Publications

Accepted Paper List

• Junjie Sheng, Xiangfeng Wang, Bo Jin, Wenhao Li, J. Yan, T.-H. Chang, J. Wang and Hongyuan Zha, Learning Structured Communication for Multi-agent Reinforcement Learning. Autonomous Agents and Multi-Agent Systems, accepted 2022.
• Wenhao Li, Xiangfeng Wang, Bo Jin, D. Luo and Hongyuan Zha, Structured Cooperative Reinforcement Learning with Time-varying Composite Action Space. IEEE Transactions on Pattern Analysis and Machine Intelligence, accepted, 2021.

2022

• Junjie Sheng, Shengliang Cai, Haochuan Cui, Wenhao Li, Yun Hua, Bo Jin, W. Zhou, Y. Hu, L. Zhu, Q. Peng, Hongyuan Zha and Xiangfeng Wang, VMAgent: A Practical Virtual Machine Scheduling Platform. IJCAI-Demos, 2022. (The VMAgent Simulator)
• Xiangfeng Wang, J. Ye, X. Yuan, S. Zeng and J. Zhang, Perturbation Techniques for Convergence Analysis of Proximal Gradient Method and Other First-order Algorithms via Variational Analysis. Set-Valued and Variational Analysis, 30, 2022, pp.39-79.
• Wenhao Li, Hongjun Chen, Bo Jin, W. Tan, Hongyuan Zha, Xiangfeng Wang, Multi-Agent Path Finding with Prioritized Communication Learning. ICRA, 2022.
• Tingyu Wu, Wenhao Li, Bo Jin, W. Zhang and Xiangfeng Wang, Weighted Mean-Field Multi-Agent Reinforcement Learning via Reward Attribution Decomposition. DASFAA, 2022.
• Wenhao Li, Bo Jin, Xiangfeng Wang, Junjie Sheng and Hongyuan Zha, Dealing with Non-Stationarity in Multi-Agent Reinforcement Learning via Trust Region Decomposition. ICLR, 2022.
• Junjie Sheng, Yiqiu Hu, W. Zhou, L. Zhu, Bo Jin, J. Wang and Xiangfeng Wang, Learning to Schedule Multi-NUMA Virtual Machines via Reinforcement Learning. Pattern Recognition, 121, 2022, pp.108254.

2021

• C. Ma, Qisen Xu, Xiangfeng Wang, Bo Jin, X. Zhang, Y. Wang and Y. Zhang, Boundary-aware Supervoxel-level Iteratively Refined Interactive 3D Image Segmentation with Multi-agent Reinforcement Learning. IEEE Transactions on Medical Imaging, 40(10), 2021, pp.2563-2574.
• Xiangfeng Wang, J. Yan, Bo Jin and Wenhao Li, Distributed and Parallel ADMM for Structured Nonconvex Optimization Problem. IEEE Transactions on Cybernetics, 51(9), 2021, pp.4540-4552.
• Y. Song, T. Liu, T. Wei, Xiangfeng Wang, Z. Tao and M. Chen, FDA3: Federated Defense Against Adversarial Attacks for Cloud-Based IIoT Applications. IEEE Transactions on Industrial Informatics, 17(11), 2021, pp.7830-7838.
• Yun Hua, Xiangfeng Wang, Bo Jin, Wenhao Li, J. Yan, X. He and Hongyuan Zha, Hyper-Meta Reinforcement Learning with Sparse Reward. KDD, 2021.
• Wenyan Liu, Chuyun Shen, Xiangfeng Wang, Bo Jin, X. Lu, X. Wang, Hongyuan Zha and J. He, Fairness in Trustworthy Machine Learning: A Survey. Journal of Software, 32(5), 2021, pp.1404-1426. (Chinese version)
• Qisen Xu, Qian Wu, Hu Yiqiu, Bo Jin, B. Hu, F. Zhu, Y. Li and Xiangfeng Wang, Semi-supervised Medical Image Segmentation with Confidence Calibration. IJCNN, 2021.
• Wenhao Li, Xiangfeng Wang, Bo Jin, Junjie Sheng, Yun Hua and Hongyuan Zha, Structured Diversification Emergence via Reinforced Organization Control and Hierachical Consensus Learning. AAMAS (oral), 2021.

