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Publications

Explainable AI

  • J. Wang, K. Feng, and J. Wu, “SVM-based deep stacking networks,” in Proceedings of the AAAI Conference on Artificial Intelligence (AAAI'19), vol. 33, pp. 5273–5280, 2019. (Acceptance rate=16.2%, CCF A) read more
  • J. Wang, Y. Zhang, K. Tang, J. Wu, and Z. Xiong, “AlphaStock: A buying-winners-and-selling-losers investment strategy using interpretable deep reinforcement attention networks,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (SIGKDD'19), pp. 1900–1908, 2019. (Acceptance rate=14.2%, CCF A) read more
  • J. Wang, Z. Wang, J. Li, and J. Wu, “Multilevel wavelet decomposition network for interpretable time series analysis,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (SIGKDD'18), pp. 2437–2446, ACM, 2018. (Acceptance rate=18.4%, CCF A) read more
  • J. Wang, Q. Gu, J. Wu, G. Liu, and Z. Xiong, “Traffic speed prediction and congestion source exploration: A deep learning method,” in 2016 IEEE 16th International Conference on Data Mining (ICDM'16), pp. 499–508, IEEE, 2016. (Acceptance rate=8.6%) read more
  • J. Wang, Y. Mao, J. Li, Z. Xiong, and W.-X. Wang, “Predictability of road traffic and congestion in urban areas,” PloS one, vol. 10, no. 4, p. e0121825, 2015. (IF=2.776)  read more
  • L. Cong, K. Tang, J. Wang and Y. Zhang, “Alphaportfolio and interpretable AI for finance,” working paper.

 

Fintech & Econometrics

  • J. Wang, Y. Zhang, K. Tang, J. Wu, and Z. Xiong, “AlphaStock: A buying-winners-and-selling-losers investment strategy using interpretable deep reinforcement attention networks,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (SIGKDD'19), pp. 1900–1908, 2019. (Acceptance rate=14.2%, CCF A) read more
  • H. Hong, X. Lin, K. Tang and J. Wang, “Artificial-Intelligence assisted decision making: a statistical framework,” Available at SSRN 3508224, 2019, working paper. read more
  • K. Feng, H. Hong, K. Tang, and J. Wang, “Decision making with machine learning and ROC curves,” Available at SSRN 3382962, 2019, working paper. read more
  • L. Cong, K. Tang, J. Wang and Y. Zhang, “Alphaportfolio and interpretable AI for finance,” working paper.

 

