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ORCIDZhuqing Liu
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2020 – today
- 2024
[j4]Zhuqing Liu:
Optimisation of digital media technology for film and television animation post-production considering motion capture technology. Int. J. Inf. Commun. Technol. 25(8): 1-13 (2024)- [j3]Xiao-Dong Liu, Zhuqing Liu, Xianbei Huang, Yao-jun Li, Wei Yang:
Study on the suitable high-frequency PIV sample for centrifugal pump visualization based on impeller speed. J. Vis. 27(2): 133-148 (2024) - [c12]Zhuqing Liu, Xin Zhang, Jia Liu, Zhengyuan Zhu, Songtao Lu:
PILOT: An $\mathcal{O}(1/K)$-Convergent Approach for Policy Evaluation with Nonlinear Function Approximation. ICLR 2024 - [c11]Zhihao Dou
, Xin Hu, Haibo Yang, Zhuqing Liu, Minghong Fang:
Adversarial Attacks to Multi-Modal Models. LAMPS@CCS 2024: 35-46 - [i9]Zhihao Dou, Xin Hu, Haibo Yang, Zhuqing Liu, Minghong Fang:
Adversarial Attacks to Multi-Modal Models. CoRR abs/2409.06793 (2024) - 2023
- [c10]Zhuqing Liu, Xin Zhang, Prashant Khanduri, Songtao Lu, Jia Liu:
Prometheus: Taming Sample and Communication Complexities in Constrained Decentralized Stochastic Bilevel Learning. ICML 2023: 22420-22453 - [c9]Peiwen Qiu, Yining Li, Zhuqing Liu, Prashant Khanduri, Jia Liu, Ness B. Shroff, Elizabeth Serena Bentley, Kurt A. Turck:
DIAMOND: Taming Sample and Communication Complexities in Decentralized Bilevel Optimization. INFOCOM 2023: 1-10 - [c8]Zhuqing Liu, Xin Zhang, Songtao Lu, Jia Liu:
PRECISION: Decentralized Constrained Min-Max Learning with Low Communication and Sample Complexities. MobiHoc 2023: 191-200 - [c7]Haibo Yang, Zhuqing Liu, Jia Liu, Chaosheng Dong, Michinari Momma:
Federated Multi-Objective Learning. NeurIPS 2023 - [i8]Zhuqing Liu, Xin Zhang, Songtao Lu, Jia Liu:
PRECISION: Decentralized Constrained Min-Max Learning with Low Communication and Sample Complexities. CoRR abs/2303.02532 (2023) - [i7]Haibo Yang, Zhuqing Liu, Jia Liu, Chaosheng Dong, Michinari Momma:
Federated Multi-Objective Learning. CoRR abs/2310.09866 (2023) - 2022
- [c6]Zhuqing Liu, Xin Zhang, Prashant Khanduri, Songtao Lu, Jia Liu:
INTERACT: achieving low sample and communication complexities in decentralized bilevel learning over networks. MobiHoc 2022: 61-70 - [c5]Xin Zhang, Minghong Fang, Zhuqing Liu, Haibo Yang, Jia Liu, Zhengyuan Zhu:
NET-FLEET: achieving linear convergence speedup for fully decentralized federated learning with heterogeneous data. MobiHoc 2022: 71-80 - [c4]Zhuqing Liu, Xin Zhang, Jia Liu:
SYNTHESIS: a semi-asynchronous path-integrated stochastic gradient method for distributed learning in computing clusters. MobiHoc 2022: 151-160 - [c3]Haibo Yang, Zhuqing Liu, Xin Zhang, Jia Liu:
SAGDA: Achieving $\mathcal{O}(\epsilon^{-2})$ Communication Complexity in Federated Min-Max Learning. NeurIPS 2022 - [i6]Luning Bi, Yunlong Wang, Fan Zhang, Zhuqing Liu, Yong Cai, Emily Zhao:
FD-GATDR: A Federated-Decentralized-Learning Graph Attention Network for Doctor Recommendation Using EHR. CoRR abs/2207.05750 (2022) - [i5]Zhuqing Liu, Xin Zhang, Prashant Khanduri, Songtao Lu, Jia Liu:
INTERACT: Achieving Low Sample and Communication Complexities in Decentralized Bilevel Learning over Networks. CoRR abs/2207.13283 (2022) - [i4]Zhuqing Liu, Xin Zhang, Jia Liu:
SYNTHESIS: A Semi-Asynchronous Path-Integrated Stochastic Gradient Method for Distributed Learning in Computing Clusters. CoRR abs/2208.08425 (2022) - [i3]Xin Zhang, Minghong Fang, Zhuqing Liu, Haibo Yang, Jia Liu, Zhengyuan Zhu:
NET-FLEET: Achieving Linear Convergence Speedup for Fully Decentralized Federated Learning with Heterogeneous Data. CoRR abs/2208.08490 (2022) - [i2]Haibo Yang, Zhuqing Liu, Xin Zhang, Jia Liu:
SAGDA: Achieving O(ε-2) Communication Complexity in Federated Min-Max Learning. CoRR abs/2210.00611 (2022) - [i1]Peiwen Qiu, Yining Li, Zhuqing Liu, Prashant Khanduri, Jia Liu, Ness B. Shroff, Elizabeth Serena Bentley, Kurt A. Turck:
DIAMOND: Taming Sample and Communication Complexities in Decentralized Bilevel Optimization. CoRR abs/2212.02376 (2022) - 2021
- [c2]Xin Zhang, Zhuqing Liu, Jia Liu, Zhengyuan Zhu, Songtao Lu:
Taming Communication and Sample Complexities in Decentralized Policy Evaluation for Cooperative Multi-Agent Reinforcement Learning. NeurIPS 2021: 18825-18838
2010 – 2019
- 2019
- [j2]Xianbei Huang, Wei Yang, Yaojun Li, Baoyun Qiu, Qiang Guo, Zhuqing Liu:
Review on the sensitization of turbulence models to rotation/curvature and the application to rotating machinery. Appl. Math. Comput. 341: 46-69 (2019) - 2017
- [j1]Luning Bi, Ying Zuo, Fei Tao, T. Warren Liao, Zhuqing Liu:
Energy-Aware Material Selection for Product With Multicomponent Under Cloud Environment. J. Comput. Inf. Sci. Eng. 17(3) (2017) - 2016
- [c1]Zhuqing Liu, Haibin Duan, Yijun Yang, Xiaoguang Hu:
Pendulum-like oscillation controller for UAV based on Lévy-flight pigeon-inspired optimization and LQR. SSCI 2016: 1-6
Coauthor Index
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