2020

• Xiangfeng Wang, J. Zhang and W. Zhang, The Distance Between Convex Sets with Minkowski Sum Structure: Application to Collision Detection. Computational Optimization and Applications, 77, 2020, pp.465–490.
• M. Hong, T.-H. Chang, Xiangfeng Wang, M. Razaviyayn, S. Ma and Z.-Q. Luo, A Block Successive Upper Bound Minimization Method of Multipliers for Linearly Constrained Convex Optimization. Mathematics of Operations Research, 45(3), 2020, pp.833-861.
• Xin Li, Xiangfeng Wang, Bo Jin, Wenjie Zhang, J. Wang and Hongyuan Zha, VSB$^2$-Net: Visual-Semantic Bi-Branch Network for Zero-Shot Hashing. ICPR, 2020.
• Junjie Wang, Xiangfeng Wang, Bo Jin, J. Yan, W. Zhang and Hongyuan Zha, Heterogeneous Graph-based Knowledge Transfer for Generalized Zero-shot Learning. ICPR, 2020.
• X. Liao, Wenhao Li, Qisen Xu, Xiangfeng Wang, Bo Jin, X. Zhang, Y. Zhang and Y. Wang, Iteratively-Refined Interactive 3D Medical Image Segmentation with Multi-Agent Reinforcement Learning. CVPR, 2020.
• Xin Li, X. Wen, Bo Jin, Xiangfeng Wang, Junjie Wang and Jinghui Cai, Visual-to-Semantic Hashing for Zero Shot Learning. IJCNN, 2020.

2019

• C. Li, Xiangfeng Wang, W. Dong, J. Yan, Q. Liu and Hongyuan Zha, Active Sample Learning and Feature Selection: A Unified Approach. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(6), 2019, pp.1382-1396.
• C. Li, F. Wei, W. Dong, Q. Liu, Xiangfeng Wang and X. Zhang, Dynamic Structure Embedded Online Multiple-output Regression for Streaming Data. IEEE Transactions on Pattern Analysis and Machine Intelligence, 41(2), 2019, pp.323-336.
• Y. Xie, Xiangfeng Wang, R. Wang and Hongyuan Zha, A Fast Proximal Point Method for Computing Exact Wasserstein Distance. UAI, 2019.
• Wenhao Li, Bo Jin and Xiangfeng Wang, SparseMAAC: Sparse Attention for Multi-agent Reinforcement Learning. DASFAA, 2019.

2018

• H. Yue, Q. Yang, Xiangfeng Wang and X. Yuan, Implementing the ADMM to Big Datasets: A Case Study of LASSO. SIAM Journal on Scientific Computing, 40(5), 2018, pp.A3121-A3156.
• Xiangfeng Wang, W. Zhang, J. Yan, X. Yuan and Hongyuan Zha, On the Flexibility of Block Coordinate Descent for Large-Scale Optimization. Neurocomputing, 272, 2018, pp.471-480.
• H.T. Zhao, S.Y. Sun, Bo Jin. Sequential Fault Diagnosis based on LSTM Neural Network. IEEE Access, 2018.
• Wenjie Zhang, J. Yan, Xiangfeng Wang and Hongyuan Zha, Deep eXtreme Multi-label Learning. ICMR, 2018.

2017

• M. Hong, Xiangfeng Wang, M. Razaviyayn and Z.-Q. Luo, Iteration Complexity Analysis of Block Coordinate Descent Methods. Mathematical Programming Series A, 163, 2017, pp.85-114.
• Bo Jin, Z.L. Jing, H.T. Zhao. Incremental and Decremental Extreme Learning Machine Based on Generalized Inverse. IEEE Access, 2017.
• X. Liu, J. Yan, S. Xiao, Xiangfeng Wang, Hongyuan Zha and S. Chu, On Predictive Patent Valuation: Forecasting Patent Citations and Their Types. AAAI, 2017.

