Faculty Publications - Michael Jordan

Books

• M. I. Jordan and T. J. Sejnowski, Eds., Graphical Models: Foundations of Neural Computation, Computational Neuroscience, Cambridge, MA: MIT Press, 2001.
• M. I. Jordan, Ed., Learning in Graphical Models, Adaptive Computation and Machine Learning, Cambridge, MA: MIT Press, 1999.

Book chapters or sections

• L. Huang, X. Nguyen, M. Garofalakis, M. Jordan, A. D. Joseph, and N. Taft, "In-network PCA and anomaly detection," in Advances in Neural Information Processing Systems 19: Proc. 20th Annual Conf. (NIPS 2006), B. Scholkopf, J. Platt, and T. Hofmann, Eds., Advances in Neural Information Processing Systems, Vol. 19, Cambridge, MA: MIT Press, 2007, pp. 617-624.
• M. Wainwright and M. Jordan, "A variational principle for graphical models," in New Directions in Statistical Signal Processing: From Systems to Brain, S. Haykin, J. C. Principe, T. J. Sejnowski, and J. McWhirter, Eds., Neural Information Processing, Cambridge, MA: MIT Press, 2006, pp. 155-202.
• N. D. Lawrence and M. Jordan, "Gaussian processes and the null-category noise model," in Semi-Supervised Learning, O. Chapelle, B. Schoelkopf, and A. Zien, Eds., Cambridge, MA: MIT Press, 2006, pp. 138-144.
• X. Nguyen, M. Wainwright, and M. Jordan, "Divergences, surrogate loss functions and experimental design," in Advances in Neural Information Processing Systems 18: Proc. 19th Annual Conf. (NIPS 2005), Y. Weiss, B. Schoelkopf, and J. Platt, Eds., Advances in Neural Information Processing Systems, Vol. 18, Cambridge, MA: MIT Press, 2006, pp. 1011-1018.
• B. Taskar, S. Lacoste Julien, and M. Jordan, "Structured prediction via the extragradient method," in Advances in Neural Information Processing Systems 18: Proc. 19th Annual Conf. (NIPS 2005), Y. Weiss, B. Schoelkopf, and J. Platt, Eds., Advances in Neural Information Processing Systems, Vol. 18, Cambridge, MA: MIT Press, 2006, pp. 1345-1352.
• P. Flaherty, M. Jordan, and A. P. Arkin, "Robust design of biological experiments," in Advances in Neural Information Processing Systems 18: Proc. 19th Annual Conf. (NIPS 2005), Y. Weiss, B. Schoelkopf, and J. Platt, Eds., Advances in Neural Information Processing Systems, Vol. 18, Cambridge, MA: MIT Press, 2006, pp. 363-370.
• Y. W. Teh, M. Jordan, M. J. Beal, and D. M. Blei, "Sharing clusters among related groups: Hierarchical Dirichlet processes," in Advances in Neural Information Processing Systems 17: Proc. 18th Annual Conf. (NIPS 2004), L. K. Saul, Y. Weiss, and L. Bottou, Eds., Advances in Neural Information Processing Systems, Vol. 17, Cambridge, MA: MIT Press, 2005, pp. 1385-1392.
• F. R. Bach and M. Jordan, "Blind one-microphone speech separation: A spectral learning approch," in Advances in Neural Information Processing 17: Proc. 18th Annual Conf. (NIPS 2004), L. K. Saul, Y. Weiss, and L. Bottou, Eds., Advances in Neural Information Processing, Vol. 17, Cambridge, MA: MIT Press, 2005, pp. 65-72.
• A. d'Aspremont, L. El Ghaoui, M. Jordan, and G. R. G. Lanckriet, "A direct formulation for sparse PCA using semidefinite programming," in Advances in Neural Information Processing Systems 17: Proc. 18th Annual Conf. (NIPS 2004), L. K. Saul, Y. Weiss, and L. Bottou, Eds., Advances in Neural Information Processing Systems, Vol. 17, Cambridge, MA: MIT Press, 2005, pp. 41-48.
• N. D. Lawrence and M. Jordan, "Semi-supervised learning via Gaussian processes," in Advances in Neural Information Processing Systems 17: Proc. 18th Annual Conf. (NIPS 2004), L. K. Saul, Y. Weiss, and L. Bottou, Eds., Advances in Neural Information Processing Systems, Vol. 17, Cambridge, MA: MIT Press, 2005, pp. 753-760.
• F. R. Bach, R. Thibaux, and M. Jordan, "Computing regularization paths for learning multiple kernels," in Advances in Neural Information Processing Systems 17: Proc. 18th Annual Conf. (NIPS 2004), L. K. Saul, Y. Weiss, and L. Bottou, Eds., Advances in Neural Information Processing Systems, Vol. 17, Cambridge, MA: MIT Press, 2005, pp. 73-80.
• N. D. Lawrence, J. C. Platt, and M. Jordan, "Extensions of the informative vector machine," in Deterministic and Statistical Methods in Machine Learning: Proc. 1st Intl. Sheffield Machine Learning Workshop. Revised Lectures, J. Winkler, M. Niranjan, and N. Lawrence, Eds., Lecture Notes in Computer Science, Vol. 3635, Berlin, Germany: Springer-Verlag, 2005, pp. 56-87.