Spatio-temporal Data Mining & Urban Computing

  • S. Guo, C. Chen, J. Wang, Y. Liu, X. Ke, Z. Yu, D. Zhang, and D.-M. Chiu, “Rod-revenue: Seeking strategies analysis and revenue prediction in ride-on-demand service using multi-source urban data,” IEEE Transactions on Mobile Computing (TMC), 2019. (IF=4.474, CCF A) read more
  • S. Guo, C. Chen, J. Wang, Y. Liu, K. Xu, and D. M. Chiu, “Fine-grained dynamic price prediction in ride-on-demand services: Models and evaluations,” Mobile Networks and Applications (MONET), pp. 1–16, 2019. read more
  • N. Wu, J. Wang, W. X. Zhao, and Y. Jin, “Learning to effectively estimate the travel time for fastest route recommendation,” in Proceedings of the 28th ACM International Conference on Information and Knowledge Management (CIKM'19), pp. 1923–1932, 2019. (Acceptance rate=19.4%) read more
  • J. Wang, N. Wu, X. Lu, X. Zhao, and K. Feng, “Deep trajectory recovery with fine-grained calibration using Kalman filter,” IEEE Transactions on Knowledge and Data Engineering (TKDE), 2019. (IF=3.857, CCF A) read more
  • J. Wang, N. Wu, W. X. Zhao, F. Peng, and X. Lin, “Empowering A* search algorithms with neural networks for personalized route recommendation,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD'19), pp. 539–547, 2019.  (Acceptance rate= 14.2%, CCF A) read more
  • J. Wang, J. Wu, Z. Wang, F. Gao, and Z. Xiong, “Understanding urban dynamics via context-aware tensor factorization with neighboring regularization,” IEEE Transactions on Knowledge and Data Engineering (TKDE), 2019.  (IF=3.857, CCF A) read more
  • S. Guo, C. Chen, J. Wang, Y. Liu, K. Xu, and D. M. Chiu, “Dynamic price prediction in ride-on-demand service with multi-source urban data,” in Proceedings of the 15th EAI International Conference on Mobile and Ubiquitous Systems: Computing, Networking and Services (MobiQuitous' 18), pp. 412–421, ACM, 2018. read more
  • S. Guo, C. Chen, J. Wang, Y. Liu, K. Xu, D. Zhang, and D. M. Chiu, “A simple but quantifiable approach to dynamic price prediction in ride-on-demand services leveraging multi-source urban data,” Proceedings of the ACM on Interactive, Mobile, Wearable and Ubiquitous Technologies (Ubicomp'18), vol. 2, no. 3, p. 112, 2018. (CCF A) read more
  • J. Wang, X. Wang, and J. Wu, “Inferring metapopulation propagation network for intra-city epidemic control and prevention,” in Proceedings of the 24th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (SIGKDD'18), pp. 830–838, ACM, 2018. (Acceptance rate=18.4%, CCF A) read more
  • J. Wang, X. He, Z. Wang, J. Wu, N. J. Yuan, X. Xie, and Z. Xiong, “CD-CNN: a partially supervised cross-domain deep learning model for urban resident recognition,” in Thirty-Second AAAI Conference on Artificial Intelligence (AAAI'18), 2018. (Acceptance rate=24.6%, CCF A) read more
  • J. Wang, C. Chen, J. Wu, and Z. Xiong, “No longer sleeping with a bomb: a duet system for protecting urban safety from dangerous goods,” in Proceedings of the 23rd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (SIGKDD'17), pp. 1673–1681, ACM, 2017. (Acceptance rate=17.4%, CCF A) read more
  • J. Wang, Y. Lin, J. Wu, Z. Wang, and Z. Xiong, “Coupling implicit and explicit knowledge for customer volume prediction,” in Thirty-First AAAI Conference on Artificial Intelligence (AAAI'17), 2017. (Acceptance rate=24.6%, CCF A) read more
  • J. Wang, Q. Gu, J. Wu, G. Liu, and Z. Xiong, “Traffic speed prediction and congestion source exploration: A deep learning method,” in 2016 IEEE 16th International Conference on Data Mining (ICDM'16), pp. 499–508, IEEE, 2016. (Acceptance rate=8.6%) read more
  • J. Wang, Y. Mao, J. Li, Z. Xiong, and W.-X. Wang, “Predictability of road traffic and congestion in urban areas,” PloS one, vol. 10, no. 4, p. e0121825, 2015. (IF=2.776) read more
  • C. Yin, Z. Xiong, H. Chen, J. Wang, D. Cooper, and B. David, “A literature survey on smart cities,” Science China Information Sciences, vol. 58, no. 10, pp. 1–18, 2015. read more
  • J. Wang, F. Gao, P. Cui, C. Li, and Z. Xiong, “Discovering urban spatio-temporal structure from time-evolving traffic networks,” in Asia-Pacific Web Conference (APWeb'14), pp. 93–104, Springer, 2014. read more
  • Z. Zhai, B. Liu, J. Wang, H. Xu, and P. Jia, “Product feature grouping for opinion mining,” IEEE Intelligent Systems, vol. 27, no. 4, pp. 37–44, 2011. (IF=4.464) read more

 

Transportation Protocols for Big Data 

  • W. Jing, D. Tong, Y. Wang, J. Wang, Y. Liu, and P. Zhao, “MAMR: High-performance mapreduce programming model for material cloud applications,” Computer Physics Communications, vol. 211, pp. 79–87, 2017. (IF=3.309) read more
  • J. Wang, J. Wen, J. Zhang, Z. Xiong, and Y. Han, “TCP-FIT: An improved TCP algorithm for heterogeneous networks,” Journal of Network and Computer Applications (JNCA), vol. 71, pp. 167–180, 2016. (IF=5.273) read more
  • J. Wang, J. Wen, C. Li, Z. Xiong, and Y. Han, “DC-Vegas: a delay-based TCP congestion control algorithm for datacenter applications,” Journal of Network and Computer Applications (JNCA), vol. 53, pp. 103–114, 2015. (IF=5.273) read more
  • J. Wang, J. Wen, Y. Han, J. Zhang, C. Li, and Z. Xiong, “CUBIC-FIT: A high performance and TCP cubic friendly congestion control algorithm,” IEEE Communications Letters, vol. 17, no. 8, pp. 1664–1667, 2013. (IF=3.457) read more
  • J. Wang, J. Wen, J. Zhang, and Y. Han, “TCP-FIT: An improved TCP congestion control algorithm and its performance,” in 2011 Proceedings IEEE INFOCOM (INFOCOM), pp. 2894–2902, IEEE, 2011. (Acceptance rate=16.0%, CCF A) read more
  • J. Wang, H. Li, Z. Zhai, X. Chen, and S. Yang, “An improved TCP friendly rate control algorithm for wireless networks,” IEICE Transactions on Fundamentals of Electronics, Communications and Computer Sciences, vol. 94, no. 11, pp. 2295–2305, 2011. read more