• Weishi Shi, Dayou Yu, and Qi Yu: A Gaussian Process-Bayesian Bernoulli Mixture Model for Multi-Label Active Learning, NeurIPS 2021.
  • Dingrong Wang, Hitesh Sapkota, Xumin Liu, and Qi Yu: Deep Reinforced Attention Regression for Partial Sketch Based Image Retrieval, ICDM 2021 (full paper).
  • Krishna Neupane, Ervine Zheng, and Qi Yu:MetaEDL: Meta Evidential Learning For Uncertainty-Aware Cold-Start Recommendations, ICDM 2021 (short paper).
  • Wentao Bao, Qi Yu, Yu Kong: Evidential Deep Learning for Open Set Action Recognition. ICCV 2021 (oral).
  • Wentao Bao, Qi Yu, Yu Kong: DRIVE: Deep Reinforced Accident Anticipation with Visual Explanation. ICCV 2021.
  • Niranjana Deshpande, Naveen Sharma, Qi Yu and Daniel Krutz: R-CASS: Using Algorithm Selection for Self-Adaptive Service Oriented Systems, ICWS 2021 (best paper award)
  • Weishi Shi, Qi Yu: Active Learning with Maximum Margin Sparse Gaussian Processes. AISTATS 2021: 406-414
  • Hitesh Sapkota, Yiming Ying, Feng Chen, Qi Yu: Distributionally Robust Optimization for Deep Kernel Multiple Instance Learning. AISTATS 2021: 2188-2196
  • Ervine Zheng, Qi Yu, Rui Li, Pengcheng Shi, Anne R. Haake: A Continual Learning Framework for Uncertainty-Aware Interactive Image Segmentation. AAAI 2021
  • Rui Liu, Chao Peng, Yunbo Zhang, Hannah Husarek, Qi Yu: A survey of immersive technologies and applications for industrial product development. Computers & Graphics 2021.


  • Ervine Zheng, Qi Yu, Rui Li, Pengcheng Shi, Anne R. Haake: Dynamic Fusion of Eye Movement Data and Verbal Narrations in Knowledge-rich Domains. NeurIPS 2020
  • Weishi Shi, Xujiang Zhao, Feng Chen, Qi Yu: Multifaceted Uncertainty Estimation for Label-Efficient Deep Learning. NeurIPS 2020
  • Peng-Nien Yin, KC Kishan, Shishi Wei, Qi Yu, Rui Li, Anne R Haake, Hiroshi Miyamoto, Feng Cui: Histopathological distinction of non-invasive and invasive bladder cancers using machine learning approaches, BMC medical informatics and decision making 2020.
  • Wentao Bao, Qi Yu, Yu Kong: Uncertainty-based traffic accident anticipation with spatio-temporal relational learning, Proceedings of the 28th ACM International Conference on Multimedia (ACM MM) 2020.
  • Wentao Bao, Qi Yu, Yu Kong: Object-Aware Centroid Voting for Monocular 3D Object Detection, 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2020.
  • Yasmine El-Glaly, Weishi Shi, Samuel Malachowsky, Qi Yu, Daniel E Krutz: Presenting and evaluating the impact of experiential learning in computing accessibility education, 2020 IEEE/ACM 42nd International Conference on Software Engineering: Software Engineering Education and Training (ICSE-SEET), 2020.
  • Moayad Alshangiti, Weishi Shi, Xumin Liu, Qi Yu: A Bayesian learning model for design-phase service mashup popularity prediction, Expert Systems with Applications (ESWA), 2020. 
  • Hongbing Wang, Jiajie Li, Qi Yu, Tianjing Hong, Jia Yan, Wei Zhao: Integrating recurrent neural networks and reinforcement learning for dynamic service composition, Future Generation Computer Systems 2020. 
  • Hongbing Wang, Xingguo Hu, Qi Yu, Mingzhu Gu, Wei Zhao, Jia Yan, Tianjing Hong: Integrating reinforcement learning and skyline computing for adaptive service composition, Information Sciences 2020. 




