Boyu Wang

I am primarily working on transfer/multitask/lifelong learning, which is essential for building an intelligent agent in the real world with any amount of versatility. On the application side, I am interested in applying these techniques to brain signal analysis to solve the problems raised in neuroscience, biomedical engineering, neural engineering, healthcare, etc. I am also interested in applying these machine learning techniques to other real-world applications, such as smart grids and financial optimization.

1. 1. Yujiao Hao, Rong Zheng, and Boyu Wang. Invariant Feature Learning for Sensor-Based Human Activity Recognition. To appear in IEEE Transactions on Mobile Computing (TMC).
   2. Roozbeh Razavi-Far, Maryam Farajzadeh-Zanajni, Boyu Wang, Mehrdad Saif, and Shiladitya Chakrabarti. Imputation-based Ensemble Techniques for Class Imbalance Learning. To appear in IEEE Transactions on Knowledge and Data Engineering (TKDE).
   3. Boyu Wang, Chi Man Wong, Zhao Kang, Feng Liu, Changjian Shui, Feng Wan, and C. L. Philip Chen. Common Spatial Pattern Reformulated for Regularizations in Brain-Computer Interfaces. . To appear in IEEE Transactions on Cybernetics (TCYB).
   4. Fan Zhou, Zhuqing Jiang, Changjian Shui, Boyu Wang*, and Brahim Chaib-draa*. Domain Generalization via Optimal Transport with Metric Similarity Learning. To appear in Neurocomputing
   5. Fan Zhou, Changjian Shui, Mahdieh Abbasi, Louis-Émile Robitaille, Boyu Wang, and Christian Gagné. Task Similarity Estimation through Adversarial Multitask Neural Network. IEEE Transactions on Neural Networks and Learning Systems (TNNLS), vol. 32, no. 2, pp. 466-480, 2021.
   6. Fan Zhou, Brahim Chaib-draa and Boyu Wang. Multi-task Learning by Leveraging the Semantic Information. AAAI Conference on Artificial Intelligence (AAAI), 2021.
   7. Jingmei Yang, Feng Liu, Boyu Wang, Chaoyang Chen, Timothy Church, Lee Dukes, and Jeffrey O. Smith. Blood Pressure States Transition Inference Based on Multi-state Markov Model. IEEE Journal of Biomedical and Health Informatics (JBHI), vol. 25, no. 1, pp. 237-246, 2021.
   8. Juncheng Lv, Zhao Kang, Boyu Wang, Luping Ji, and Zenglin Xu. Multi-view Subspace Clustering via Partition Fusion. Information Sciences, vol. 560, pp. 410-423, 2021.
   9. Jorge Mendez, Boyu Wang, and Eric Eaton. Lifelong Policy Gradient Learning of Factored Policies for Faster Training Without Forgetting. Neural Information Processing Systems (NeurIPS), pp. 14398-14409, 2020.
   10. Changjian Shui, Fan Zhou, Christian Gagné, and Boyu Wang. Deep Active Learning: Unified and Principled Method for Query and Training. International Conference on Artificial Intelligence and Statistics (AISTATS), pp. 1308-1318, 2020.
   11. Jiahao Xie, Zebang Shen, Chao Zhang, Boyu Wang, and Hui Qian. Efficient Projection-Free Online Methods with Stochastic Recursive Gradient. AAAI Conference on Artificial Intelligence (AAAI), pp. 6446-6453, 2020.
   12. Chi Man Wong, Boyu Wang, Ze Wang, Ka Fai Lao, Agostinho Rosa, Feng Wan. Spatial Filtering in SSVEP-based BCIs: Unified Framework and New Improvements. IEEE Transactions on Biomedical Engineering (TBME), vol. 67, no. 11, pp. 3057-3072, 2020.
   13. Chi Man Wong, Ze Wang, Boyu Wang, Ka Fai Lao, Agostinho Rosa, Peng Xu, Tzyy-Ping Jung, C. L. Philip Chen, and Feng Wan. Inter- and Intra-Subject Transfer Reduces Calibration Effort for High-Speed SSVEP-based BCIs. IEEE Transactions on Neural Systems and Rehabilitation Engineering (TNSRE), vol. 28, no. 10, pp. 2123-2135, 2020.
   14. Chi Man Wong, Feng Wan, Boyu Wang, Ze Wang, Wenya Nan, Ka Fai Lao, Peng Un Mak, Mang I Vai, and Agostinho Rosa. Learning Across Multi-Stimulus Enhances Target Recognition Methods in SSVEP-based BCIs. Journal of Neural Engineering, vol. 17, no. 1, 016026, 2020.
   15. Di Wu, Boyu Wang, Doina Precup, and Benoit Boulet. Multiple Kernel Learning based Transfer Regression for Electric Load Forecasting. IEEE Transactions on Smart Grid (TSG), vol. 11, no. 2, pp. 1183-1192, 2020.
   16. Boyu Wang, Jorge Mendez, Ming Bo Cai, and Eric Eaton. Transfer Learning via Minimizing the Performance Gap Between Domains. Neural Information Processing Systems (NeurIPS), pp. 10644-10654, 2019.
   17. Boyu Wang, Hejia Zhang, Peng Liu, Zebang Shen, and Joelle Pineau. Multitask Metric Learning: Theory and Algorithm. International Conference on Artificial Intelligence and Statistics (AISTATS), pp. 3362-3371, 2019.
   18. Boyu Wang, James Antony, Sarah Lurie, Paula Brooks, Ken Paller, and Kenneth Norman. Targeted Memory Reactivation during Sleep Elicits Neural Signals Related to Learning Content. Journal of Neuroscience, vol. 39, no. 34, pp. 6728-6736, 2019.

• 2021 Winter: CS2035 - Data Analysis and Visualization

•  2021 Winter: CS4442/CS9542 - Artificial Intelligence II

•  2020 Fall: CS9875 - Theoretical Machine Learning

Knowledge Transfer in Artificial Intelligence Systems, supported by the NSERC Discovery Grants program