2016

• T.-H. Chang, M. Hong, W.-C. Liao and Xiangfeng Wang, Asynchronous Distributed ADMM for Large-Scale Optimization-Part I: Algorithm and Convergence Analysis. IEEE Transactions on Signal Processing, 64(12), 2016, pp.3118-3130.
• T.-H. Chang, W.-C. Liao, M. Hong and Xiangfeng Wang, Asynchronous Distributed ADMM for Large-Scale Optimization-Part II: Linear Convergence Analysis and Numerical Performances. IEEE Transactions on Signal Processing, 64(12), 2016, pp.3131-3144.
• T.-H. Chang, M. Hong, W.-C. Liao and Xiangfeng Wang, Asynchronous Distributed Alternating Direction Method of Multipliers: Algorithm and Convergence Analysis. ICASSP, 2016.
• D. Hajinezhad, T.-H. Chang, Xiangfeng Wang, Q. Shi and M. Hong, Nonnegative Matrix Factorization using ADMM: Algorithm and Convergence Analysis. ICASSP, 2016.
• S. Xiao, J. Yan, C. Li, Bo Jin, Xiangfeng Wang, Hongyuan Zha, X. Yang and S. Chu, On Modelling and Predicting Individual Paper Citation Count Over Time. IJCAI, 2016.
• J. Yan, S. Xiao, C. Li, Bo Jin, Xiangfeng Wang, Hongyuan Zha and X. Yang, Modelling Contagious M$\&$A via Point Processes with a Profile Regression Prior. IJCAI, 2016.
• C. Li, F. Wei, W. Dong, Xiangfeng Wang, J. Yan, X. Zhu, Q. Liu and X. Zhang, Spatially Regularized Streaming Sensor Selection. AAAI, 2016.

2015

• Xiangfeng Wang, On the Convergence Rate of a Class of Proximal-Based Decomposition Methods for Monotone Variational Inequalities. Journal of the Operations Research Society of China, 3(3), 2015, pp.347-362.
• Xiangfeng Wang, M. Hong, S. Ma and Z.-Q. Luo, Solving Multiple-Block Separable Convex Minimization Problems using Two-Block ADMM. Pacific Journal of Optimization, 11(4), 2015, pp.645-667.
• T.-H. Chang, M. Hong and Xiangfeng Wang, Multi-Agent Distributed Large-Scale Optimization by Inexact Consensus ADMM. IEEE Transactions on Signal Processing, 63(2), 2015, pp.482-497.
• X. Luo, Xiangfeng Wang, Z. Suo and Z. Li, Efficient InSAR Phase Noise Reduction via Total Variation Regularization. Science China (Information Sciences), 2015, 58(8), 1-13.

2014

• Bo Jin, Z.L. Jing and H.T. Zhao. EVD Dualdating based Online Subspace Learning. Mathematical Problems in Engineering, 2014.
• Bo Jin, Z.L. Jing, M. Wang and H. Pan. Robust Visual Multi-task Tracking via Composite Sparse Model. Journal of Electronic Imaging, 2014, 23(6).
• Xiangfeng Wang, M. Hong, T.-H. Chang, M. Razaviyayn and Z.-Q. Luo, Joint Day-Ahead Power Procurement and Load Scheduling using Stochastic ADMM. ICASSP, 2014.

2013

• H. Pan, Z.L. Jing, Lei Ming, R.L. Liu, Bo Jin and C.L. Zhang. A Sparse Proximal Newton Splitting Method for Constrained Image Deblurring. Neurocomputing, 122, 2013, pp.245-257.
• C.L. Zhang, Z.L. Jing, Bo Jin, H. Pan. Robust Visual Tracking using Discriminative Stable Regions and K-means Clustering. Neurocomputing, 111, 2013, pp.131-143.
• H.-W. Tseng, S. Vishnubhotla, M. Hong, Xiangfeng Wang, J. Xiao, Z.-Q. Luo and T. Zhang, A Single Channel Speech Enhancement Approach by Combining Statistical Criterion and Multi-Frame Sparse Dictionary Learning. INTERSPEECH, 2013.

2012

• Xiangfeng Wang and X. Yuan, The Linearized Alternating Direction Method of Multipliers for Dantzig Selector. SIAM Journal of Scientific Computing, 34(5), 2012, pp.A2792-A2811.
• C.L. Zhang, Z.L. Jing, Bo Jin, Z.X. Li. A Dual-kernel-based Tracking Approach for Visual Target. Science China (Information Sciences), 55(3), 2012, pp.566-576.