Papers in Refereed Journals
[12]
Survey on Multi- Task Learning in Smart Transportation
M. Alzahrani, Q. Wang, W. Liao, X. Chen, and W. Yu, IEEE Access, To appear.
[11] Robust Multi-agent Reinforcement Learning for UAV Systems: Countering Byzantine Attacks
J. K. Medhi, R. Liu, Q. Wang, and X. Chen, MDPI Information, November 2023.
[10] A Deep Multi-Task Learning Approach for Bioelectrical Signal Analysis
J. K. Medhi, P. Ren, M. Hu, and X. Chen, MDPI Mathematics, November 2023.
[9] Resource-aware Knowl- edge Distillation for Federated Learning
Z. Chen, P. Tian, W. Liao, X. Chen, G. Xu, F. Chen, and W. Yu, IEEE Transactions on Emerging Topics in Computing, March 2023.
[8] Building Load Forecasting Using Deep Neural Network with Efficient Feature Fusion
J. Wang, X. Chen, F. Zhang, F. Chen, and Y. Xin, IEEE Journal of Modern Power Systems and Clean Energy, Janurary 2021.
[7] Deep Q-Network based Feature Selection for Multi-Sourced Data Cleaning
Q. Wang, Y. Guo, L. Yu, X. Chen, and P. Li, IEEE Internet of Things (IEEE J-IoT), August 2020.
[6] TrafficChain: A Blockchain-based Secure and Privacy-Preserving Traffic Map
Q. Wang, T. Ji, Y. Guo, L. Yu, X. Chen, and P. Li, IEEE Access, March 2020.
[5] Parallel Secure Outsourcing of Large-scale Nonlinearly Constrained Nonlinear Programming Problems
C. Luo, J. Ji, X. Chen, M. Li, L. Yang, and P. Li, IEEE Transactions on Big Data (IEEE TBD), March 2018.
[4] Channel State Information Prediction in 5G Wireless Communications: A Deep Learning Approach
C. Luo, J. Ji, Q. Wang, X. Chen, and P. Li , IEEE Transactions on Network Science and Engineering (IEEE TNSE), Vol.5, no.6, pp. 5055-5064, March 2018.
[3] Efficient Secure Outsourcing of Large-scale Sparse Linear Systems of Equations
S. Salinas, C. Luo, X. Chen, W. Liao, and P. Li, IEEE Transactions on Big Data (IEEE TBD), Vol. 4, No. 1, pp. 26-39, January-March 2018. (Best Journal Paper Award)
[2] Economic-Robust Transmission Opportunity Auction for D2D Communications in Cognitive Mesh Assisted Cellular Networks
M. Li, W. Liao, X. Chen, J. Sun, X. Huang, and P. Li, IEEE Transactions on Mobile Computing (IEEE TMC), Vol. 17, No. 8, pp. 1806-1819, August 2018.
[1] A Tutorial on Secure Outsourcing of Large-scale Computations for Big Data
S. Salinas, X. Chen, J. Ji, and P. Li, IEEE Access, 2016.
M. Alzahrani, Q. Wang, W. Liao, X. Chen, and W. Yu, IEEE Access, To appear.
[11] Robust Multi-agent Reinforcement Learning for UAV Systems: Countering Byzantine Attacks
J. K. Medhi, R. Liu, Q. Wang, and X. Chen, MDPI Information, November 2023.
[10] A Deep Multi-Task Learning Approach for Bioelectrical Signal Analysis
J. K. Medhi, P. Ren, M. Hu, and X. Chen, MDPI Mathematics, November 2023.
[9] Resource-aware Knowl- edge Distillation for Federated Learning
Z. Chen, P. Tian, W. Liao, X. Chen, G. Xu, F. Chen, and W. Yu, IEEE Transactions on Emerging Topics in Computing, March 2023.
