning Xu, Feng Yin, Jiawei Zhang, Zhi-Quan Luo, Shuguang Cui:
A General O(n2) Hyper-Parameter Optimization for Gaussian Process Regression with Cross-Validation and Non-linearly Constrained ADMM. CoRR abs/1906.02387 (2019) - [i1]Jiawei Zhang, Jie Ding, Yuhong Yang:
A Binary Regression Adaptive Goodness-of-fit Test (BAGofT). CoRR abs/1911.03063 (2019)
Coauthor Index
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