Group Members

Undergraduate students

• Omar Gonzales

   Graduate students

• Kaichuang Zhang

• Ethan Villalobos

• Alina Basharat

Publications

Journal Papers 

• J1. Ping Xu, Cameron Nowzari, and Zhi Tian, "A Class of Distributed Event-Triggered Average Consensus Algorithms for Multi-Agent Systems," International Journal of Control (2020): 1-14.

• J2. Ping Xu, Yue Wang, Xiang Chen, and Zhi Tian, "COKE: Communication-Censored Decentralized Kernel Learning," Journal of Machine Learning Research, 22.196 (2021): 1-35.

• J3. Ping Xu, Yue Wang,  Xiang Chen, and Zhi Tian, "QC-ODKLA: Quantized and Communication-Censored Online Decentralized Kernel Learning via Linearized ADMM," IEEE Transactions on Neural Networks and Learning Systems (2023).

• J4. Guanqiang Zhou, Ping Xu, Yue Wang, and Zhi Tian, "Robust Distributed Learning Against Both Distributional Shifts and Byzantine Attacks," IEEE Transactions on Neural Networks and Learning Systems, to appear.

Conference Papers 

• C1. Yue Wang, Ping Xu, and Zhi Tian, "Efficient Channel Estimation for Massive MIMO Systems via Truncated Two-Dimensional Atomic Norm Minimization," IEEE International Conference on Communications (ICC), Pairs, France, May 2017.

• C2. Ping Xu, Cameron Nowzari, and Zhi Tian, "A Class of Event-Triggered Coordination Algorithms for Multi-Agent Systems on Weight-Balanced Digraphs," Proceedings of the American Control Conference (ACC), Milwaukee, Wisconsin, 2018.

• C3. Ping Xu, Zhi Tian, and Yue Wang, "An Energy-Efficient Distributed Average Consensus Scheme via Infrequent Communication," IEEE Global Conference on Signal and Information Processing (GlobalSIP), Anaheim, CA, USA, November 2018.

• C4. Ping Xu, Zhi Tian, Zhe Zhang, and Yue Wang, "COKE: Communication-Censored Kernel Learning via Random Features," IEEE Data Science Workshop (DSW 2019), Minneapolis, MN, USA, June, 2019.

• C5. Fuxun Yu, Zhuwei Qin, Di Wang, Ping Xu, Chenchen Liu, Zhi Tian, and Xiang Chen, "DCCNN: Computational Flow Redefinition for Efficient CNN Inference through Model Structural Decoupling," 2020 Design Automation & Test in Europe Conference & Exhibition (DATE). IEEE, 2020.

• C6. Ping Xu, Yue Wang, Xiang Chen, and Zhi Tian, "Deep Kernel Learning Networks with Multiple Learning Paths," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2022), Singapore, May 2022.

• C7. Guanqiang Zhou, Ping Xu, Yue Wang, and Zhi Tian, "Achieving Certified Fairness and Robustness in Distributed Learning on Heterogeneous Datasets," 37th Annual Conference on Neural Information Processing Systems (NeurIPS 2023), New Orleans, LA, December 2023.

• C8. Ping Xu, Yue Wang, Xiang Chen, and Zhi Tian, "Communication-Efficient Decentralized Dynamic Kernel Learning," IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2024), Seoul, April 2024.

• C9. Ethan Villalobos, Constantine Tarawneh, Jia Chen, Evangelos E. Papalexakis, and Ping Xu, "Kernel Ridge Regression in Predicting Railway Crossing Accidents," American Society of Mechanical Engineers (ASME) 2024 Joint Rail Conference (JRC2024), Columbia, SC, May 2024.

• C10. Ethan Villalobos, Hector Lugo, Biqian Cheng, Miguel Gutierrez, Constantine Tarawneh, Ping Xu, Jia Chen, Evangelos E. Papalexakis, "Spectral Clustering in Railway Crossing Accidents Analysis," American Society of Mechanical Engineers (ASME) 2024 Joint Rail Conference (JRC2024), Columbia, SC, May 2024.

• C11. Kevin Quaye, Ping Xu, Dimah Dera, Heinrich Foltz, Constantine Tarawneh, Alberto Diaz, "Feature Extraction From Vibration Signature Required From Railroad Bearing Onboard Condition Monitoring Sensor Modules," American Society of Mechanical Engineers (ASME) 2024 Joint Rail Conference (JRC2024), Columbia, SC, May 2024.