Eric Xing's Publications List

Graphical Models

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).
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.

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).

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%)

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).

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).

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

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).

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).

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 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].

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, 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, 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.

Last updated 08/09/06