  • X. Liu, W. Shi, A. Kale, C. Ding, and Q. Yu, Statistical Learning of Domain-Specific Quality-of-Service Features from User Reviews, ACM Trans. Internet Techn. (TOIT), 17(2): 22:1-22:24, 2017.
  • H. Wang, L. Wang, Q. Yu, Z. Zheng, A. Bouguettaya, and M. Lyu Online Reliability Prediction via Motifs-based Dynamic Bayesian Networks for Service-Oriented Systems, IEEE Transactions on Software Engineering (TSE), 43(6): 556-579, 2017.
  • Bouguettaya, A., Singh, M., Huhns, M., Sheng, Q.Z., Dong, H., Yu, Q., Neiat, A.G., Mistry, S., Benatallah, B., Medjahed, B., Ouzzani, M., Casati, F., Liu, X., Wang, H., Georgakopoulos, D., Chen, L., Nepal, S., Malik, Z., Erradi, A., Wang, Y., Blake, B., Dustdar, S., Leymann, F., Papazoglou, M., A Service Computing Manifesto: The Next Ten Years, Communications of the ACM (CACM), 60(4): 64-72, 2017 (PDF).
  • H. Wang, P. Ma, Q. Yu, D. Yang, J. Li, and Huanhuan Fei Combining quantitative constraints with qualitative preferences for effective non-functional properties-aware service composition , J. Parallel Distrib. Comput., 100: 71-84 (2017).
  • H. Wang, L. Wang, X. Chen, Q. Wu, Q. Yu, X. Hu, Z. Zheng, and A. Bouguettaya Integrating Reinforcement Learning with Multi-Agent Techniques for Adaptive Service Composition , ACM Transactions on Autonomous and Adaptive Systems (TAAS), 2(2): 8:1-8:42 (2017).
  • X. Guo, R. Li, Q. Yu, and A. Haake Modeling Physicians' Utterances to Explore Diagnostic Decision-making , International Joint Conference on Artificial Intelligence (IJCAI), 2017: 3700-3706.
  • W. Shi, X. Liu, and Q. Yu, Correlation-Aware Multi-Label Active Learning for Web Service Tag Recommendation , IEEE International Conference on Web Services (ICWS), Honolulu, HI, 2017 (Research Track: 21%): 229-236.
  • H. Wang, Z. Yang, and Q. Yu, Online Reliability Prediction via Long Short Term Memory for Service-Oriented Systems , IEEE International Conference on Web Services (ICWS), Honolulu, HI, 2017 (Research Track: 21%): 81-88.
  • S. Peng, H. Wang, and Q. Yu, Estimation of Distribution with Restricted Boltzmann Machine for Adaptive Service Composition , IEEE International Conference on Web Services (ICWS), Honolulu, HI, 2017 (Research Track: 21%): 114-121.










  • A. Bouguettaya, D. Gracanin, Q. Yu, X. Zhang, X. Liu, and Z. Malik, WebSenior: A Digital Government Infrastructure for Senior Citizens. International Workshop on the Management of Business Processes in Government, co-located with 5th International Conference on Business Process Management (BPM 2007), Brisbane, Australia, September 2007


  • K. C. Tan, Q. Yu, and J. H. Ang, A dual-objective evolutionary algorithm for rules extraction in data miningComputational Optimization and Applications, Springer, vol. 34, pp. 273-294, 2006.
  • K. C. Tan, Q. Yu, J. H. Ang, and T. H. Lee, A coevolutionary algorithm for rules discovery in data mining, International Journal of Systems Science, Taylor & Francis, vol. 37, no. 12, pp. 835-864, 2006.
  • A. Bouguettaya, D. Gracanin, Q. Yu, X. Zhang, X. Liu, and Z. Malik, Ubiquitous Web Services for E-Government Social Services, AAAI Spring Symposium The Semantic Web meets eGovernment, Stanford University, California, USA, March 27-29, 2006.


  • K. C. Tan, Q. Yu, and T. H. Lee, A distributed coevolutionary classifier for knowledge discovery in data mining, IEEE Transactions on Systems, Man and Cybernetics: Part C (Applications and Reviews), IEEE, vol. 35, no. 2, pp. 131-142, 2005.
  • Q. Yu, K. C. Tan, and T. H. Lee, An evolutionary algorithm for rules discovery in data mining, Evolutionary Computation in Data Mining, A. Ghosh and L. C. Jain (Eds.), Physica-Verlag, Germany, pp. 101-123, 2005.


  • A. Bouguettaya, B. Medjahed, A. Rezgui, M. Ouzzani, X. Liu, and Q. Yu, WebDG - A platform for E-Government Web services. ER (Workshops) 2004: 553-565, November 2004.


  • K. C. Tan, Q. Yu, C. M. Heng, and T. H. Lee, Evolutionary Computing for Knowledge Discovery in Medical Diagnosis. Artificial Intelligence in Medicine, vol. 27, no. 2, pp. 129-154, 2003.