Parameter Expanded Variational Bayesian Methods, Y. Qi and T.S. Jaakkola, in Advances in Neural Information Processing Systems 19, MIT Press, Cambridge, MA,  2007.  [pdf]

Cortical Surface Shape Analysis Based on Spherical Wavelets, P. Yu, P. E. Grant, Y. Qi, X. Han, F. Segonne, R. Pienaar, E. Busa, J. Pacheco, N. Makris, R. L. Buckner, P. Golland, and B. Fischl,  IEEE Transaction on Medical Imaging, 26(4):582-597, 2007. [html]

Expectation Propagation for Signal Detection in Flat-fading Channels, Yuan Qi and Thomas Minka, in IEEE transactions on Wireless Communications, vol. 6, no. 1, 348-355, 2007. [Preprint][IEEE notice] Obsolete version: MIT Media Lab  Technical Report Vismod-TR-555. Abstract of the previous version appears in the Proceedings of IEEE International Symposium on Information Theory, 2003, Yokohama, Japan.
An efficient fixed-lag smoothing algorithm for hybrid dynamic Bayesian networks with its application to wireless communications.

Modularity and Dynamics of Cellular Networks,  Y. Qi and  H. Ge,  PLoS Computational Biology, vol. 2, no. 12, 1502-1510, December, 2006. [html / pdf]

High-resolution Computational Models of Genome Binding Events, Y. Qi, A. Rolfe, K. D. MacIsaac, G. K. Gerber, D. Pokholok, J. Zeitlinger, T. Danford, R. D. Dowell, E. Fraenkel, T. S. Jaakkola, R. A. Young and D. K. Gifford, Nature Biotechnology, vol. 24, 963-970, August, 2006. [link].

Approximate Expectation Propagation for Bayesian Inference on Large-scale Problems, Y. Qi, T. S. Jaakkola, and D.K. Gifford, CSAIL Tech Report, [pdf].
EP inference on large networks for protein-DNA binding detection.

Semi-supervised Analysis of Gene Expression Profiles for Lineage-specific Development in the Caenorhabditis Elegans Embryo, Yuan (Alan) Qi, Patrycja E. Missiuro, Ashish Kapoor, Craig P. Hunter, Tommi S. Jaakkola, David K. Gifford and Hui Ge, Bioinformatics, vol. 22, no. 14, e417-e423, 2006. [Abstract] and  [pdf].

Bayesian Conditional Random Fields, Yuan Qi, Martin Szummer, and Thomas P. Minka,  to appear in Journal of Machine Learning Research.

Hyperparameter and Kernel Learning for Graph Based Semi-Supervised Classification, Ashish Kapoor, Yuan (Alan) Qi, Hyungil Ahn, and Rosalind W. Picard, Advances in Neural Information Processing Systems 18, MIT Press, Cambridge, MA,  2006.

Diagram Structure Recognition by Bayesian Conditional Random Fields, Yuan Qi, Martin Szummer, and Thomas P. Minka,  in the Proceedings of  International Conference on Computer Vision and Pattern Recognition, 2005.  [pdf/ps]
Introducing Automatic Relevance Determination to CRFs for feature selection and using contextual information for joint object recognition.

Extending Expectation Propagation for Graphical Models, Yuan Qi, Ph.D. thesis, MIT, 2005. [pdf]

Bayesian Conditional Random Fields, Yuan Qi, Martin Szummer, and Thomas P. Minka, in the proceedings of AISTATS 2005. [paper/pdf]

Predictive Automatic Relevance Determination by Expectation Propagation, Yuan Qi, Thomas P. Minka, Rosalind W. Picard, and Zoubin Ghahramani, in the Proceedings of Twenty-first International Conference on Machine Learning, July 4-8, 2004, Banff, Alberta, Canada. [paper/pdf] and [slides/ppt]
Bayesian sparse classifiers, which were applied to gene expression classification.

Tree-structured Approximations by Expectation Propagation, Thomas Minka and Yuan Qi, Advances in Neural Information Processing Systems 16,  2004. [pdf]

An efficient inference algorithm for loopy graphs. 

Questions and answers about philosophy of science, causation, and human/machine learning, Yuan Qi, October 2002, [pdf/ps]. 

Context-sensitive Bayesian Classifiers and  Application to Mouse Pressure Pattern Classification, Yuan Qi,  and Rosalind W. Picard, in the proceedings of International Conference on Pattern Recognition, Québec City, Canada, August 2002. [slide/ps] and [Paper/pdf].
A simple probabilistic way to combine multiple classifiers which are trained on different subsets of  a given training set.

Bayesian Spectrum Estimation of Unevenly Sampled Nonstationary Data, Yuan Qi, Thomas P. Minka, and Rosalind W. Picard, MIT Media Lab  Technical Report Vismod-TR-556.  [Abstract]  and  [Paper/pdf].
Check out this web page that summarizes experimental results, including comparison with  classical methods, e.g., Multitaper methods. The short version of this paper does not include sparsification techniques and appears in ICASSP 02, Orlando, Florida, May 2002. [Poster/pdf] and [Paper/pdf].