Yuan (Alan) Qi

Papers

  • Distributed Autonomous Online Learning: Regrets and Intrinsic Privacy-Preserving Properties, F. Yan, S. Sundaram, S. V. N. Vishwanathan, and Y. Qi, IEEE Transactions on Knowledge and Data Engineering, DOI:10.1109/TKDE.2012.59, 2012. [pdf]
  • Self-adjusting Models for Semi-supervised Learning in Partially-observed Settings, F. Akova, B. Rajwa, Y. Qi, and M. Dundar, IEEE International Conference on Data Mining, 2012.
  • Using Probabilistic Generative Models For Ranking Risks of Android Apps, H. Peng, C. Gates, B. Sarma, N. Li, Y. Qi, R. Potharaju, C. Nita-Rotaru and I. Molloy, ACM Conference on Computer and Communications Security, Oct., 2012. [pdf]
  • Minimizing Private Data Disclosures in the Smart Grid, W. Yang, N. Li, Y. Qi, W. Qardaji, S. McLaughlin and P. McDaniel, ACM Conference on Computer and Communications Security, Oct., 2012.[pdf]
  • Enriching Amnestic MCI Populations for Clinical Trials: Optimal Combination of Biomarkers to Predict Conversion to Dementia, P. Yu, R.A. Dean, S.D. Hall, Y. Qi, G. Sethuraman, B.A. Willis, E.R. Siemers, F. Martenyi, J.T. Tauscher, A. J. Schwarz,  the Alzheimer’s Disease Neuroimaging Initiative, Volume 32, Number 2, Journal of Alzheimer's Disease, 2012
  • Infinite Tucker decomposition: nonparametric Bayesian models for multiway data analysis, Z. Xu, F. Yan, Y. Qi, in Proceedings of International Conference on Machine Learning, 2012. [pdf]
  • Bayesian nonexhaustive learning for online discovery and modeling of emerging classes, M. Dundar, F Akova, Y. Qi, B. Rajwa, In Proceedings of International Conference on Machine Learning, 2012. [pdf]
  • Message passing with relaxed moment matching , Y. Qi and Y. Guo, arXiv:1204.4166v1 [cs.LG], 2012 [pdf]
  • EigenGP: Gaussian processes with sparse data-dependent eigenfunctions, , Y. Qi, B. Dai, and Y. Zhu, arXiv:1204.3972v1 [cs.LG], 2012. [pdf]
  • Infinite Tucker decomposition: nonparametric Bayesian models for multiway data analysis, Z. Xu, F. Yan, Y. Qi, arXiv:1108.6296v2 [cs.LG] 2012. [pdf]
  • EigenNet: A Bayesian hybrid of generative and conditional models for sparse learning, Y. Qi, F. Yan,   in Advances in Neural Information Processing Systems, MIT Press, Cambridge, MA, 2011. [pdf]
  • t-divergence based approximate inference, N. Ding, S V N Vishwanathan, Y. Qi,  in Advances in Neural Information Processing Systems, MIT Press, Cambridge, MA, 2011. [pdf]
  • Sparse matrix-variate Gaussian process blockmodels for network modeling, F. Yan, Z. Xu, and Y. Qi, In Proceedings of the 27th Conference on Uncertainty in Artificial Intelligence, Barcelona, Spain, 2011.  [pdf]
  • Sparse matrix-variate t process blockmodels, Z. Xu, F. Yan, and Y. Qi, in Proceedings of the 25th Conference on Artificial Intelligence (AAAI-2011), San Francisco, CA, 2011.  (Previous version: CS TR 11-005,  Purdue University.) [pdf]
  • Identifying neuroimaging and proteomic biomarkers for MCI and AD via the Elastic Net, L. Shen, S. Kim, Y. Qi, M. Inlow, S. Swaminathan, K. Nho, J. Wan, S. Risacher, L. Shaw, J. Trojanowski, M. Weiner, A. Saykin, ADNI, MBIA 2011: Lecture Notes in Computer Science (LNCS) 7012:27-34, Springer, Heidelberg, 2011. [link]
  • Distributed Autonomous Online Learning: Regret and Intrinsic Privacy-Preserving Properties, F. Yan, S. Sundaram, S. V. N. Vishwanathan, Y. Qi, [arxiv.org/abs/1006.4039].
  • Sparse-posterior Gaussian Processes for general likelihoods, Y. Qi, A. Abdel-Gawad, T.P. Minka, In Proceedings of the 26th Conference on Uncertainty in Artificial Intelligence, 2010.[pdf]
  • Uncovering Transcriptional Regulatory Networks by Sparse Bayesian Factor Model, Jia Meng, Jianqiu Zhang, Yuan Qi, Yidong Chen and Yufei Huang,  in EURASIP Journal on Advances in Signal Processing, 2010.[link]
