Related Publications

References

[1] Y. Tang, J. Zhang, and N. Li, “Distributed zero-order algorithms for nonconvex multi-agent op-timization,” IEEE Transactions on Control of Network Systems. 8 (1) 269 to 281, 2021.

[2] Malu, Mohit and Dasarathy, Gautam and Spanias, Andreas. “Bayesian Optimization in High-Dimensional Spaces: A Brief Survey.” International Conference on Information Intelligence Systems and Applications.

[3] Li, Weizhi and Dasarathy, Gautam and Ramamurthy, Karthikeyan N. and Berisha, Visar. “Finding the Homology of Decision Boundaries with Active Learning.” Advances in neural information processing systems 2020.

[4] J. Zhang, N. Li and Dedeoglu: “Federated Learning over Wireless Networks: A Band-limited Coordinated Descent Approach,” presented at INFOCOM 2021.

[5] Z. Liu, M. Del Rosario† and Z. Ding, “A Markovian Model-Driven Deep Learning Framework for Massive MIMO CSI Feedback,” in IEEE Transactions on Wireless Communications, doi: 10.1109/TWC.2021.3103120.

[6] Y. -C. Lin, Z. Liu, T. -S. Lee and Z. Ding, “Deep Learning Phase Compression for MIMO CSI Feedback by Exploiting FDD Channel Reciprocity,” in IEEE Wireless Communications Letters, doi: 10.1109/LWC.2021.3096808.

[7] S. Zhang, S. Cui and Z. Ding, “Hypergraph Spectral Analysis and Processing in 3D Point Cloud,” in IEEE Transactions on Image Processing, vol. 30, pp. 1193-1206, 2021, doi: 10.1109/TIP.2020.3042088.

[8] Mason del Rosario and Zhi Ding, “Learning-Based MIMO Channel Estimation under Practical Pilot Sparsity and Feedback Compression”, IEEE Transactions on Wireless Communications, Accepted, 2022.

[9] Z. Liu, M. Del Rosario† and Z. Ding, “A Markovian Model-Driven Deep Learning Framework for Massive MIMO CSI Feedback,” in IEEE Transactions on Wireless Communications. doi: 10.1109/TWC.2021.3103120.

[10] Chamain, Lahiru D. and Qi, Siyu and Ding, Zhi, “End-to-End Image Classificationand Compression with variational autoencoders”, IEEE Internet of Things Journal, 2022. doi: 10.1109/JIOT.2022.3182313

[11] S. Zhang, S. Cui and Z. Ding, “Hypergraph Spectral Analysis and Processing in 3D Point Cloud,” in IEEE Transactions on Image Processing, vol. 30, pp. 1193-1206, 2021. doi: 10.1109/TIP.2020.3042088.

[12] Q. Deng, S. Zhang, and Z. Ding, “Point Cloud Resampling via Hypergraph Signal Processing”, in IEEE Signal Processing Letters, vol. 28, pp. 2117-2121, 2021. doi: 10.1109/LSP.2021.3119257.

[13] Y. -C. Lin, Z. Liu, T. -S. Lee and Z. Ding, “Deep Learning for Partial MIMO CSI Feedback by Exploiting Channel Temporal Correlation,” 55th Asilomar Conference on Signals, Systems, and Computers, 2021, doi:10.1109/IEEECONF53345.2021.9723211.

[14] Yujie Tang, Vikram Ramanathan, Junshan Zhang, Na Li: “Communication-Efficient Distributed SGD With Compressed Sensing.” IEEE Control. Syst. Lett. 6: 2054-2059 (2022)

[15] Sen Lin, Li Yang, Deliang Fan, Junshan Zhang: “TRGP: Trust Region Gradient Projection for Continual Learning.” ICLR 2022

[16] Hang Wang, Sen Lin, Junshan Zhang:”Adaptive Ensemble Q-learning: Minimizing Estimation Bias via Error Feedback.”  NeurIPS 2021: 24778-24790.

[17] Xuanyu Cao and Tamer Basar and Suhas N. Diggavi and Yonina C. Eldar and Khaled B. Letaief and H. Vincent Poor and Junshan Zhang: “Communication-Efficient Distributed Learning: An Overview.” IEEE J. Sel. Areas Commun. 41(4): 851-873 (2023).

[18] Q. Wu, X. Chen, Z. Zhou, and J. Zhang, “FedHome: Cloud-Edge based Personalized Federated Learning for In-Home Health Monitoring,”  IEEE Transactions on Mobile Computing, 21(8): 2818-2832 (2022). 

