Xiaotong Shen's  Home Page

Xiaotong Shen

Professor
School of Statistics
University of Minnesota 
381 Ford Hall
Minneapolis, MN 55455

Office Phone:  (612) 624-7098
E-mail:   xshen@stat.umn.edu

   My areas of interest include likelihood-based inference, semiparametric and nonparametric models, classification, model selection and averaging, and nonparametric Bayes. My current research effort is mainly devoted to the further development of psi-learning as well as margin-based classification techniques. The targeted application areas include cancer genomics classification and multimedia compression.

Manuscripts:  (including those supported by NSF under Grant DMS-0604394 and Grant IIS-0328802 on multiple object tracking, in collaboration with Professor Yuan F. Zheng's group at OSU for this project)

.  Wang, J., Shen, X., and Pan, W. (2009). Large margin hierarchical classification with multiple paths. Journal of American Statistical Association. To appear.
.  Pan, W., Xie, B., and Shen, X. (2009). Incorporating predictor network in penalized regression with application to microarray data. Biometrics. To appear.
.  Zhu, Y., Shen, X., and Pan, W. (2008). Network-based support vector machines for classification of microarray samples. The proceeding of The Seventh Asia Pacific Bioinformatics Conference, Beijing, China, January 2009. BMC Bioinfortmatics. 10, S21.
.  Wang, J., Shen, X., and Pan., W. (2009). Efficient large margin semisupervised learning. Journal of Machine Learning Research. 10, 719-742.
.  Wu, S., Shen, X., and Geyer, C. (2008). Adaptive regularization through entire solution surface. Biometrika. To appear.
.  Xie, B., Pan, W., and Shen, X. (2008). Variable selection in penalized model-based clustering via regularization on grouped parameters. Biometrics. To appear.
.  Wang, J., Shen, X., Liu, Y.F. (2008). Probability estimation for large margin classifiers. Biometrika. 95, 149-167.
.  Xie, B., Pan, W., and Shen, X. (2008). Penalized model-based clustering with cluster-specific diagonal covariances and grouped variables. Electrical Journal of Statistics. 2, 168-212.
.  Liu, Y., Zheng, Y., and Shen, X. (2008). Applying the multi-category learning to multiple video object extraction. Pattern Recognition. 9, 2777-2788.
.  Wang, J., Shen, X., and Pan, W. (2007). On transductive support vector machines. Contemporary Mathematics. 443, 7-19.
.  Shen, X., and Wang, L. (2007). Generalization error for multi-class margin classification. Electrical Journal of Statistics. 1, 307-330.
.  Pan, W., and Shen, X. (2007). Semisupervised learning via constraints. Contemporary Mathematics. 443, 193-204.
.  Li, Q., Shen, X., and Pearl, D.  (2007). Bayesian modeling of Individual's hepatotoxicity. Statistics in Medicine. 26, 3591-3611.
.  Wang, J., Shen, Z., and Pan, W.  (2007). On transductive support vector machines.  To appear.
.  Wang, J., and Shen, X.  (2007).  Large margin semi-supervised learning. Journal of Machine Learning Reserach, 8, 1867-1891.
.  Wang, L., and Shen, X. (2007). On L1-norm multi-class support vector machines: methodology and theory.  Journal of the American Statistical Association. 102, 595-602.
.   Pan, W., and Shen, X.  (2007).  Penalized model-based clustering with application to variable selection. Journal of Machine Learning Research, 8, 1145-1164.
.  Shen, X., and Wang, J. (2006). Discussion of 2004 IMS Medallion Lecture: ``Local Rademacher complexities
  and oracle inequalities in risk minimization'' The Annals of Statistics. In press.
.  Wang, L., Shen, X., and Zheng, Y. (2006). On L1-norm multicategory support vector machines. Proceedings of the 2006 International Conference on Machine Learning and Applications in Orlando, Florida, 83-88.
.  Pan, W., Shen, X., Jiang, A. and Hebbel, R.  (2006). Semisupervised learning via penalized mixture model with application to microarray sample classification. Bioinformatics.  22(19), 2388-2395.
. Wang, J., and Shen, X.  (2006). Estimation of generalization error: fixed and random inputs. Statistica Sinica, 16, 569-588. Special issue on machine learning and data mining.
. Wang, L., and Shen, X.  (2006). Multicategory support vector machines, feature selection and solution path. Statistica Sinica, 16, 617-634. Special issue on machine learning and data mining.
.  Liu, YF., and Shen, X. (2006). Multicategory SVM and psi-learning-methodology and theory.
  Journal of the American Statistical Association, 101, 500-509.
. Shen, X., and Huang, H. (2006). Optimal model assessment, selection and combination. Journal of the American
  Statistical Association, 101, 554-568.
. Liu, S., Shen, X., and Wong, W.  (2005). Computational developments of psi-learning.   Proceedings of
 The Fifth SIAM International Conference on Data Mining, Newport, California, April, 2005, pp. 1-12.

Object Tracking:

. Liu, Y., and Zheng, Y. (2006). Soft SVM and its application to video object extraction.
. Liu, Y., Zheng, Y. and Shen, X.  (2006). A new class of multi-category learning for multiple object extraction.
. Liu, Y., and Zheng, Y. (2006). Minimum enclosing and maximum excluding machines for pattern description and discrimination.
. Liu, Y., and Zheng, Y.  (2005). One-against-all multiclass SVM classification using reliability measures.
. Liu, Y., and Zheng, Y.  (2005). FS_SFS: A novel feature selection method for support vector machines.

Conferences/Workshops:

.  , IMS Annual Meeting, Joint Statistical Meetings, Washington, D.C, 2009.
.  IMS-China, HangZhou, 2008.
.  NSF sponsorted International Conference on Machine Learning and Data Mining, Beijing, China, 2008.
.  NSF sponsored  International Conference on Bioinformatics, Huang Zhou, China, 2007.
.  AISTATS (2007).
.  AMS-IMS-SIAM Summer Research Conference: Machine Learning, Statistics, and Discovery II, Snowbird, Utah, 2006.
.  AMS-IMS-SIAM Summer Research Conference: Machine Learning, Statistics, and Discovery I, Snowbird, Utah, 2003.