PUBLICATION

2008

• S. Kim and E. P. Xing, Sparse Feature Learning in High-Dimensional Space via Block Regularized Regression., Proceedings of the 24th International Conference on Conference on Uncertainty in Artificial Intelligence (UAI 2008).

• J. Zhu, E. P. Xing, B. Zhang, Laplace Maximum Margin Markov Networks, Proceedings of the 25th International Conference on Machine Learning (ICML 2008). (A longer version is available in CMU-MLD Technical Report 08-104 titled Maximum Entropy Discrimation Markov Network.)

• A. Martins, M. Figueiredo, P. Aguiar, N. A. Smith and E. P. Xing, Nonextensive Entropic Kernels, Proceedings of the 25th International Conference on Machine Learning (ICML 2008). (A longer version is available soon in CMU-MLD Technical Report 08-106 with the same title.)

• S. Shringarpure and E. P. Xing, mStruct: A New Admixture Model for Inference of Population Structure in Light of Both Genetic Admixing and Allele Mutations, Proceedings of the 25th International Conference on Machine Learning (ICML 2008). (A longer version is available soon in CMU-MLD Technical Report 08-105 with the same title.)

• P. Ray, S. Shringarpure, M. Kolar and E. P. Xing, CSMET: Comparative Genomic Motif Detection via Multi-Resolution Phylogenetic Shadowing, PLoS Computational Biology (2008), to appear.

• E. Airodi, D. Blei, S. Fienberg and E. P. Xing, Mixed Membership Stochastic Blockmodels, Journal of Machine Learning Research (2008), to appear.

• A.Ahmed and E. P. Xing, Dynamic Non-Parametric Mixture Models and the Recurrent Chinese Restaurant Process, Proceedings of The Eighth SIAM International Conference on Data Mining (SDM2008).

• Z. Guo, Z. Zhang, E. P. Xing and C. Faloutsos, Semi-supervised Learning Based on Semiparametric Regularization, Proceedings of The Eighth SIAM International Conference on Data Mining (SDM2008).

• T. Lin, P. Ray, G. K. Sandve, S. Uguroglu, and E. P. Xing, BayCis: a Bayesian hierarchical HMM for cis-regulatory module decoding in metazoan genomes, Proceedings of the Twelfth Annual International Conference on Research in Computational Molecular Biology (RECOMB2008).

• J. Yang, R. Yan, Y. Liu, and E. P. Xing, Harmonium Models for Video Classification, Journal of Statistical Analysis and Data Mining, Vol 1, issue 1, p23-37, 2008.

• W. Wu and E. P. Xing, A Survey of cDNA Microarray Normalization and a Comparison by k-NN Classification, in Methods in Microarray Normalization (Ed. S. Phillip), CRC Press. p81-120, 2008.

2007

• B. Zhao and E. P. Xing, HM-BiTAM: Bilingual Topic Exploration, Word Alignment, and Translation, Advances in Neural Information Processing Systems 20 (NIPS2007).

• L. Chang, N. Pollard, T. Michell and E. P. Xing, Feature selection for grasp recognition from optical markers, Proceedings of the 2007 IEEE/RSJ Intl. Conference on Intelligent Robots and Systems, 2007 (IROS2007).

• E. P. Xing and K. Sohn, A Nonparametric Bayesian Approach for Haplotype Reconstruction from Single and Multi-Population Data, CMU-MLD Technical Report 07-107.

• K. Sohn and E. P. Xing, Spectrum: Joint Bayesian Inference of Population Structure and Recombination Event, The Fifteenth International Conference on Intelligence Systems for Molecular Biology (ISMB 2007).

• F. Guo, S. Hanneke, W. Fu and E. P. Xing, Recovering Temporally Rewiring Networks: A model-based approach, Proceedings of the 24th International Conference on Machine Learning (ICML 2007).

• L. Gu, E. P. Xing, and T. Kanade, Learning GMRF Structures for Spatial Priors, Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR 2007).