[8] Building Load Forecasting Using Deep Neural Network with Efficient Feature Fusion
J. Wang, X. Chen, F. Zhang, F. Chen, and Y. Xin, IEEE Journal of Modern Power Systems and Clean Energy, Janurary 2021.
[7] Deep Q-Network based Feature Selection for Multi-Sourced Data Cleaning
Q. Wang, Y. Guo, L. Yu, X. Chen, and P. Li, IEEE Internet of Things (IEEE J-IoT), August 2020.
[6] TrafficChain: A Blockchain-based Secure and Privacy-Preserving Traffic Map
Q. Wang, T. Ji, Y. Guo, L. Yu, X. Chen, and P. Li, IEEE Access, March 2020.
[5] Parallel Secure Outsourcing of Large-scale Nonlinearly Constrained Nonlinear Programming Problems
C. Luo, J. Ji, X. Chen, M. Li, L. Yang, and P. Li, IEEE Transactions on Big Data (IEEE TBD), March 2018.
[4] Channel State Information Prediction in 5G Wireless Communications: A Deep Learning Approach
C. Luo, J. Ji, Q. Wang, X. Chen, and P. Li , IEEE Transactions on Network Science and Engineering (IEEE TNSE), Vol.5, no.6, pp. 5055-5064, March 2018.
[3] Efficient Secure Outsourcing of Large-scale Sparse Linear Systems of Equations
S. Salinas, C. Luo, X. Chen, W. Liao, and P. Li, IEEE Transactions on Big Data (IEEE TBD), Vol. 4, No. 1, pp. 26-39, January-March 2018. (Best Journal Paper Award)
[2] Economic-Robust Transmission Opportunity Auction for D2D Communications in Cognitive Mesh Assisted Cellular Networks
M. Li, W. Liao, X. Chen, J. Sun, X. Huang, and P. Li, IEEE Transactions on Mobile Computing (IEEE TMC), Vol. 17, No. 8, pp. 1806-1819, August 2018.
[1] A Tutorial on Secure Outsourcing of Large-scale Computations for Big Data
S. Salinas, X. Chen, J. Ji, and P. Li, IEEE Access, 2016.
Papers in Refereed Conferences
[12]
Byzantine Resilient Reinforcement Learn- ing for Multi-Agent UAV Systems
J. K. Medhi, C. Huang, R. Liu, and X. Chen, AIAA SCITECH 2023 Forum, National Harbor, MD, January 2023.
[11] Asynchronous Blockchain-based Privacy-preserving Training Framework for Disease Diagnosis
X. Chen, X. Wang, and K. Yang, Workshop on Security and Privacy on Blockahcian (IEEE BigData'19 Workshop), Los Angelas, December 2019.
[10] Data Streaming Analysis Framework for Through-time 3D Free-breathing Liver DCE-MRI
K. Yang, X. Chen, and P. Li, IEEE International Conference on Big Data (IEEE BigData’19), Los Angelas, December 2019.
[9] Learning to Learn Gradient Aggregation by Gradient Descent
J. Ji, X. Chen, Q. Wang, L. Yu, and P. Li, The 28th International Joint Conference on Artificial Intelligence (IJCAI'19), Macao, China, August 2019. (Acceptance Ratio = 850/4752 = 17.9%)
[8] When Machine Learning Meets Blockchain: A Decentralized, Privacy-preserving, and Secure Design
X. Chen, J. Ji, C. Luo, W. Liao, and P. Li, IEEE International Conference on Big Data (IEEE BigData’18), Seattle, WA, December, 2018. (Acceptance Ratio = 99/518 = 19.1%)
[7] A Unified Unsupervised Gaussian Mixture Variational Autoencoder for High Dimensional Outlier Detection
W. Liao, Y. Guo, X. Chen, and P. Li, IEEE International Conference on Big Data (IEEE BigData’18), Seattle, WA, December, 2018. (Acceptance Ratio = 99/518 = 19.1%)
[6] SecureNets: Secure Inference of Deep Neural Networks on an Untrusted Cloud
X. Chen, J. Ji, L. Yu, C. Luo, and P. Li, The 10th Asian Conference on Machine Learning (ACML’18), Beijing, China, November, 2018. (Acceptance Ratio = 57/230 = 24.8%)
[5] Cost-Sensitive Deep Active Learning for Epileptic Seizure Detection
X. Chen, J. Ji, T. Ji, and P. Li, The 9th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB’18), Washington, DC, August, 2018.
[4] Cross-Domain Sentiment Classification via A Bifurcation-LSTM
J. Ji, C. Luo, X. Chen, L. Yu, and P. Li, The 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'18), Melbourne, Australia, June, 2018. (Acceptance Ratio = 59 (long papers)/590 = 10%) (IEEE PAKDD Best Application Paper Award)
[3] A Deep Multi-task Learning Approach for ECG Data Analysis
J. Ji, X. Chen, C. Luo, and P. Li, IEEE International Conference on Biomedical and Health Informatics (IEEE BHI'18), Las Vegas, NV, March, 2018.
