Research

Our primary area of research is machine learning, with an emphasis on learning useful data representations, and learning accurate classifiers under various circumstances. Bioinformatics, natural language processing and computer vision are our application areas. Our research covers the following topics:

• Active learning
• Learning graphical models
• Multi-label learning, multi-view learning
• Domain adaptation and transfer learning
• Dimensionality reduction and feature selection
• Multiple Instance learning
• Optimization
• Bioinformatics

Students

• Min Xiao (Ph.D. Student)
• Xin Li (Ph.D. Student)
• Wei Xue (Ph.D. Student)
• Richard Hart (M.S. Student)

Papers

• V. Ouzienko, Yuhong Guo, and Z. Obradovic (2011),  ``A Decoupled Exponential Random Graph Model for Prediction of Structure and Attributes in Temporal Social Networks". In press. Statistical Analysis and Data Mining Journal.
  
  
• L. Lan, N. Djuric, Yuhong Guo and S. Vucetic (2011),  ``Protein Function Prediction by Integrating Different Data Sources". AFP/CAFA 2011. 
  
  
• Y. Wang, Yuhong Guo and J. Wu (2011),  ``Making Many People Happy: Greedy Solutions for Content Distribution". In Proceedings of the International Conference on Parallel Processing (ICPP-11).  
  
  
• Yuhong Guo and D. Schuurmans (2011),  ``Adaptive Large Margin Training for Multilabel Classification". In Proceedings of the Twenty-Fifth National Conference on Artificial Intelligence (AAAI-11). 
  
  
• Yuhong Guo and S. Gu (2011),  ``Multi-label Classification using Conditional Dependency Networks''. In Proceedings of the International Joint Conference on Artificial Intelligence (IJCAI-11). 
  
  
• Yuhong Guo (2010),  ``Active Instance Sampling via Matrix Partition''. In Proceedings of Advances in Neural Information Processing Systems (NIPS-10).  [pdf]
  
  
• Y. Shi, Yuhong Guo, G. Lin, and D. Schuurmans (2010),  ``Kernel-based Gene Regulatory Network Inference''. In Proceedings of the LSS Computational Systems Bioinformatics Conference (CSB-10).  [pdf]
  
  
• K. Rsitovski, D. Das, V. Ouzienko, Yuhong Guo, and Z. Obradovic (2010),  ``Regression Learning with Multiple Noisy Oracles''. In Proceedings of European Conference on Artificail Intelligence (ECAI-10).
  
  
• V. Ouzienko, Yuhong Guo, and Z. Obradovic (2010),  ``Prediction of Attributes and Links in Temporal Social Networks''. In Proceedings of European Conference on Artificail Intelligence (ECAI-10).
  
  
• Yuhong Guo (2009),  ``Supervised Exponential Family PCA via Global Optimization''. [pdf]
  
  
• Yuhong Guo (2009),  ``Max-Margin Multiple-Instance Learning via Semidefinite Programming''. In Advances in Machine Learning, Asian Conference on Machine Learning (ACML-09).  [pdf]
  
  
• Yuhong Guo and Dale Schuurmans (2009),  ``A Reformulation of Support Vector Machines for General Confidence Functions". In Advances in Machine Learning, Asian Conference on Machine Learning (ACML-09).  [pdf] 
  
  
• Yuhong Guo (2008),  ``Supervised Exponential Family Principal Component Analysis via Convex Optimization''. In Proceedings of Advances in Neural Information Processing Systems (NIPS-08). [pdf]
  
  
• Yuhong Guo and Dale Schuurmans (2008),  ``Efficient Global Optimization for Exponential Family PCA and Low-rank Matrix Factorization". In Allerton Conference on Communication, Control, and Computing (Allerton-08). [pdf] 
  
  
• Yuhong Guo (2007),  ``Learning Bayesian Networks from Data: Structure Optimization and Parameter Estimation".  Ph.D. Thesis, Department of Computing Science, University of Alberta.
  
  
• Yuhong Guo and Dale Schuurmans (2007),  ``Convex Relaxations of Latent Variable Training''. In Proceedings of Advances in Neural Information Processing Systems (NIPS-07).  [pdf]
  
  
• Yuhong Guo and Dale Schuurmans (2007),  ``Discriminative Batch Mode Active Learning''. In Proceedings of Advances in Neural Information Processing Systems (NIPS-07).  [pdf]
  
  
• Yuhong Guo and Dale Schuurmans (2007),  ``Learning Gene Regulatory Networks via Globally Regularized Risk Minimization''.  In Proceedings of the Fifth Annual RECOMB Satellite Workshop on Comparative Genomics (RECOMB-CG'07).  [pdf]
  
  
• Yuhong Guo and Russ Greiner (2007),  ``Optimistic Active Learning using Mutual Information''.  In Proceedings of the Twentieth International Joint Conference on Artificial Intelligence (IJCAI-07).  [pdf]
  
  
• Yuhong Guo and Dale Schuurmans (2006),  ``Convex Structure Learning for Bayesian Networks: Polynomial Feature Selection and Approximate Ordering''.  In Proceedings of the Twenty-Second Conference on Uncertainty in Artificial Intelligence (UAI-06).  [pdf]
  
  
• Dale Schuurmans, Finnegan Southey, Dana Wilkinson and Yuhong Guo (2006),   ``Metric-based Approaches for Semi-supervised Regression and Classification''.   In O. Chapelle, B. Schoelkopf, and A. Zien, editors, Semi-Supervised Learning, MIT Press. [pdf]
  
  
• Yuhong Guo, Russ Greiner and Dale Schuurmans (2005),   ``Learning Coordination Classifiers".   In Proceedings of the Nineteenth International Joint Conference on Artificial Intelligence (IJCAI-05).  (Distinguished Paper) [pdf]
  
  
• Yuhong Guo, Dana Wilkinson and Dale Schuurmans (2005),  ``Maximum Margin Bayesian Networks''.  In Proceedings of the Twenty-First Conference on Uncertainty in Artificial Intelligence (UAI-05).  [pdf]
  
  
• Yuhong Guo and Russ Greiner (2005),  ``Discriminative Model Selection for Belief Net Structures".  In Proceedings of the Twentieth National Conference on Artificial Intelligence (AAAI-05).  [pdf]