• Z. Guo, Z. Zhang, E. P. Xing, C. Faloutsos, Enhanced Max Margin Learning on Multimodal Data Mining in a Multimedia Database, The Thirteenth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2007).

• Z. Guo, Z. Zhang, E. P. Xing, C. Faloutsos, A Max Margin Framework on Image Annotation and Multimodal Image Retrieval, IEEE International Conference on Multimedia & Expo (ICME 2007).

• E. P. Xing, M. Jordan, and R. Roded, Bayesian Haplotype Inference via the Dirichlet Process, Journal of Computational Biology, Volume 14, Number 3, Pp. 267-284, 2007.

• E. P. Xing and K. Sohn, Hidden Markov Dirichlet Process: Modeling Genetic Recombination in Open Ancestral Space,  Bayesian Analysis, Volunn 2, Number 2, 2007. A synopsis of this paper also appears in Bayesian Statistics 8, the Proc. Valencia / ISBA 8th World Meeting on Bayesian Statistics.
  

• A. Ahmed and E. P. Xing, On Tight Approximate Inference of Logistic-Normal Admixture Model, Proceedings of the Eleventh International Conference on Artifical Intelligence and Statistics (AISTAT 2007).

• J. Yang, Y. Liu, E. P. Xing and A. Hauptmann, Harmonium-Based Models for Semantic Video Representation and Classification , Proceedings of The Seventh SIAM International Conference on Data Mining (SDM 2007). (Recipient of the BEST PAPER Award)

• H. Kamisetty, E. P. Xing, C. J. Langmead, Free Energy Estimates of All-atom Protein Structures Using Generalized Belief Propagation, The Eleventh Annual International Conference on Research in Computational Molecular Biology (RECOMB 2007).

• Y. Shi, F. Guo, W. Wu and E. P. Xing, GIMscan: A New Statistical Method for Analyzing Whole-Genome Array CGH Data, The Eleventh Annual International Conference on Research in Computational Molecular Biology (RECOMB 2007).

2006

• F. Guo, W. Fu, Y. Shi and E. P. Xing, Reverse engineering temporally rewiring gene networks, The NIPS workshop on New Problems and Methods in Computational Biology (NIPS2006).

• F. Li, Y. Yang and E. P. Xing, Inferring regulatory networks using a hierarchical Bayesian graphical Gaussian model, CMU-MLD Technical Report 06-117.

• Y. Shi, F. Guo, W. Wu and E. P. Xing, GIMscan: A New Statistical Method for Analyzing Whole-Genome Array CGH Data, CMU-MLD Technical Report 06-115.

• E. P. Xing and K. Sohn, A New Nonparametric Bayesian Model for Genetic Inference in Open Ancestral Space, CMU-MLD Technical Report 06-111.

• K. Sohn and E. P. Xing, Hidden Markov Dirichlet Process: Modeling Genetic Recombination in Open Ancestral Space, Advances in Neural Information Processing Systems 19 (NIPS2006). (full oral presentation, top 3%)
  

• J-Y Pan, A. Balan, E.P. Xing, A. Traina and C. Faloutsos, Automatic Mining of Fruit Fly Embryo Images, The Twelfth ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2006).

• B. Zhao and E.P Xing, BiTAM: Bilingual Topic AdMixture Models for Word Alignment, The joint conference of the International Committee on Computational Linguistics and the Association for Computational Linguistics (ACL 2006).

• T. Lin, E.W. Myers and E.P. Xing, Interpreting Anonymous DNA Samples From Mass Disasters --- probabilistic forensic inference using genetic markers, Bioinformatics 22(14):e298-e306. (special issue for The Fourteenth International Conference on Intelligence Systems for Molecular Biology (ISMB 2006)).

• E.P. Xing, K. Sohn, M.I. Jordan and Y.W. Teh, Bayesian Multi-Population Haplotype Inference via a Hierarchical Dirichlet Process Mixture, Proceedings of the 23rd International Conference on Machine Learning (ICML 2006).