• M. Wainwright and M. Jordan, "Semidefinite relaxations for approximate inference on graphs with cycles," in Advances in Neural Information Processing Systems 16: Proc. 17th Annual Conf. (NIPS 2003), S. Thrun, L. K. Saul, and B. Schoelkopf, Eds., Neural Information Processing Systems, Vol. 16, Cambridge, MA: MIT Press, 2004, pp. 369-376.
• F. R. Bach and M. Jordan, "Learning spectral clustering," in Advances in Neural Information Processing Systems 16: Proc. 17th Annual Conf. (NIPS 2003), S. Thrun, L. K. Saul, and B. Schoelkopf, Eds., Advances in Neural Information Processing Systems, Vol. 16, Cambridge, MA: MIT Press, 2004, pp. 305-312.
• D. M. Blei, T. L. Griffiths, M. Jordan, and J. B. Tenenbaum, "Hierarchical topic models and the nested Chinese restaurant process," in Advances in Neural Information Processing Systems 16: Proc. 17th Annual Conf. (NIPS 2003), S. Thrun, L. K. Saul, and B. Schoelkopf, Eds., Advances in Neural Information Processing Systems, Vol. 16, Cambridge, MA: MIT Press, 2004, pp. 17-24.
• K. Fukumizu, F. R. Bach, and M. Jordan, "Kernel dimensionality reduction for supervised learning," in Advances in Neural Information Processing Systems 16: Proc. 17th Annual Conf. (NIPS 2003), S. Thrun, L. K. Saul, and B. Schoelkopf, Eds., Advances in Neural Information Processing Systems, Vol. 16, Cambridge, MA: MIT Press, 2004, pp. 81-88.
• P. Bartlett, M. Jordan, and J. D. McAuliffe, "Large margin classifiers: Convex loss, low noise, and convergence rates," in Advances in Neural Information Processing Systems 16: Proc. 17th Annual Conf. (NIPS 2003), S. Thrun, L. K. Saul, and B. Schoelkopf, Eds., Advances in Neural Information Processing Systems, Vol. 16, Cambridge, MA: MIT Press, 2004, pp. 1173-1180.
• X. Nguyen and M. Jordan, "On the concentration of expectation and approximate inference in layered networks," in Advances in Neural Information Processing Systems 16: Proc. 17th Annual Conf. (NIPS 2003), S. Thrun, L. K. Saul, and B. Schoelkopf, Eds., Advances in Neural Information Processing Systems, Vol. 16, Cambridge, MA: MIT Press, 2004, pp. 393-400.
• A. X. Zheng, M. Jordan, B. Liblit, and A. Aiken, "Statistical debugging of sampled programs," in Advances in Neural Information Processing Systems 16: Proc. 17th Annual Conf. (NIPS 2003), S. Thrun, L. K. Saul, and B. Schoelkopf, Eds., Advances in Neural Information Processing Systems, Vol. 16, Cambridge, MA: MIT Press, 2004, pp. 603-610.
• A. Y. Ng, H. J. Kim, M. Jordan, and S. S. Sastry, "Autonomous helicopter flight via reinforcement learning," in Advances in Neural Information Processing Systems 16: Proc. 17th Annual Conf. (NIPS 2003), S. Thrun, L. K. Saul, and B. Schoelkopf, Eds., Advances in Neural Information Processing Systems, Vol. 16, Cambridge, MA: MIT Press, 2004, pp. 799-806.
• F. R. Bach and M. Jordan, "Learning graphical models with Mercer kernels," in Advances in Neural Information Processing Systems: Proc. 16th Annual Conf. (NIPS 2002), S. Becker, S. Thrun, and K. Obermayer, Eds., Vol. 15, Cambridge, MA: MIT Press, 2003, pp. 1033-1040.
• G. R. G. Lanckriet, L. El Ghaoui, and M. Jordan, "Robust novelty detection with single-class MPM," in Advances in Neural Information Processing Systems: Proc. 16th Annual Conf. (NIPS 2002), S. Becker, S. Thrun, and K. Obermayer, Eds., Vol. 15, Cambridge, MA: MIT Press, 2003, pp. 929-936.
• E. P. Xing, A. Y. Ng, M. Jordan, and S. J. Russell, "Distance metric learning with application to clustering with side-information," in Advances in Neural Information Processing Systems: Proc. 16th Annual Conf. (NIPS 2002), S. Becker, S. Thrun, and K. Obermayer, Eds., Vol. 15, Cambridge, MA: MIT Press, 2003, pp. 521-528.
• E. Todorov and M. Jordan, "A minimal intervention principle for coordinated movement," in Advances in Neural Information Processing Systems: Proc. 16th Annual Conf. (NIPS 2002), S. Becker, S. Thrun, and K. Obermayer, Eds., Vol. 15, Cambridge, MA: MIT Press, 2003, pp. 27-34.
