💻 Selected Research Papers
🙋♂️ Mobile Crowdsensing
- AoI-minimal UAV Crowdsensing by Model-based Graph Convolutional Reinforcement Learning, Zipeng Dai, Chi Harold Liu, Yuxiao Ye, Rui Han, Ye Yuan, Guoren Wang, Jian Tang, INFOCOM 2022,
- Delay-Sensitive Energy-Efficient UAV Crowdsensing by Deep Reinforcement Learning, Zipeng Dai, Chi Harold Liu, Rui Han, Guoren Wang, Kin K. Leung, Jian Tang, IEEE Transactions on Mobile Computing (TMC), 2022,
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Distributed and Energy-Efficient Mobile Crowdsensing with Charging Stations by Deep Reinforcement Learning, Chi Harold Liu, Zipeng Dai, Yinuo Zhao, Jon Crowcroft, Dapeng Wu, Kin K. Leung, IEEE Transactions on Mobile Computing (TMC), 2019,
- [Energy-Efficient 3D Vehicular Crowdsourcing for Disaster Response by Distributed Deep Reinforcement Learning], Hao Wang, Chi Harold Liu et al, “”, in ACM SIGKDD’21, New York, pp. 3679–3687. (Best Paper Runner-up Award) Hao Wang, Chi Harold Liu et al., “Ensuring Threshold AoI for UAV-assisted Mobile Crowdsensing by Multi-Agent Deep Reinforcement Learning with Transformer”, IEEE/ACM Trans. on Networking, 10.1109/TNET.2023.3289172. Hao Wang, Bo Tang, Chi Harold Liu, et al., “HiBid: A Cross-Channel Constrained Bidding System with Budget Allocation by Hierarchical Offline Deep Reinforcement Learning”, IEEE Transactions on Computers, 2023. (accepted) YuxiaoYe, Hao Wang (Equal Contribution), Chi Harold Liu, et al., “QoI-Aware Mobile Crowdsensing for Metaverse by Multi-Agent Deep Reinforcement Learning”, IEEE Journal on Selected Areas in Communications, 2023. (accepted) Zipeng Dai, Hao Wang, Chi Harold Liu, et al., “Mobile Crowdsensing for Data Freshness: A Deep Reinforcement Learning Approach”, in IEEE INFOCOM 2021, Vancouver, BC, Canada, pp. 1-1.
🚖 Recommender System & Constrained Bidding
- HiBid: A Cross-Channel Constrained Bidding System with Budget Allocation by Hierarchical Offline Deep Reinforcement Learning, Hao Wang, Bo Tang, Chi Harold Liu, et al., “HiBid: A Cross-Channel Constrained Bidding System with Budget Allocation by Hierarchical Offline Deep Reinforcement Learning”, IEEE Transactions on Computers, 2023. (accepted)