• S. Hanneke and E.P Xing, Discrete Temporal Models of Social Networks, In proceedings of the Workshop on Statistical Network Analysis, the 23rd International Conference on Machine Learning (ICML-SNA 2006).

• G. Fan and E.P Xing, Bayesian Exponential Family Harmoniums, CMU-MLD Technical Report 06-103.

• E.M. Airoldi, D.M. Blei, S.E. Fienberg, E.P. Xing, Latent mixed-membership allocation models of relational and multivariate attribute data, Valencia & ISBA Joint World Meeting on Bayesian Statistics (2006).

• E.M Airodi, D.M. Blei, E.P. Xing and S.E. Fienberg, Mixed membership stochastic block models for relational data, with applications to protein-protein interactions, Proceedings of International Biometric Society-ENAR Annual Meetings (2006). (Recipient of the John Van Ryzin Award).

        2005

• E.P. Xing, On Topic Evolution. CMU-CALD Technical Report 05-115.
  

• E.P. Xing, Dynamic Nonparametric Bayesian Models and the Birth-Death Process. CMU-CALD Technical Report 05-114.

• W. Wu, N. Dave, G.C. Tseng, T. Richards, E.P. Xing, and N. Kaminsky, Comparison of normalization methods for CodeLink Bioarray data. BMC Bioinformatics 2005, 6:309 (28 Dec, 2005).

• F. Li, Y. Yang and E. P. Xing, From Lasso regression to Feature vector machine, Advances in Neural Information Processing Systems 18 (NIPS2005).

• W. Wu, E.P. Xing, C. Myers, I. S. Mian and M. J. Bissell, Evaluation of normalization methods for cDNA microarray data by k-NN classification. BMC Bioinformatics 2005, 6:191 (26 Jul, 2005) .

• E. Airoldi, D. Blei, E.P. Xing and S. Fienberg, A Latent Mixed Membership Model for Relational Data. Workshop on Link Discovery: Issues, Approaches and Applications (LinkKDD-2005).

• E.P. Xing, R. Yan and A. G. Hauptmann, Mining Associated Text and Images with Dual-Wing Harmoniums. Uncertainty in Artificial Intelligence, 2005 (UAI2005).
  

• Y. Liu, E.P. Xing, and J. Carbonell, Predicting Protein Folds with Structural Repeats Using a Chain Graph Model. Proceedings of the 22st International Conference on Machine Learning (ICML2005).

• B. Zhao, E.P Xing, and A. Waibel, Bilingual Word Spectral Clustering for Statistical Machine Translation. ACL-Workshop-WPT, Ann Arbor MI, 2005.

        2004

• E.P. Xing, Probabilistic graphical models and algorithms for genomic analysis. Ph.D. Thesis, University of California, Berkeley, July 2004.

• E.P. Xing, M.I Jordan and S. Russell, Graph partition strategies for generalized mean field inference. Uncertainty in Artificial Intelligence 20 (UAI2004),  (eds. Meek and Halpern) AUAI Press, 602-610, 2004. [ps]

• E.P. Xing, R. Sharan and M.I Jordan, Bayesian Haplotype Inference via the Dirichlet Process. Proceedings of the 21st International Conference on Machine Learning (ICML2004),  (eds. Greiner and Schuurmans), ACM Press, 879-886, [ps]. An earlier version of this paper also appeared as a book chapter in Lecture Notes in Bioinformatics, Special issue for 2nd RECOMB Satellite Workshop on Computational Methods for SNPs and Haplotypes, 2004. (ps).

• E.P. Xing and R. M. Karp, MotifPrototyper: a profile Bayesian model for motif family. Proc. Natl. Acad. Sci. vol. 101, no. 29, 10523-10528, 2004. [pnas site].

        2003

• E.P. Xing, M.I. Jordan, and S. Russell, A generalized mean field algorithm for variational inference in exponential families, Uncertainty in Artificial Intelligence (UAI2003),  (eds. Meek and Kjaelff) Morgan Kaufmann Publishers, 583-591}, 2003. This paper was awarded a Runner-up Best Student Paper Award. (ps, code.) 