• E. P. Xing, M. Jordan, R. M. Karp, and S. J. Russell, "A hierarchical Bayesian Markovian model for motifs in biopolymer sequences," in Advances in Neural Information Processing Systems: Proc. 16th Annual Conf. (NIPS 2002), S. Becker, S. Thrun, and K. Obermayer, Eds., Vol. 15, Cambridge, MA: MIT Press, 2003, pp. 1513-1520.

Articles in journals or magazines

• A. d'Aspremont, L. El Ghaoui, M. Jordan, and G. Lanckriet, "A Direct Formulation for Sparse PCA Using Semidefinite Programming," SIAM Review, vol. 49, no. 3, pp. 434--488, 2007.
• G. Kimmel, M. Jordan, E. Halperin, R. Shamir, and R. M. Karp, "A randomization test for controlling population stratification in whole-genome association studies," The American J. Human Genetics, vol. 81, no. 5, pp. 895-905, Nov. 2007.
• A. D'Aspremont, L. El Ghaoui, M. Jordan, and G. Lanckriet, "A direct formulation for sparse PCA using semidefinite programming," SIAM Review, vol. 49, pp. 434-448, July 2007.
• E. P. Xing, M. Jordan, and R. Sharan, "Bayesian haplotype inference via the Dirichlet process," Journal of Computational Biology, vol. 14, pp. 267-284, June 2007.
• D. M. Blei and M. Jordan, "Variational inference for Dirichlet process mixtures," Bayesian Analysis, vol. 1, no. 1, pp. 121-143, 2006.
• B. Taskar, S. Lacoste Julien, and M. Jordan, "Structured prediction, dual extragradient and Bregman projections," J. Machine Learning Research, vol. 7, pp. 1627-1653, Dec. 2006.
• F. R. Bach and M. Jordan, "Learning spectral clustering, with application to speech separation," J. Machine Learning Research, vol. 7, pp. 1963-2001, Dec. 2006.
• Y. W. Teh, M. Jordan, M. J. Beal, and D. M. Blei, "Hierarchical Dirichlet processes," J. American Statistical Association, vol. 101, no. 476, pp. 1566-1581, Dec. 2006.
• P. Bartlett, M. Jordan, and J. D. McAuliffe, "Comment on "Support vector machines with applications"," Statistical Science, vol. 21, no. 3, pp. 341-346, Aug. 2006.
• M. Wainwright and M. Jordan, "Log-determinant relaxation for approximate inference in discrete Markov random fields," IEEE Trans. Signal Processing, vol. 54, no. 6, pt. 1, pp. 2099-2109, June 2006.
• Y. W. Teh, M. Jordan, M. J. Beal, and D. M. Blei, "Hierarchical Dirichlet processes," Journal of the American Statistical Association, vol. 101, pp. 1566-1581, June 2006.
• D. M. Blei, K. Franks, M. Jordan, and I. S. Mian, "Statistical modeling of biomedical corpora: Mining the Caenorhabditis Genetic Center bibliography for genes related to life span," BMC Bioinformatics, vol. 7, pp. 250-269, May 2006.
• J. D. McAuliffe, D. M. Blei, and M. Jordan, "Nonparametric empirical Bayes for the Dirichlet process mixture model," Statistics and Computing, vol. 16, no. 1, pp. 5-14, March 2006.
• P. Bartlett, M. Jordan, and J. D. McAuliffe, "Convexity, classification, and risk bounds," J. American Statistical Association, vol. 101, no. 473, pp. 138-156, March 2006.
• P. Bartlett, M. Jordan, and J. D. McAuliffe, "Convexity, classification, and risk bounds," Journal of the American Statistical Association, vol. 101, no. 473, pp. 138-156, March 2006.
• B. Taskar, S. Lacoste-Julien, and M. Jordan, "Structured prediction, dual extragradient and Bregman projections," Journal of Machine Learning Research, vol. 7, pp. 1627-1653, Jan. 2006.
• F. R. Bach and M. Jordan, "Learning spectral clustering, with application to speech separation," Journal of Machine Learning Research, vol. 7, pp. 1963-2001, Jan. 2006.
• X. Nguyen, M. Wainwright, and M. Jordan, "Nonparametric decentralized detection using kernel methods," IEEE Trans. Signal Processing, vol. 53, no. 11, pp. 4053-4066, Nov. 2005.
• B. E. Engelhardt, M. Jordan, K. E. Muratore, and S. E. Brenner, "Protein molecular function prediction by Bayesian phylogenomics," PLoS Computational Biology, vol. 1, no. 5, pp. 432-445, Oct. 2005.
• P. Flaherty, G. Giaever, J. Kumm, M. Jordan, and A. P. Arkin, "A latent variable model for chemogenomic profiling," Bioinformatics, vol. 21, no. 15, pp. 3286-3293, Aug. 2005.
• X. Nguyen, M. Jordan, and B. Sinopoli, "A kernel-based learning approach to ad hoc sensor network localization," ACM Trans. Sensor Networks, vol. 1, no. 1, pp. 134-152, Aug. 2005.
• W. Lee, R. P. St. Onge, M. Proctor, P. Flaherty, M. Jordan, A. P. Arkin, R. W. Davis, C. Nislow, and G. Giaever, "Genome-wide requirements for resistance to functionally distinct DNA-damaging agents," PLoS Genetics, vol. 1, no. 2, pp. 235-246, Aug. 2005.
