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Publications of Author

1. Yan Liu, Yiming Yang, Jaime G. Carbonell
   Boosting to correct inductive bias in text classification. [Citation Graph (0, 0)][DBLP]
   CIKM, 2002, pp:348-355 [Conf]
   
2. Yan Liu, Jaime G. Carbonell, Rong Jin
   A New Pairwise Ensemble Approach for Text Classification. [Citation Graph (0, 0)][DBLP]
   ECML, 2003, pp:277-288 [Conf]
   
3. John D. Lafferty, Xiaojin Zhu, Yan Liu
   Kernel conditional random fields: representation and clique selection. [Citation Graph (0, 0)][DBLP]
   ICML, 2004, pp:- [Conf]
   
4. Yan Liu, Eric P. Xing, Jaime G. Carbonell
   Predicting protein folds with structural repeats using a chain graph model. [Citation Graph (0, 0)][DBLP]
   ICML, 2005, pp:513-520 [Conf]
   
5. Jingrui He, Jaime G. Carbonell, Yan Liu
   Graph-Based Semi-Supervised Learning as a Generative Model. [Citation Graph (0, 0)][DBLP]
   IJCAI, 2007, pp:2492-2497 [Conf]
   
6. Katharina Probst, Rayid Ghani, Marko Krema, Andrew E. Fano, Yan Liu
   Semi-Supervised Learning of Attribute-Value Pairs from Product Descriptions. [Citation Graph (0, 0)][DBLP]
   IJCAI, 2007, pp:2838-2843 [Conf]
   
7. Yan Liu, Jaime G. Carbonell, Vanathi Gopalakrishnan, Peter Weigele
   Protein Quaternary Fold Recognition Using Conditional Graphical Models. [Citation Graph (0, 0)][DBLP]
   IJCAI, 2007, pp:937-945 [Conf]
   
8. Yan Liu, Jaime G. Carbonell, Peter Weigele, Vanathi Gopalakrishnan
   Segmentation Conditional Random Fields (SCRFs): A New Approach for Protein Fold Recognition. [Citation Graph (0, 0)][DBLP]
   RECOMB, 2005, pp:408-422 [Conf]
   
9. Yan Liu, Jaime G. Carbonell, Judith Klein-Seetharaman, Vanathi Gopalakrishnan
   Context sensitive vocabulary and its application in protein secondary structure prediction. [Citation Graph (0, 0)][DBLP]
   SIGIR, 2004, pp:538-539 [Conf]
   
10. Yan Liu, Jaime G. Carbonell, Judith Klein-Seetharaman, Vanathi Gopalakrishnan
    Comparison of probabilistic combination methods for protein secondary structure prediction. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2004, v:20, n:17, pp:3099-3107 [Journal]
    
11. Andrew Arnold, Yan Liu, Naoki Abe
    Temporal causal modeling with graphical granger methods. [Citation Graph (0, 0)][DBLP]
    KDD, 2007, pp:66-75 [Conf]
    
12. Jun Yang, Yan Liu, Eric P. Xing, Alexander G. Hauptmann
    Harmonium Models for Semantic Video Representation and Classification. [Citation Graph (0, 0)][DBLP]
    SDM, 2007, pp:- [Conf]
    
13. Katharina Probst, Rayid Ghani, Marko Krema, Andy Fano, Yan Liu
    Extracting and Using Attribute-Value Pairs from Product Descriptions on the Web. [Citation Graph (0, 0)][DBLP]
    WebMine, 2006, pp:41-60 [Conf]
    
14. 
    Graph-based transfer learning. [Citation Graph (, )][DBLP]
    
    
15. 
    Graph-Based Rare Category Detection. [Citation Graph (, )][DBLP]
    
    
16. 
    Undirected Graphical Models for Video Analysis and Classification. [Citation Graph (, )][DBLP]
    
    
17. 
    Who is the expert? Analyzing gaze data to predict expertise level in collaborative applications. [Citation Graph (, )][DBLP]
    
    
18. 
    Topic-link LDA: joint models of topic and author community. [Citation Graph (, )][DBLP]
    
    
19. 
    Learning Temporal Causal Graphs for Relational Time-Series Analysis. [Citation Graph (, )][DBLP]
    
    
20. 
    Grouped graphical Granger modeling methods for temporal causal modeling. [Citation Graph (, )][DBLP]
    
    
21. 
    Looking for Great Ideas: Analyzing the Innovation Jam. [Citation Graph (, )][DBLP]
    
    
22. 
    Learning dynamic temporal graphs for oil-production equipment monitoring system. [Citation Graph (, )][DBLP]
    
    
23. 
    Spatial-temporal causal modeling for climate change attribution. [Citation Graph (, )][DBLP]
    
    
24. 
    Proximity-Based Anomaly Detection Using Sparse Structure Learning. [Citation Graph (, )][DBLP]
    
    
25. 
    Grouped graphical Granger modeling for gene expression regulatory networks discovery. [Citation Graph (, )][DBLP]