Yuhong is a Canada CIFAR AI Chair at Amii, a professor in the School of Computer Science at Carleton University, and a Canada Research Chair in Machine Learning.
Guo focuses on learning useful data representations and accurate classification models under various circumstances. Her research program is founded on three main directions: generalized transfer learning, learning from incomplete data, and learning from weakly supervised data. Her goal is to automate the learning process and reduce the dependence of learning systems on human guidance. Guo has also developed techniques that can learn accurate prediction models from weak supervision such as imprecise annotations collected via crowdsourcing from non-experts.
- Best Paper Award, TASK-CV Workshop at ECCV, 2020
- Chair Award for Outstanding Research, Dept of CIS, Temple University, 2012
- Outstanding Paper Award, AAAI, 2012
- Postdoctoral Fellowship, NSERC, 2008
- Distinguished Paper Award, IJCAI, 2005
Zhao, Z., Guo, Y., Shen, H., & Ye, J. (2020). Adaptive Object Detection with Dual Multi-Label Prediction.
Ye, M., & Guo, Y. (2019). Progressive ensemble networks for zero-shot recognition. In Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (pp. 11728-11736).
Guo, Y. (2017). Convex co-embedding for matrix completion with predictive side information. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 31, No. 1).
Xiao, M., & Guo, Y. (2014). Feature space independent semi-supervised domain adaptation via kernel matching. IEEE transactions on pattern analysis and machine intelligence, 37(1), 54-66.
Xiao, M., & Guo, Y. (2013). A novel two-step method for cross language representation learning. Advances in Neural Information Processing Systems, 26, 1259-1267.
CIFAR is a registered charitable organization supported by the governments of Canada, Alberta and Quebec, as well as foundations, individuals, corporations and Canadian and international partner organizations.