• B. Liblit, M. Naik, A. X. Zheng, A. Aiken, and M. Jordan, "Scalable statistical bug isolation," ACM SIGPLAN Notices, vol. 40, no. 6, pp. 15-26, June 2005.
• J. D. McAuliffe, M. Jordan, and L. Pachter, "Subtree power analysis finds optimal species for comparative genomics," Proc. National Academy of Sciences, vol. 102, no. 22, pp. 7900-7905, May 2005.
• P. Gyaneshwar, O. Paliy, J. McAuliffe, D. L. Popham, M. Jordan, and S. Kustu, "Sulfur and nitrogen limitation in Escherichia coli K12: Specific homeostatic responses," J. Bacteriology, vol. 187, no. 3, pp. 1074-1090, Feb. 2005.
• K. Fukumizu, F. R. Bach, and M. Jordan, "Dimensionality reduction for supervised learning with reproducing kernel Hilbert spaces," J. Machine Learning Research, vol. 5, pp. 73-99, Dec. 2004.
• G. R. G. Lanckriet, N. Cristianini, P. Bartlett, L. El Ghaoui, and M. Jordan, "Learning the kernel matrix with semidefinite programming," J. Machine Learning Research, vol. 5, pp. 27-72, Dec. 2004.
• C. Bhattacharyya, L. R. Grate, M. Jordan, and L. El Ghaoui, "Robust sparse hyperplane classifiers: Application to uncertain molecular profiling data," J. Computational Biology, vol. 11, no. 6, pp. 1073-1089, Dec. 2004.
• G. G. R. Lanckriet, T. De Bie, N. Cristianini, M. Jordan, and W. S. Noble, "A statistical framework for genomic data fusion," Bioinformatics, vol. 20, no. 16, pp. 2626-2635, Nov. 2004.
• B. Sinopoli, L. Schenato, M. Franceschetti, K. Poolla, M. Jordan, and S. S. Sastry, "Kalman filtering with intermittent observations," IEEE Trans. Automatic Control: Special Issue on Sensor Networks, vol. 49, no. 9, pp. 1453-1464, Sep. 2004.
• B. Sinopoli, L. Schenato, M. Franceschetti, K. Poolla, M. Jordan, and S. S. Sastry, "Kalman filtering with intermittent observations," IEEE Trans. Automatic Control, vol. 49, no. 9, pp. 1453-1464, Sep. 2004.
• F. R. Bach and M. Jordan, "Learning graphical models for stationary time series," IEEE Trans. Signal Processing, vol. 52, no. 8, pp. 2189-2199, Aug. 2004.
• J. D. McAuliffe, L. Pachter, and M. Jordan, "Multiple-sequence functional annotation and the generalized hidden Markov phylogeny," Bioinformatics, vol. 20, no. 12, pp. 1850-1860, Aug. 2004.
• E. P. King, W. Wu, M. Jordan, and R. M. Karp, "LOGOS: A modular Bayesian model for de novo motif detection," J. Bioinformatics and Computational Biology, vol. 2, no. 1, pp. 127-154, March 2004.
• P. Bartlett, M. Jordan, and J. D. McAuliffe, "[Consistency in Boosting]: Discussion," Annals of Statistics, vol. 32, no. 1, pp. 85-91, Feb. 2004.
• M. Jordan, "Graphical models," Statistical Science: Special Issue on Bayesian Statistics, vol. 19, no. 1, pp. 140-155, Feb. 2004.
• M. Jordan, "Graphical models," Statistical Science: Special Issue on Bayesian Statistics, vol. 19, no. 1, pp. 140-155, Feb. 2004.
• G. Giaever, P. Flaherty, J. Kumm, M. Proctor, C. Nislow, D. F. Jaramillo, A. M. Chu, M. Jordan, A. P. Arkin, and R. W. Davis, "Chemogenomic profiling: Identifying the functional interactions of small molecules in yeast," Proc. National Academy of Sciences of the United States of America, vol. 101, no. 3, pp. 793-798, Jan. 2004.
• G. R. G. Lanckriet, L. El Ghaoui, C. Bhattacharyya, and M. Jordan, "A robust minimax approach to classification," The J. of Machine Learning, vol. 3, pp. 555-582, March 2003.
• D. M. Blei, A. Y. Ng, and M. Jordan, "Latent Dirichlet allocation," J. Machine Learning Research, vol. 3, pp. 993-1022, Jan. 2003.
• M. Jordan, Z. Ghahramani, T. S. Jaakkola, and L. K. Saul, "An introduction to variational methods for graphical models," Machine Learning, vol. 37, no. 2, pp. 183-233, Nov. 1999.
• D. Wolpert, Z. Ghahramani, and M. Jordan, "An internal forward model for sensorimotor integration," Science, vol. 269, pp. 1880-1882, Sep. 1995.
• M. Jordan and R. A. Jacobs, "Hierarchical mixtures of experts and the EM algorithm," Neural Computation, vol. 6, no. 2, pp. 181-214, March 1994.

