Projects per year
Personal profile
Research Interests
Bandit Learning, Reinforcement Learning, Wireless Networks, and Bayesian Optimization
Education/Academic qualification
PhD, Electrical Engineering, Texas A&M University
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Dive into the research topics where Ping-Chun Hsieh is active. These topic labels come from the works of this person. Together they form a unique fingerprint.
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Collaborations and top research areas from the last five years
Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
Projects
- 4 Finished
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基於深度強化學習與大規模物聯網之即時無線感測(4/4)
1/09/22 → 31/08/23
Project: Government Ministry › Other Government Ministry Institute
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Deep Reinforcement Learning for Real-Time Wireless Sensing in Massive Io
1/09/21 → 30/11/22
Project: Government Ministry › Other Government Ministry Institute
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Deep Reinforcement Learning for Real-Time Wireless Sensing in Massive IoT
1/09/20 → 30/11/21
Project: Government Ministry › Other Government Ministry Institute
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Deep Reinforcement Learning for Real-Time Wireless Sensing in Massive IoT
1/09/19 → 30/11/20
Project: Government Ministry › Other Government Ministry Institute
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Deterministic Bandwidth-Based Packet-Level Traffic Splitting for Datacenter Networks
Wu, C. Y., Yen, L. H., Hsieh, P-C. & Tseng, C-C., 2022, APNOMS 2022 - 23rd Asia-Pacific Network Operations and Management Symposium: Data-Driven Intelligent Management in the Era of beyond 5G. Institute of Electrical and Electronics Engineers Inc., (APNOMS 2022 - 23rd Asia-Pacific Network Operations and Management Symposium: Data-Driven Intelligent Management in the Era of beyond 5G).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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Neural Frank-Wolfe Policy Optimization for Region-of-Interest Intra-Frame Coding with HEVC/H.265
Ho, Y. H., Chia-Hao Kao, Peng, W-H. & Hsieh, P-C., 2022, 2022 IEEE International Conference on Visual Communications and Image Processing (VCIP). (2022 IEEE International Conference on Visual Communications and Image Processing (VCIP)).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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Escaping from Zero Gradient: Revisiting Action-Constrained Reinforcement Learning via Frank-Wolfe Policy Optimization
Lin, J. L., Hung, W., Yang, S. H., Hsieh, P-C. & Liu, X., 27 Jul 2021, p. 397-407. 11 p.Research output: Contribution to conference › Paper › peer-review
4 Scopus citations -
NeurWIN: Neural Whittle Index Network For Restless Bandits Via Deep RL
Nakhleh, K., Ganji, S., Hsieh, P-C., Hou, I. H. & Shakkottai, S., Dec 2021, Proceedings of the Thirty-fifth Conference on Neural Information Processing Systems. p. 1-20 20 p.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
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NeurWIN: Neural Whittle Index Network for Restless Bandits Via Deep RL
Nakhleh, K., Ganji, S., Hsieh, P. C., Hou, I. H. & Shakkottai, S., 2021, Advances in Neural Information Processing Systems 34 - 35th Conference on Neural Information Processing Systems, NeurIPS 2021. Ranzato, MA., Beygelzimer, A., Dauphin, Y., Liang, P. S. & Wortman Vaughan, J. (eds.). Neural information processing systems foundation, p. 828-839 12 p. (Advances in Neural Information Processing Systems; vol. 2).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review
7 Scopus citations