Publications


Conference Papers in 2021:

C. Shi and C. Shen, “An Attackability Perspective on No-Sensing Adversarial Multi-player Multi-armed Bandits,” IEEE International Symposium on Information Theory (ISIT), July 2021

X. Wei and C. Shen, “Federated Learning over Noisy Channels,” IEEE International Conference on Communications (ICC), June 2021

C. Shen, P. Zhao, and X. Luo, “On Energy Efficient Uplink Multi-User MIMO with Shared LNA Control,” IEEE International Conference on Communications (ICC), June 2021

S. Zheng, C. Shen, and X. Chen, “Design and Analysis of Uplink and Downlink Communications for Federated Learning,” IEEE International Conference on Communications (ICC), June 2021 (Best Paper Award)

H. Lee, C. Shen, W. Zame, J. Lee, and M. van der Schaar, “SDF-Bayes: Cautious Optimism in Safe Dose-Finding Clinical Trials with Drug Combinations and Heterogeneous Patient Groups,” The 24rd International Conference on Artificial Intelligence and Statistics (AISTATS), Apr. 2021

C. Shi, C. Shen, and J. Yang, “Federated Multi-armed Bandits with Personalization,” The 24rd International Conference on Artificial Intelligence and Statistics (AISTATS), Apr. 2021 (Oral Presentation, 48/1527 = 3%) 

C. Shi and C. Shen, “Federated Multi-Armed Bandits,” The 35th AAAI Conference on Artificial Intelligence (AAAI), Feb. 2021 (acceptance rate: 21.4%)

Journal papers in 2021:

C. Shi and C. Shen, “Multi-player Multi-armed Bandits with Collision-Dependent Reward Distributions,” IEEE Trans. Signal Processing, accepted for publication

S. Chen *, C. Shen *, L. Zhang, and Y. Tang, “Dynamic Aggregation for Heterogeneous Quantization in Federated Learning,” IEEE Trans. Wireless Commun., accepted for publication (*: equal contribution)

S. Zheng, C. Shen, and X. Chen, “Design and Analysis of Uplink and Downlink Communications for Federated Learning,” IEEE J. Select. Areas Commun., Series on Machine Learning for Communications and Networks, vol. 39, no. 7, pp. 2150-2167, July 2021

C. Shi and C. Shen, “On No-Sensing Adversarial Multi-player Multi-armed Bandits with Collision Communications,” IEEE Journal on Selected Areas in Information Theory, Special Issue on Sequential, Active, and Reinforcement Learning, vol. 2, no. 2, pp. 515–533, June 2021

C. Shen, J. Xu, S. Zheng, and X. Chen, “Resource Rationing for Wireless Federated Learning: Concept, Benefits, and Challenges,” IEEE Commun. Mag., Data Science and Artificial Intelligence Series, vol. 59, no. 5, pp. 82-87, May 2021

L. Chen, C. Shen, P. Zhou, J. Xu, “Collaborative Service Placement for Edge Computing in Dense Small Cell Networks,” IEEE Trans. Mobile Comput., vol. 20, no. 2, pp. 377-390, Feb. 2021

Conference papers in 2020:

H.-S. Lee, Y. Zhang, W. Zame, C. Shen, J.-W. Lee, and M. van der Schaar, “Robust Recursive Partitioning for Heterogeneous Treatment Effects with Uncertainty Quantification,” accepted to the Thirty-fourth Conference on Neural Information Processing Systems (NeurIPS), Dec. 2020

C. Shen and S. Chen, “Federated Learning with Heterogeneous Quantization,” ACM/IEEE Symposium on Edge Computing - Workshop on Edge Computing and Communications (EdgeComm), Nov. 2020

C. Shen, D. Li, and J. Yang, “Adaptive MIMO Antenna Selection via Deep Learning and Submodular Optimization,” The 54th Asilomar Conference on Signals, Systems and Computers, Nov. 2020 (Invited Paper)

C. Gan, J. Yang, and C. Shen, “Thresholded Wirtinger Flow for Fast Millimeter Wave Beam Alignment,” The 54th Asilomar Conference on Signals, Systems and Computers, Nov. 2020 (Invited Paper)

C. Shen, Z. Wang, S. Villa, and M. van der Schaar, “Learning for Dose Allocation in Adaptive Clinical Trials with Safety Constraints,” The 37th International Conference on Machine Learning (ICML), July 2020

W. Chen, R. Zhou, C. Tian, and C. Shen, “On Top-k Selection from m-wise Partial Rankings via Borda Counting,” IEEE International Symposium on Information Theory (ISIT), June 2020

K. Yang, C. Shen, and T. Liu, “Deep Reinforcement Learning based Wireless Network Optimization: A Comparative Study,” IEEE INFOCOM 2020 Workshop on Data Driven Intelligence for Networks, July 2020

H. Lee, C. Shen, J. Jordon, and M. van der Schaar, “Contextual Constrained Learning for Dose-Finding Clinical Trials,” The 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), Aug. 2020

C. Shi, W. Xiong, C. Shen, and J. Yang, “Decentralized Multi-player Multi-armed Bandits with No Collision Information,” The 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), Aug. 2020

W. Wu, J. Yang, and C. Shen, “Stochastic Linear Contextual Bandits with Diverse Contexts,” The 23rd International Conference on Artificial Intelligence and Statistics (AISTATS), Aug. 2020

Journal papers in 2020:

W. R. Zame, I. Bica, C. Shen, A. Curth, H.-S. Lee, S. Bailey, J. Weatherall, D. Wright, F. Bretz, and M. van der Schaar, “Machine learning for clinical trials in the era of COVID-19,” Statistics in Biopharmaceutical Research, Special Issue on Covid-19, Aug. 2020.

S. Liu, S. Chen, C. Shen, M. Ismail, and R. Kumar, “Improved Low-Resolution Quantized SIMO Estimation via Deep Learning,” IEEE Wireless Commun. Letters, Vol. 9, No. 8, Page(s): 1331-1335, Aug. 2020

C. Gan, R. Zhou, J. Yang and C. Shen, “Cost-aware Cascading Bandits,” IEEE Trans. Signal Process., Vol. 68, Page(s): 3692-3706, June 2020

X. Xu, M. Tao, and C. Shen, “Collaborative Multi-Agent Multi-Armed Bandit Learning for Small-Cell Caching,” IEEE Trans. Wireless Commun., Vol. 19, No. 4, Page(s): 2570-2585, April 2020

F. Liang, C. Shen, W. Yu, and F. Wu, “Towards Optimal Power Control via Ensembling Deep Neural Networks,” IEEE Trans. Commun., Vol. 68, No. 3, Page(s): 1760-1776, March 2020

S. Chen, L. Zhang, C. Shen, K. Yu, S. H. Myint and Z. Wen, “On Scheduling Policies with Heavy-Tailed Dynamics in Wireless Queueing Systems,” IEEE Access, Vol. 8, No. 1, Page(s): 32137-32149, Feb. 2020