Articles in conference proceedings

• T. Li, C. Ding, and M. Jordan, "Solving consensus and semi-supervised clustering problems using nonnegative matrix factorization," in Proc. 7th IEEE Intl. Conf. on Data Mining (ICDM 2007), Los Alamitos, CA: IEEE Computer Society, 2007, pp. 577-582.
• J. J. Kivinen, E. B. Sudderth, and M. Jordan, "Learning multiscale representations of natural scences using Dirichlet processes," in Proc. 11th IEEE Intl. Conf. on Computer Vision (ICCV 2007), Piscataway, NJ: IEEE Press, 2007, pp. 8 pg.
• J. J. Kivinen, E. B. Sudderth, and M. Jordan, "Image denoising with nonparametric hidden Markov trees," in Proc. IEEE Intl. Conf. on Image Processing (ICIP 2007), Vol. 3, Piscataway, NJ: IEEE Press, 2007, pp. 121-124.
• X. Nguyen, M. Wainwright, and M. Jordan, "Nonparametric estimation of the likelihood ratio and divergence functionals," in Proc. 2007 IEEE Intl. Symp. on Information Theory (ISIT 2007), Piscataway, NJ: IEEE Press, 2007, pp. 6 pg.
• P. Liang, M. Jordan, and B. Taskar, "A permutation-augmented sampler for DP mixture models," in Proc. 24th Intl. Conf. on Machine Learning, Z. Ghahramani, Ed., ACM International Conference Proceeding, Vol. 227, New York, NY: The Association for Computing Machinery, Inc., 2007, pp. 545-552.
• P. Liang, S. Petrov, M. Jordan, and D. Klein, "The infinite PCFG using hierarchical Dirichlet processes," in Proc. 2007 Joint Conf. on Empirical Methods in Natural Language Processing and Computational Natural Language Learning (EMNLP-CoNLL 2007), East Stroudsburg, PA: The Association for Computational Linguistics, 2007, pp. 688-697.
• J. Nilsson, F. Sha, and M. Jordan, "Regression on manifolds using kernel dimension reduction," in Proc. 24th Intl. Conf. on Machine Learning (ICML '07), Z. Ghahramani, Ed., ACM International Conference Proceeding Series, Vol. 227, New York, NY: The Association for Computing Machinery, Inc., 2007, pp. 697-704.
• L. Huang, X. Nguyen, M. Garofalakis, J. M. Hellerstein, M. Jordan, A. D. Joseph, and N. Taft, "Communication-Efficient Online Detection of Network-Wide Anomalies," in Proceedings of 26th IEEE International Conference on Computer Communications (INFOCOM'07), Anchorage, Alaska: IEEE Press, 2007, pp. 134-142.
• L. Huang, X. Nguyen, M. Garofalakis, J. M. Hellerstein, M. Jordan, A. D. Joseph, and N. Taft, "Communication-efficient online detection of network-wide anomalies," in Proc. 26th IEEE Intl. Conf. on Computer Communications (INFOCOM 2007), Piscataway, NJ: IEEE Press, 2007, pp. 134-142.
• R. Thibaux and M. Jordan, "Hierarchical beta processes and the Indian buffet process," in Proc. 11th Intl. Conf. on Artificial Intelligence and Statistics (AISTATS 2007), M. Meila and X. Shen, Eds., Madison, WI: Omnipress, 2007, pp. online.
• X. Nguyen, M. Wainwright, and M. Jordan, "On optimal quantization rules for sequential decision problems," in Proc. 2006 IEEE Intl. Symp. on Information Theory (ISIT 2006), Piscataway, NJ: IEEE Press, 2006, pp. 2652-2656.
• Z. Zhang and M. Jordan, "Bayesian multicategory support vector machines," in Proc. 22nd Conf. on Uncertainty in Artificial Intelligence (UAI 2006), Arlington, VA: AUAI Press, 2006, pp. 8 pg.
• P. Bodik, A. Fox, M. Jordan, D. A. Patterson, A. Banerjee, R. Jagannathan, T. Su, S. Tenginakai, B. Turner, and J. Ingalls, "Advanced tools for operators at Amazon.com," in Proc. 1st Workshop on Hot Topics in Autonomic Computing (HotAC I), Piscataway, NJ: IEEE Press, 2006, pp. 5 pg.
• S. Lacoste Julien, B. Taskar, D. Klein, and M. Jordan, "Word alignment via quadratic assignment," in Proc. 4th Human Language Technology Conf. of the North American Chapter of the Association for Computational Linguistics Annual Meeting (HLT-NAACL 2006), R. C. Moore, J. A. Bilmes, J. Chu Carroll, and M. Sanderson, Eds., East Stroudsburg, PA: Association for Computational Linguistics, 2006, pp. 112-119.
• B. E. Engelhardt, M. Jordan, and S. E. Brenner, "A graphical model for predicting protein molecular function," in Proc. 23rd Intl. Conf. on Machine Learning (ICML '06), W. W. Cohen and A. Moore, Eds., ACM International Conference Proceeding, Vol. 148, New York, NY: The Association for Computing Machinery, Inc., 2006.