• E.P. Xing and M.I Jordan, On semidefinite relaxation for normalized k-cut and connections to spectral clustering. Technical Report CSD-03-1265, Division of Computer Science, University of California, Berkeley, 2003. (ps)

• E.P. Xing, W. Wu, M.I. Jordan and R.M. Karp, LOGOS: A modular Bayesian model for de novo motif detection. Journal of Bioinformatics and Computational biology 2004, 2(1), 127-154. An earlier version of this paper was also presented in IEEE Computer Society Bioinformatics Conference, CSB2003. (ps)

• E.P. Xing, An expressive modular probabilistic model for de novo motif detection, IJCAI Workshop on Learning Graphical Models for Computational Genomics, 2003.

• E.P. Xing, Feature Selection in Microarray Analysis, in D.P. Berrar, W. Dubitzky and M. Granzow (Eds.), A Practical Approach to Microarray Data Analysis, Kluwer Academic Publishers, 2003.

        2002

• E.P. Xing, M.I. Jordan, R.M. Karp and S. Russell, A Hierarchical Bayesian Markovian Model for Motifs in Biopolymer Sequences. Advances in Neural Information Processing Systems 16 (NIPS2002), (eds. Becker et al.) MIT Press, 2002. (ps) A long version with more biological results is available here.

• E.P. Xing, A.Y. Ng, M.I. Jordan and S. Russell, Distance Metric Learning, with application to Clustering with side-information, Advances in Neural Information Processing Systems 16 (NIPS2002), (eds. Becker et al.) MIT Press, 521-528, 2002. (ps, data, code.)

        2001

• E.P. Xing, M.I. Jordan and R.M. Karp, Feature selection for high-dimensional genomic microarray data, Proceedings of the Eighteenth International Conference on Machine Learning, (ICML2001). (ps)

• E.P. Xing and R.M. Karp, CLIFF: Clustering of High-Dimensional Microarray Data via Iterative Feature Filtering Using Normalized Cuts, Bioinformatics 17 Suppl 1:S306-15, 2001. (special issue for The Ninth International Conference on Intelligence Systems for Molecular Biology, (ISMB 2001)).

• E.P. Xing, D. Wolf, I. Dubchak, S. Spengler, M. Zorn, C. Kulikowski, I. Muchnik, Automatic discovery of sub-molecular sequence domains in multi-aligned sequences: a dynamic programming algorithm for multiple alignment segmentation, Journal of Theoretical Biology, 21;212(2):129-39, 2001.

        Pre-2000

• E.P. Xing, C. Kulikowski, I. Muchnik, I. Dubchak, D. Wolf, S. Spengler and M. Zorn, Analysis of ribosomal RNA sequences by combinatorial clustering, Proceedings, The Seventh International Conference on Intelligence Systems for Molecular Biology (ISMB99), (Eds. T. Lengauer et al.) AAAI/MIT Press, Menlo Park, CA. P. 287-296, 1999.

• E.P. Xing, Y. Nie, Y-L Song, G-Y. Yang, C. C. Cai, L-D. Wang, C. S. Chung, Mechanisms of inactivation of p14^ARF, p15^INK4b and p16^INK4a genes in human esophageal squamous cell carcinoma: p14^ARF is potentially another inactivation hotspot within the 9p21 gene cluster;   Clinical Cancer Research, 1999 Oct; 5(10): 2704-13.

• E. P. Xing, G-Y. Yang, L-D. Wang, C. S. Yang, Loss of Heterozygous of the Rb gene correlates with pRb protein expression and associates with p53 alteration in human esophageal cancer, Clinical Cancer Research, 1999 May; 5(5): 1231-40.

• E. P. Xing, Y. Nie, L-D. Wang, G-Y. Yang, C.S. Yang, Aberrant methylation of p16^INK4a and deletion of p15^INK4b are frequent events in human esophageal cancer, Carcinogenesis, 1999 Jan; 20(1): 77-84.

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