[2] Real-time Personalized Cardiac Arrhythmia Detection and Diagnosis: A Cloud Computing Architecture
X. Chen, J. Ji, K. A. Loparo, and P. Li, IEEE International Conference on Biomedical and Health Informatics (IEEE BHI’17), Orlando, FL, February, 2017.
[1] Efficient Secure Outsourcing of Large-scale Linear Systems of Equations
S. Salinas, C. Luo, X. Chen, and P. Li, IEEE International Conference on Computer Communications (IEEE INFOCOM’15), Hong Kong, China, April, 2015. (Acceptance ratio = 316/1640 = 19.3%)
J. K. Medhi, C. Huang, R. Liu, and X. Chen, AIAA SCITECH 2023 Forum, National Harbor, MD, January 2023.
[11] Asynchronous Blockchain-based Privacy-preserving Training Framework for Disease Diagnosis
X. Chen, X. Wang, and K. Yang, Workshop on Security and Privacy on Blockahcian (IEEE BigData'19 Workshop), Los Angelas, December 2019.
[10] Data Streaming Analysis Framework for Through-time 3D Free-breathing Liver DCE-MRI
K. Yang, X. Chen, and P. Li, IEEE International Conference on Big Data (IEEE BigData’19), Los Angelas, December 2019.
[9] Learning to Learn Gradient Aggregation by Gradient Descent
J. Ji, X. Chen, Q. Wang, L. Yu, and P. Li, The 28th International Joint Conference on Artificial Intelligence (IJCAI'19), Macao, China, August 2019. (Acceptance Ratio = 850/4752 = 17.9%)
[8] When Machine Learning Meets Blockchain: A Decentralized, Privacy-preserving, and Secure Design
X. Chen, J. Ji, C. Luo, W. Liao, and P. Li, IEEE International Conference on Big Data (IEEE BigData’18), Seattle, WA, December, 2018. (Acceptance Ratio = 99/518 = 19.1%)
[7] A Unified Unsupervised Gaussian Mixture Variational Autoencoder for High Dimensional Outlier Detection
W. Liao, Y. Guo, X. Chen, and P. Li, IEEE International Conference on Big Data (IEEE BigData’18), Seattle, WA, December, 2018. (Acceptance Ratio = 99/518 = 19.1%)
[6] SecureNets: Secure Inference of Deep Neural Networks on an Untrusted Cloud
X. Chen, J. Ji, L. Yu, C. Luo, and P. Li, The 10th Asian Conference on Machine Learning (ACML’18), Beijing, China, November, 2018. (Acceptance Ratio = 57/230 = 24.8%)
[5] Cost-Sensitive Deep Active Learning for Epileptic Seizure Detection
X. Chen, J. Ji, T. Ji, and P. Li, The 9th ACM Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM BCB’18), Washington, DC, August, 2018.
[4] Cross-Domain Sentiment Classification via A Bifurcation-LSTM
J. Ji, C. Luo, X. Chen, L. Yu, and P. Li, The 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD'18), Melbourne, Australia, June, 2018. (Acceptance Ratio = 59 (long papers)/590 = 10%) (IEEE PAKDD Best Application Paper Award)
[3] A Deep Multi-task Learning Approach for ECG Data Analysis
J. Ji, X. Chen, C. Luo, and P. Li, IEEE International Conference on Biomedical and Health Informatics (IEEE BHI'18), Las Vegas, NV, March, 2018.
[2] Real-time Personalized Cardiac Arrhythmia Detection and Diagnosis: A Cloud Computing Architecture
X. Chen, J. Ji, K. A. Loparo, and P. Li, IEEE International Conference on Biomedical and Health Informatics (IEEE BHI’17), Orlando, FL, February, 2017.
[1] Efficient Secure Outsourcing of Large-scale Linear Systems of Equations
S. Salinas, C. Luo, X. Chen, and P. Li, IEEE International Conference on Computer Communications (IEEE INFOCOM’15), Hong Kong, China, April, 2015. (Acceptance ratio = 316/1640 = 19.3%)