• A. X. Zheng, M. Jordan, B. Liblit, M. Naik, and A. Aiken, "Statistical debugging: Simultaneous identification of multiple bugs," in Proc. 23rd Intl. Conf. on Machine Learning (ICML '06), W. W. Cohen and A. Moore, Eds., ACM International Conference Proceeding, Vol. 148, New York, NY: The Association for Computing Machinery, Inc., 2006, pp. 1105-1112.
• E. P. Xing, K. A. Sohn, M. Jordan, and Y. W. Teh, "Bayesian multi-population haplotype inference via a hierarchical Dirichlet process mixture," in Proc. 23rd Intl. Conf. on Machine Learning (ICML '06), W. W. Cohen and A. Moore, Eds., ACM International Conference Proceeding, Vol. 148, New York, NY: The Association for Computing Machinery, Inc., 2006, pp. 1049-1056.
• M. Jordan, "Nonparametric Bayesian methods: Dirichlet processes, Chinese restaurant processes and all that (Tutorial)," in Proc. 19th Annual Conf. on Neural Information Processing Systems (NIPS 2005), Neural Information Processing Systems Foundation, 2005.
• X. Nguyen, M. Wainwright, and M. Jordan, "On information divergence measures, surrogate loss functions and decentralized hypothesis testing," in 43rd Annual Allerton Conf. on Communication, Control, and Computing, G. Dullerud and A. Singer, Eds., Urbana-Champaign, IL: University of Illinois, 2005.
• F. R. Bach and M. Jordan, "Predictive low-rank decomposition for kernel methods," in Proc. 22nd Intl. Conf. on Machine Learning (ICML '05), L. De Raedt and S. Wrobel, Eds., ACM International Conference Proceeding, Vol. 119, New York, NY: The Association for Computing Machinery, Inc., 2005, pp. 33-40.
• M. Rozen Zvi, M. Jordan, and A. Yuille, "The DLR hierarchy of approximate inference," in Proc. 21st Conf. in Uncertainty in Artificial Intelligence (UAI 2005), Arlington, VA: AUAI Press, 2005, pp. 493-500.
• P. Bodic, G. Friedman, L. Biewald, H. Levine, G. Candea, K. Patel, G. Tolle, J. Hui, A. Fox, M. Jordan, and D. A. Patterson, "Combining visualization and statistical analysis to improve operator confidence and efficiency for failure detection and localization," in Proc. 2nd Intl. Conf. on Autonomic Computing (ICAC '05), Los Alamitos, CA: IEEE Computer Society, 2005, pp. 89-100.
• B. Liblit, M. Naik, A. X. Zheng, A. Aiken, and M. Jordan, "Scalable statistical bug isolation," in Proc. 2005 ACM SIGPLAN Conf. on Programming Language Design and Implementation (PLDI '05), New York, NY: The Association for Computing Machinery, Inc., 2005, pp. 15-26.
• B. K. Vogel, M. Jordan, and D. Wessel, "Multi-instrument musical transcription using a dynamic graphical model," in Proc. 2005 IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing (ICASSP '05), Vol. 5, Piscataway, NJ: IEEE Press, 2005, pp. 493-496.
• F. R. Bach and M. Jordan, "Discriminative training of hidden Markov models for multiple pitch tracking," in Proc. 2005 IEEE Intl. Conf. on Acoustics, Speech, and Signal Processing (ICASSP '05), Vol. 5, Piscataway, NJ: IEEE Press, 2005, pp. 489-492.
• Y. W. Teh, M. Seeger, and M. Jordan, "Semiparametric latent factor models," in Proc. 10th Intl. Workshop on Artificial Intelligence and Statistics (AISTATS 2005), R. Cowell and Z. Ghahramani, Eds., NJ: The Society for Artificial Intelligence and Statistics, 2005, pp. 333-340.
• A. Fox, E. Kiciman, D. A. Patterson, R. H. Katz, and M. Jordan, "Combining statistical monitoring and predictable recovery for self-management," in Proc. 1st ACM SIGSOFT Workshop on Self-Managed Systems (WOSS '04), D. Garlan, J. Kramer, and A. Wolf, Eds., New York, NY: The Association for Computing Machiney, Inc., 2004, pp. 49-53.
• M. Wainwright and M. Jordan, "Variational inference in graphical models: The view from the marginal polytope," in Proc. 41st Allerton Conf. on Communication, Control, and Computing, Urbana-Champaign, IL: University of Illinois, 2004.
• D. M. Blei and M. Jordan, "Variational methods for the Dirichlet process," in Proc. 21st Intl. Conf. on Machine Learning (ICML '04), ACM International Conference Proceeding Series, Vol. 69, New York, NY: The Association for Computing Machinery, Inc., 2004, pp. Art. 12.
• X. Nguyen, M. Wainwright, and M. Jordan, "Decentralized detection and classification using kernel methods," in Proc. 21st Intl. Conf. on Machine Learning (ICML '04), ACM International Conference Proceeding Series, Vol. 69, New York, NY: The Association for Computing Machinery, Inc., 2004, pp. Art. 80.