  • Sparse Bayesian Learning for Identifying Imaging Biomarkers in AD Prediction, L. Shen, Y. Qi, S. Kim, K. Nho, J. Wan, S. L. Risacher, A.J. Saykin, and ADNI, in Proceedings of the 13th International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), China, September 2010.[pdf]
  • Identification of Rare Cell Populations in Comparative Flow Cytometry, A. Azad, J. Langguth, Y. Fang, Y. Qi, and A. Pothen, 10th Workshop on Algorithms in Bioinformatics, United Kingdom, September 2010.
  • Sparse Gaussian process regression via L_1 penalization, F. Yan, and Y. Qi, in Proceedings of International Conference on Machine Learning, Israel, June 2010 [pdf].
  • Nonparametric Bayesian Matrix Factorization by Power EP,  N. Ding, Y. Qi, R. Xiang, I. Molloy,  N. Li, in Journal Machine Learning Research, W &CP 9, vol. 9. (AI & STATS 2010) [pdf].
  • Behavior Based Record Linkage, M. Yakout, A.K. Elmagarmid, H. Elmeleegy, M. Ouzzani, and Y. Qi, in PVLDB (Journal track), Vol. 3, 2010.
  • Mining Roles with Noisy Data,  I. Molloy, N. Li, J. Lobo, Y. Qi,  and  L. Dickens, Proceedings of the Fifteenth ACM Symposium on Access Control Models and Technologies (SACMAT'10), June 2010.
  • Variable sigma Gaussian processes: An expectation propagation perspective, Y. Qi, A. H. Abdel-Gawad, T. P. Minka, arXiv:0910.0668v1.
  • Parallel Inference for Latent Dirichlet Allocation onGraphics Processing Units,  F. Yan, N. Xu, Y. Qi,  Advances in Neural Information Processing Systems, 2009 [pdf]
  • Virtual Vector Machine for Bayesian Online Classification, T.P. Minka, R. Xiang,  and Y. Qi, In Proceedings of the 25th Conference on Uncertainty in Artificial Intelligence, 2009. [pdf]
  • 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]
  • Expectation Propagation for Signal Detection in Flat-fading Channels, Y. Qi & T.P. Minka, in IEEE trans. on Wireless Communications, vol. 6, no. 1, 348-355, 2007. [Preprint, IEEE notice]
  • 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]
  • 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]
  • Modularity and Dynamics of Cellular Networks, Y. Qi and H. Ge, PLoS Computational Biology, vol. 2, no. 12, 1502-1510, December, 2006. [html / pdf]
  • 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, pdf]
  • Diagram Structure Recognition by Bayesian Conditional Random Fields, Y. Qi, M. Szummer, and T. P. Minka, in the Proceedings of International Conference on Computer Vision and Pattern Recognition,2005. [pdf/ps]
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.


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.


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. 

Expectation Propagation for Signal Detection in Flat-fading Channels, Yuan Qi and Thomas Minka,  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.

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


Hessian-based Markov Chain Monte-Carlo Algorithms, Yuan Qi and Thomas P. Minka, First Cape Cod Workshop on Monte Carlo Methods, Cape Cod, Massachusetts, September, 2002. [paper/pdf] [slides/ps]
Combining optimization techniques with MCMC leads to new fast sampling methods (HMH and AMIT).

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].

Hybrid Independent Component Analysis and Support Vector Machine Learning Scheme for Face Detection, Y. Qi, D. DeMenthon, and D. Doermann, International Conference on Acoustics, Speech, and Signal Processing (ICASSP01), Salt Lake City,Utah, May, 2001. [ps]

Learning Algorithms for Video and Audio Processing: Independent Component Analysis and Support Vector Machine based Approaches,  Yuan Qi, Technical Report LAMP-TR-056, CAR-TR-951, CS-TR-4174, Center for Automation Research, University of Maryland at College Park, August, 2000.

Subband-based Independent Component Analysis, Yuan Qi, S.A. Shamma, P.S. Krishnaprasad, in the proceedings of ICA2000, Helsinki, Finland, June 2000.