Hao Gao

Hao Gao

Previous ML interns at HUAWEI & TOYOTA. PhD in Machine Learning and Game Theory

Publication

My citation profile in Google Scholar.

Please email me if you require a copy of the paper.

Preprint Book

  1. Gao, H., et al. “Mean Fild Game guided machine learning.” Planned Submission to Springer.

Journal Article

  1. H. Gao, W. Li, R. A. Banez, Z. Han and H. V. Poor, “Mean Field Evolutionary Dynamics in Dense-User Multi-Access Edge Computing Systems,” in IEEE Transactions on Wireless Communications, vol. 19, no. 12, pp. 7825-7835, Dec. 2020, doi: 10.1109/TWC.2020.3016695.
  2. H. Gao, W. Li, M. Pan, Z. Han and H. V. Poor, “Modeling COVID-19 with mean field evolutionary dynamics: Social distancing and seasonality,” in Journal of Communications and Networks, vol. 23, no. 5, pp. 314-325, Oct. 2021, doi: 10.23919/JCN.2021.000032.
  3. H. Gao, W. Lee, Y. Kang, W. Li, Z. Han, S. Osher and H. V. Poor “Energy-Efficient Velocity Control for Massive Numbers of UAVs: A Mean Field Game Approach,” in IEEE Transactions on Vehicular Technology, vol. 71, no. 6, pp. 6266-6278, June 2022, doi: 10.1109/TVT.2022.3158896.
  4. H. Gao, A. Lin, R. A. Banez, W. Li, Z. Han, S. Osher and H. V. Poor, “Opinion Evolution in Social Networks: Connecting Mean Field Games to Generative Adversarial Nets,” in IEEE Transactions on Network Science and Engineering, vol. 9, no. 4, pp. 2734-2746, 1 July-Aug. 2022, doi: 10.1109/TNSE.2022.3169057.
  5. H. Gao, Q. Wan, Y. Kang, X. Fu, Z. Han, “ Evolutionary neural architecture search with mean field game selection mechanism”, submitted to IEEE Open Journal of Communication Society on Dec. 10, 2022.
  6. D. Shi, H. Gao, L. Wang, M. Pan, Z. Han and H. V. Poor, “Mean Field Game Guided Deep Reinforcement Learning for Task Placement in Cooperative Multiaccess Edge Computing,” in IEEE Internet of Things Journal, vol. 7, no. 10, pp. 9330-9340, Oct. 2020, doi: 10.1109/JIOT.2020.2983741.
  7. R. A. Banez, H. Gao, L. Li, C. Yang, Z. Han and H. V. Poor, “Modeling and Analysis of Opinion Dynamics in Social Networks Using Multiple-Population Mean Field Games,” in IEEE Transactions on Signal and Information Processing over Networks, vol. 8, pp. 301-316, 2022, doi: 10.1109/TSIPN.2022.3166102.

Conference Paper

  1. H. Gao, Y. Liu, E. A. Sisbot, Y. Z. Farid, K. Oguchi, and Z. Han, “Hierarchical Federated Learning with Mean Field Game Device Selection for Connected Vehicle Applications”, submitted to IEEE Intelligent Vehicle 2023 on Jan. 28. 2023.
  2. H. Gao, A. Lin, R. A. Banez, W. Li, Z. Han, S. Osher and H. V. Poor, “Belief and Opinion Evolution in Social Networks: A High-Dimensional Mean Field Game Approach,” ICC 2021 - IEEE International Conference on Communications, Montreal, QC, Canada, 2021, pp. 1-6, doi: 10.1109/ICC42927.2021.9500884.
  3. H. Gao, W. Li, R. A. Banez, Z. Han and H. V. Poor, “Mean Field Evolutionary Dynamics in Ultra Dense Mobile Edge Computing System,” 2019 IEEE Global Communications Conference (GLOBECOM), Waikoloa, HI, USA, 2019, pp. 1-6, doi: 10.1109/GLOBECOM38437.2019.9013572.
  4. H. Gao, W. Lee, W. Li, Z. Han, S. Osher and H. V. Poor, “Energy-Efficient Velocity Control for Massive Numbers of Rotary-Wing UAVs: A Mean Field Game Approach,” GLOBECOM 2020 - 2020 IEEE Global Communications Conference, Taipei, Taiwan, 2020, pp. 1-6, doi: 10.1109/GLOBECOM42002.2020.9322391.
  5. H. Gao, W. Li, M. Pan, Z. Han and H. V. Poor, “Analyzing Social Distancing and Seasonality of COVID-19 with Mean Field Evolutionary Dynamics,” 2020 IEEE Globecom Workshops (GC Wkshps, Taipei, Taiwan, 2020, pp. 1-6, doi: 10.1109/GCWkshps50303.2020.9367567.
  6. R. A. Banez, H. Gao, L. Li, C. Yang, Z. Han and H. V. Poor, “Belief and Opinion Evolution in Social Networks Based on a Multi-Population Mean Field Game Approach,” ICC 2020 - 2020 IEEE International Conference on Communications (ICC), Dublin, Ireland, 2020, pp. 1-6, doi: 10.1109/ICC40277.2020.9148985.

Patent

  1. Gao, H., et al. Systems and methods for communication-efficient model aggregation in federated networks forconnected vehicle applications. USPTO serial No. 17/968,175.
  2. Gao, H., et al. Reduce the data heterogeneity with user-edge association in hierarchical federated learning networks. Submitted to USPTO.