• Dayou Yu, Weishi Shi, Qi Yu: Discover-Then-Rank Unlabeled Support Vectors in the Dual Space for Multi-Class Active Learning. ICML 2023.
  • Ervine Zheng, Qi Yu: Evidential Interactive Learning for Medical Image Captioning. ICML 2023.
  • Dingrong Wang, Deep Shankar Pandey, Krishna Prasad Neupane, Zhiwei Yu, Ervine Zheng, Zhi Zheng, Qi Yu: Deep Temporal Sets with Evidential Reinforced Attentions for Unique Behavioral Pattern Discovery. ICML 2023.
  • Deep Shankar Pandey, Qi Yu: Learn to Accumulate Evidence from All Training Samples: Theory and Practice. ICML 2023.
  • Hitesh Sapkota, Qi Yu: Adaptive Robust Evidential Optimization For Open Set Detection from Imbalanced Data. ICLR 2023.
  • Jintang Li, Zhouxin Yu, Zulun Zhu, Liang Chen, Qi Yu, Zibin Zheng, Sheng Tian, Ruofan Wu, Changhua Meng, "Scaling Up Dynamic Graph Representation Learning via Spiking Neural Networks" AAAI 2023 (oral).
  • Ervine Zheng, Qi Yu, and Zhi Zheng: Sparse Maximum Margin Learning From Multimodal Human Behavioral Patterns, AAAI 2023 (oral). 
  • Dayou Yu, Weishi Shi, and Qi Yu: STARS: Spatial-Temporal Active Re-Sampling for Label-Efficient Learning from Noisy Annotations. AAAI 2023.
  • Deep Pandey and Qi Yu: Evidential Conditional Neural Processes. AAAI 2023 (oral).
  • Ervine Zheng, Qi Yu, Rui Li, Pengcheng Shi, Anne R. Haake: Knowledge Acquisition for Human-In-The-Loop Image Captioning. AISTATS 2023


  • Hitesh Sapkota, Qi Yu: Balancing Bias and Variance for Active Weakly Supervised Learning. KDD 2022.
  • Zhu, Zulun, Jiaying Peng, Jintang Li, Liang Chen, Qi Yu, and Siqiang Luo. "Spiking Graph Convolutional Networks." IJCAI 2022 (long oral). 
  • Krishna Neupane, Ervine Zheng, Yu Kong, and Qi Yu: A Dynamic Meta-Learning Model for Time-Sensitive Cold-Start Recommendations, AAAI 2022. 
  • Wentao Bao, Qi Yu, Yu Kong: OpenTAL: Towards Open Set Temporal Action Localization. CVPR 2022 (oral).
  • Deep Pandey and Qi Yu: Multidimensional Belief Quantification for Label-Efficient Meta-Learning. CVPR 2022.
  • Hitesh Sapkota and Qi Yu: Bayesian Nonparametric Submodular Video Partition for Robust Anomaly Detection. CVPR 2022
  • Ervine Zheng, Qi Yu, Rui Li, Pengcheng Shi, Anne R. Haake: Dual-Level Adaptive Information Filtering for Interactive Image Segmentation. AISTATS 2022
  • Yuansheng Zhu, Wentao Bao, Qi Yu: Towards Open Set Video Anomaly Detection. ECCV  2022
  • Moayad Alshangiti, Weishi Shi, Eduardo Lima, Xumin Liu, Qi Yu: Hierarchical Bayesian multi-kernel learning for integrated classification and summarization of app reviews. FSE 2022.
  • Niranjana Deshpande, Naveen Sharma, Qi Yu, Daniel E. Krutz: Online Learning Using Incomplete Execution Data for Self-Adaptive Service-Oriented Systems. ICWS 2022.


  • 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.