• E. Xing, R. Sharan, and M. Jordan, "Bayesian haplotype inference via the Dirichlet process," in Proc. 21st Intl. Conf. on Machine Learning (ICML '04), Vol. 69, New York, NY: The Association for Computing Machinery, Inc., 2004, pp. Art. 111.
• F. R. Bach, G. R. G. Lanckriet, and M. Jordan, "Multiple kernel learning, conic duality, and the SMO algorithm," in Proc. 21st Intl. Conf. on Machine Learning (ICML '04), ACM International Conference Proceeding Series, Vol. 69, New York, NY: The Association for Computing Machinery, Inc., 2004, pp. Art. 6.
• E. P. Xing, M. Jordan, and S. J. Russell, "Graph partition strategies for generalized mean field inference," in Proc. 20th Conf. on Uncertainty in Artificial Intelligence (UAI 2004), ACM International Conference Proceeding Series, Vol. 70, Arlington, VA: AUAI Press, 2004, pp. 602-610.
• B. Liblit, M. Naik, A. X. Zheng, A. Aiken, and M. Jordan, "Public deployment of cooperative bug isolation," in Proc. 2nd ICSE Workshop on Remote Analysis and Measurement of Software Systems (RAMSS '04), Stevenage, UK: IEE Society Press, 2004, pp. 57-62.
• M. Chen, A. X. Zheng, J. Lloyd, M. Jordan, and E. Brewer, "Failure diagnosis using decision trees," in Proc. 1st Intl. Conf. on Autonomic Computing (ICAC 2004), Los Alamitos, CA: IEEE Computer Society Press, 2004, pp. 36-43.
• G. R. G. Lanckriet, M. Deng, N. Cristianini, M. Jordan, and W. S. Noble, "Kernel-based data fusion and its application to protein function prediction in yeast," in Biocomputing 2004: Proc. Pacific Symp., Hawaii (PSB 2004), R. B. Altman, A. K. Dunker, L. Hunter, T. A. Jung, and T. E. Klein, Eds., Hoboken, NJ: World Scientific, 2004, pp. 300-311.
• E. P. Xing, W. Wu, M. Jordan, and R. M. Karp, "LOGOS: A modular Bayesian model for de novo motif detection," in Proc. 2nd Intl. IEEE Computer Society Computational Systems Bioinformatics Conf. (CSB 2003), Los Alamitos, CA: IEEE Computer Society, 2003, pp. 266-276.
• E. P. Xing, M. Jordan, and R. M. Karp, "Feature selection for high-dimensional genomic microarray data," in Proc. 18th Intl. Conf. on Machine Learning (ICML '01), C. E. Brodley and A. P. Danyluk, Eds., San Francisco, CA: Morgan Kaufmann Publishers Inc., 2001, pp. 601-608.

Conference proceedings (edited)

• M. Jordan, Y. LeCun, and S. A. Solla, Eds., Advances in Neural Information Processing Systems: Proceedings of the First 12 Conferences, Advances in Neural Information Processing Systems, Cambridge, MA: MIT Press, 2001.

Technical Reports

• G. Obozinski, B. Taskar, and M. Jordan, "Joint Covariate Selection for Grouped Classification," University of California, Berkeley, Department of Statistics, Tech. Rep. UCB/STAT-07-743, Oct. 2007.
• L. Huang, X. Nguyen, M. Garofalakis, M. Jordan, A. D. Joseph, and N. Taft, "In-Network PCA and Anomaly Detection," EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2007-10, Jan. 2007.
• C. Ding, T. Li, and M. Jordan, "Convex and Semi-Nonnegative Matrix Factorization," Lawrence Berkeley National Laboratory, Tech. Rep. LBNL-TR-60428, Nov. 2006.
• K. Fukumizu, F. R. Bach, and M. Jordan, "Kernel Dimension Reduction in Regression," University of California, Berkeley, Department of Statistics, Tech. Rep. UCB/STAT-06-715, Sep. 2006.
• L. Huang, X. Nguyen, M. Garofalakis, M. Jordan, A. D. Joseph, and N. Taft, "Distributed PCA and Network Anomaly Detection," EECS Department, University of California, Berkeley, Tech. Rep. UCB/EECS-2006-99, July 2006.
• X. Nguyen, M. Wainwright, and M. Jordan, "On Optimal Quantization Rules for Some Sequential Decision Problems," University of California, Berkeley, Department of Statistics, Tech. Rep. UCB/STAT-06-708, June 2006.
• B. Taskar, S. Lacoste Julien, and M. Jordan, "Structured Prediction, Dual Extragradient and Bregman Projections," University of California, Berkeley, Department of Statistics, Tech. Rep. UCB/STAT-05-697, Nov. 2005.
• X. Nguyen, M. Wainwright, and M. Jordan, "On Divergences, Surrogate Loss Functions, and Decentralized Detection," University of California, Berkeley, Department of Statistics, Tech. Rep. UCB/STAT-05-695, Oct. 2005.
• F. R. Bach and M. Jordan, "A Probabilistic Interpretation of Canonical Correlation Analysis," University of California, Berkeley, Department of Statistics, Tech. Rep. UCB/STAT-05-688, April 2005.