[19] Qiong Wu and Xu Chen and Tao Ouyang and Zhi Zhou and Xiaoxi Zhang and Shusen Yang and Junshan Zhang:
“HiFlash: Communication-Efficient Hierarchical Federated Learning With Adaptive Staleness Control and Heterogeneity-Aware Client-Edge Association.” IEEE Trans. Parallel Distributed Syst. 34(5): 1560-1579 (2023)

[20] Xuanyu Cao and Tamer Basar and Suhas N. Diggavi and Yonina C. Eldar and Khaled B. Letaief and H. Vincent Poor and Junshan Zhang: “Guest Editorial Communication-Efficient Distributed Learning Over Networks.” IEEE J. Sel. Areas Commun. 41(4): 845-850 (2023)

[21] Zhang, Songyang and Deng, Qinwen and Ding, Zhi. (2024). Signal Processing Over Multilayer Graphs: Theoretical Foundations and Practical Applications.  IEEE Internet of Things Journal. 11      

[22] Lin, Yu-Chien and Lee, Ta-Sung and Ding, Zhi. (2023). A Scalable Deep Learning Framework for Dynamic CSI Feedback with Variable Antenna Port Numbers.  IEEE Transactions on Wireless Communications.   

[23] Qi, Siyu and Chamain, Lahiru D. and Ding, Zhi. (2022). Hierarchical Training for Distributed Deep Learning Based on Multimedia Data over Band-Limited Networks.  Proceedings International Conference on Image Processing.  . 

 [24] Feres, Carlos and Levy, Bernard C. and Ding, Zhi. (2024). Over-the-Air Multisensor Collaboration for Resource Efficient Joint Detection.  IEEE Transactions on Signal Processing. 72 . 

[25] Zhang, Songyang and Deng, Qinwen and Ding, Zhi. (2022). Multilayer graph spectral analysis for hyperspectral images.  EURASIP Journal on Advances in Signal Processing. 2022  (1) . 

[26] Del Rosario, Mason and Ding, Zhi. (2023). Learning-Based MIMO Channel Estimation under Practical Pilot Sparsity and Feedback Compression.  IEEE transactions on wireless communications. 22  (2) 1161-1174.  

[27] Hsu, Chih-Ho and Feres, Carlos and Ding, Zhi. (2023). Spectral Clustering Aided User Grouping and Scheduling in Wideband MU-MIMO Systems.  IEEE International Conference on Communications. 

[28] Lin, Yu-Chien and Lee, Ta-Sung and Ding, Zhi. (2023). Exploiting Partial FDD Reciprocity for Beam Based Pilot Precoding and CSI Feedback in Deep Learning.  IEEE Transactions on Wireless Communications.  Accepted.  

[29] Zhang, Songyang and Yu, Tianhang and Tivald, Jonathan and Choi, Brian and Ouyang, Feng and Ding, Zhi. (2022). Exemplar-Based Radio Map Reconstruction of Missing Areas Using Propagation Priority.  GLOBECOM 2022 – 2022 IEEE Global Communications Conference.  1217 to 1222.  

[30] Deng, Qinwen and Zhang, Songyang and Ding, Zhi. (2021). Point Cloud Resampling via Hypergraph Signal Processing.  IEEE Signal Processing Letters. 28 2117 to 2121.  

[31] Chamain, Lahiru D. and Qi, Siyu and Ding, Zhi. (2022). End-to-End Image Classification and Compression with variational autoencoders.  IEEE Internet of Things Journal.  1 to 1.  

[32] Lin, Yu-Chien and Lee, Ta-Sung and Ding, Zhi. (2021). Deep Learning for Partial MIMO CSI Feedback by Exploiting Channel Temporal Correlation.  55th Asilomar Conference on Signals, Systems, and Computers.  345 to 350.  

[33] Lin, Yu-Chien and Liu, Zhenyu and Lee, Ta-Sung and Ding, Zhi. (2021). Deep Learning Phase Compression for MIMO CSI Feedback by Exploiting FDD Channel Reciprocity.  IEEE Wireless Communications Letters.  1 to 1.  

[34] Liu, Zhenyu and del Rosario, Mason and Ding, Zhi. (2022). A Markovian Model-Driven Deep Learning Framework for Massive MIMO CSI Feedback.  IEEE Transactions on Wireless Communications. 21  (2) 1214 to 1228.  

[35] Zhang, Songyang and Cui, Shuguang and Ding, Zhi. (2021). Hypergraph Spectral Analysis and Processing in 3D Point Cloud.  IEEE Transactions on Image Processing. 30 1193 to 1206.  

[36] Zhenyu Liu, Mason del. (2021). A Markovian Model-Driven Deep Learning Framework for Massive MIMO CSI Feedback.  IEEE transactions on wireless communications. 

A Code Repositories

• Active Learning homology [Codebase]