1. Z. Xu, K. Wu, Z. Che, J. Tang, and J. Ye,
    Knowledge transfer in multi-task deep reinforcement learning for continuous control,
    NeurIPS’2020 (AR: 20.0%), Accepted. (PDF)

  2. Z. Xu, D. Y, J. Tang, Y. Tang, T. Yuan, Y. Wang, and G. Xue,
    An Actor-Critic-based Transfer Learning Framework for Experience-driven Networking,
    IEEE/ACM Transactions on Networking (TON), Accepted.

  3. Z. Xu, J. Tang, C. Yin, Y. Wang, G. Xue, J. Wang, and M. C. Gursoy,
    ReCARL: resource allocation in cloud RANs with deep reinforcement learning,
    IEEE Transactions on Mobile Computing (TMC), Accepted.

  4. N. Liu, X. Ma, Z. Xu, Y. Wang, J. Tang, and J. Ye,
    AutoCompress: an automatic DNN structured pruning framework for ultra-high compression rates,
    AAAI’2020 (AR: 20.6%), pp. 4876-4883. (PDF)

  5. C. Yin, J. Tang, Z. Xu, and Y. Wang,
    Memory augmented deep recurrent neural network for video question answering,
    IEEE Transactions on Neural Networks and Learning Systems (TNNLS), 2019. (PDF)

  6. J. Xu, J. Tang, Z. Xu, C. Yin, K. Kwiat, and C. Kamhoua,
    A deep recurrent neural network based predictive control framework for reliable distributed stream data processing,
    IEEE IPDPS’2019 (AR: 27.7%), 2019, pp. 262-272. (PDF)

  7. Z. Xu, J. Tang, C. Yin, Y. Wang, and G. Xue,
    Experience-driven congestion control: when multi-path TCP meets deep reinforcement learning,
    IEEE Journal on Selected Areas in Communications (JSAC),
    Special Issue on AI and Machine Learning for Networking and Communications,
    Vol. 37, No. 6, 2019, pp. 1325-1336. (PDF)

  8. N. Liu, Y. Liu, B. Logan, Z. Xu, J. Tang, and Y. Wang,
    Learning the dynamic treatment regimes from medical registry data through deep Q-network,
    Nature Scientific Reports. Vol. 9, No. 1, 2019, pp. 1495. (PDF)

  9. Z. Xu, J. Tang, J. Meng, W. Zhang, Y. Wang, C. Liu, and D. Yang,
    Experience-driven networking: a deep reinforcement learning based approach,
    IEEE INFOCOM’2018 (AR: 19.2%), pp. 1871-1879. (PDF)

  10. T. Li, Z. Xu, J. Tang, and Y. Wang,
    Model-free control for distributed stream data processing using deep reinforcement learning,
    Proceedings of VLDB Endowment, Vol. 11, No. 6, 2018, pp. 705-718. (PDF)

  11. B. Zhang, C. Liu, J. Tang, Z. Xu, J. Ma, and W. Wang,
    Learning-based energy-efficient data collection by unmanned vehicles in smart cities,
    IEEE Transactions on Industrial Informatics, Vol. 14, No. 4, 2018, pp. 1666-1676. (PDF)

  12. J. Wang , J. Tang, Z. Xu, Y. Wang, G. Xue, X. Zhang, and D. Yang,
    Spatiotemportal modeling and prediction in cellular networks: a big data enabled deep learning approach,
    IEEE INFOCOM’2017 (AR: 20.9%), pp. 1323-1331. Vol. 14, No. 4, 2018, pp. 1666-1676. (PDF)

  13. N. Liu, Z. Li, J. Xu, Z. Xu , S. Lin, Q. Qiu, J. Tang, and Y. Wang,
    A hierarchical framework of cloud resource allocation and power management using deep reinforcement learning,
    IEEE ICDCS’2017 (AR: 16.9%), pp. 372-382. (PDF)

  14. Z. Xu, Y. Wang and J. Tang, J. Wang, and M. C. Gursoy,
    A deep reinforcement learning based framework for power-efficient resource allocation in cloud RANs,
    IEEE ICC’2017 (AR: 38.1%), pp. 1-6. (PDF)

  15. Y. Liu, B. Logan, N. Liu, Z. Xu, J. Tang, and Y. Wang,
    Deep reinforcement learning for dynamic treatment regimes on medical registry data,
    IEEE ICHI’2017, pp. 380-385. (PDF)

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