• Y. W. Teh, M. Jordan, M. J. Beal, and D. M. Blei, "Hierarchical Dirichlet Processes," University of California, Berkeley, Department of Statistics, Tech. Rep. UCB/STAT-04-653, Oct. 2004.
• D. M. Blei and M. Jordan, "Variational Inference for Dirichlet Process Mixtures," University of California, Berkeley, Department of Statistics, Tech. Rep. UCB/STAT-04-674, Oct. 2004.
• M. Wainwright and M. Jordan, "Treewidth-Based Conditions for Exactness of the Sherali-Adams & Lasserre Relaxations," University of California, Berkeley, Department of Statistics, Tech. Rep. UCB/STAT-04-671, Sep. 2004.
• A. d'Aspremont, L. El Ghaoui, M. I. Jordan, and G. R. G. Lanckriet, "A Direct Formulation for Sparse PCA Using Semidefinite Programming," EECS Department, University of California, Berkeley, Tech. Rep. UCB/CSD-04-1330, June 2004.
• X. Nguyen, M. Wainwright, and M. Jordan, "Decentralized Detection & Classification Using Kernel Methods," University of California, Berkeley, Department of Statistics, Tech. Rep. UCB/STAT-04-658, April 2004.
• M. Seeger and M. Jordan, "Sparse Gaussian Process Classification with Multiple Casses," University of California, Berkeley, Department of Statistics, Tech. Rep. UCB/STAT-04-661, April 2004.
• X. Nguyen, M. I. Jordan, and B. Sinopoli, "A Kernel-based Learning Approach to Ad Hoc Sensor Network Localization," EECS Department, University of California, Berkeley, Tech. Rep. UCB/CSD-04-1319, April 2004.
• F. R. Bach, G. R. G. Lanckriet, and M. I. Jordan, "Fast Kernel Learning using Sequential Minimal Optimization," EECS Department, University of California, Berkeley, Tech. Rep. UCB/CSD-04-1307, Feb. 2004.
• B. Sinopoli, L. Schenato, M. Franceschetti, K. Poolla, M. Jordan, and S. S. Sastry, "Kalman Filtering with Intermittent Observations," EECS Department, University of California, Berkeley, Tech. Rep. UCB/ERL M03/15, 2003.
• E. P. Xing, R. Sharan, and M. I. Jordan, "Bayesian Haplotype Inference via the Dirichlet Process," EECS Department, University of California, Berkeley, Tech. Rep. UCB/CSD-03-1275, Sep. 2003.
• G. R. G. Lanckriet, T. De Bie, N. Cristianini, M. I. Jordan, and W. Stafford Noble, "A Framework for Genomic Data Fusion and its Application to Membrane Protein Prediction," EECS Department, University of California, Berkeley, Tech. Rep. UCB/CSD-03-1273, Sep. 2003.
• E. P. Xing and M. I. Jordan, "On Semidefinite Relaxation for Normalized k-cut and Connections to Spectral Clustering," EECS Department, University of California, Berkeley, Tech. Rep. UCB/CSD-03-1265, June 2003.
• E. P. Xing and M. I. Jordan, "Graph Partition Strategies for Generalized Mean Field Inference," EECS Department, University of California, Berkeley, Tech. Rep. UCB/CSD-03-1274, June 2003.
• F. R. Bach and M. I. Jordan, "Learning Spectral Clustering," EECS Department, University of California, Berkeley, Tech. Rep. UCB/CSD-03-1249, June 2003.
• M. J. Wainwright and M. I. Jordan, "Semidefinite Relaxations for Approximate Inference on Graphs with Cycles," EECS Department, University of California, Berkeley, Tech. Rep. UCB/CSD-03-1226, Jan. 2003.
• G. R. G. Lanckriet, N. Cristianini, P. Bartlett, L. El Ghaoui, and M. I. Jordan, "Learning the Kernel Matrix with Semi-Definite Programming," EECS Department, University of California, Berkeley, Tech. Rep. UCB/CSD-02-1206, 2002.
• G. R. G. Lanckriet, L. El Ghaoui, C. Bhattacharyya, and M. I. Jordan, "A Robust Minimax Approach to Classification," EECS Department, University of California, Berkeley, Tech. Rep. UCB/CSD-02-1218, Dec. 2002.
• F. R. Bach and M. I. Jordan, "Finding Clusters in Independent Component Analysis," EECS Department, University of California, Berkeley, Tech. Rep. UCB/CSD-02-1209, Oct. 2002.
• F. R. Bach and M. I. Jordan, "Kernel Independent Component Analysis," EECS Department, University of California, Berkeley, Tech. Rep. UCB/CSD-01-1166, Nov. 2001.
• M. I. Jordan, Z. Ghahramani, T. S. Jaakkola, and L. K. Saul, "An Introduction to Variational Methods for Graphical Models," EECS Department, University of California, Berkeley, Tech. Rep. UCB/CSD-98-980, Jan. 1998.

Talks or presentations

• M. Jordan, "Keynote Address: Statistical Machine Learning and Computational Biology," presented at 2007 IEEE Intl. Conf. on Bioinformatics and Biomedicine (BIBM.2007), Fremont, CA, Nov. 2007.