Publications

2012

• Matrix concentration inequalities via the method of exchangeable pairs. L. Mackey, M. I. Jordan, R. Y. Chen, B. Farrell and J. A. Tropp. arXiv:1201.6002, 2012.

2011

• A scalable bootstrap for massive data. A. Kleiner, A. Talwalkar, P. Sarkar and M. I. Jordan. arXiv:1112.5016, 2011.
• Combinatorial clustering and the beta negative binomial process. T. Broderick, L. Mackey, J. Paisley and M. I. Jordan. arXiv:1111.1802, 2011.
• Stick-breaking beta processes and the Poisson process. J. Paisley, D. Blei, and M. I. Jordan. In N. Lawrence and M. Girolami (Eds.), Proceedings of the Fifteenth Conference on Artificial Intelligence and Statistics (AISTATS), Canary Islands, Spain, to appear.
• Bayesian bias mitigation for crowdsourcing. F. L. Wauthier and M. I. Jordan. In P. Bartlett, F. Pereira, J. Shawe-Taylor and R. Zemel (Eds.), Advances in Neural Information Processing Systems (NIPS) 24, to appear.
• Divide-and-conquer matrix factorization. L. Mackey, A. Talwalkar and M. I. Jordan. In P. Bartlett, F. Pereira, J. Shawe-Taylor and R. Zemel (Eds.), Advances in Neural Information Processing Systems (NIPS) 24, to appear.
• Learning dependency-based compositional semantics. P. Liang, M. I. Jordan, and D. Klein. arXiv:1109.6841, 2011.
• Phylogenetic inference via sequential Monte Carlo. A. Bouchard-Cote, S. Sankararaman, and M. I. Jordan. Systematic Biology, doi: 10.1093/sysbio/syr131, 2012.
• Beta processes, stick-breaking, and power laws. T. Broderick, M. I. Jordan and J. Pitman. Bayesian Analysis, to appear, 2011.
• Ergodic subgradient descent. J. C. Duchi, A. Agarwal, M. Johansson, and M. I. Jordan. arXiv:1105.4681v1, 2011.
• Nonparametric combinatorial sequence models. F. Wauthier, M. I. Jordan, and N. Jojic. Journal of Computational Biology, 18, 1649-1660, 2011.
• Message from the President: Visualizing Bayesians. M. I. Jordan. ISBA Bulletin, 18(3), 1-2, 2011.
• Message from the President: The era of Big Data. M. I. Jordan. ISBA Bulletin, 18(2), 1-3, 2011.
• The SCADS Director: Scaling a distributed storage system under stringent performance requirements. B. Trushkowsky, P. Bodik, A. Fox, M. Franklin, M. I. Jordan, and D. Patterson. In 9th USENIX Conference on File and Storage Technologies (FAST '11), San Jose, CA, 2011.
• Union support recovery in high-dimensional multivariate regression. G. Obozinski, M. J. Wainwright, and M. I. Jordan. Annals of Statistics, 39, 1-47, 2011.
• Bayesian inference for queueing networks and modeling of Internet services. C. Sutton and M. I. Jordan. Annals of Applied Statistics, 5, 254-282, 2011.
• Genome-scale phylogenetic function annotation of large and diverse protein families. B. Engelhardt, M. I. Jordan, J. Srouji, and S. Brenner. Genome Research, doi/10.1101/gr.104687.109, 2011.
• A sticky HDP-HMM with application to speaker diarization. E. Fox, E. Sudderth, M. I. Jordan, and A. Willsky. Annals of Applied Statistics, 5, 1020-1056, 2011.
• Learning low-dimensional signal models. L. Carin, R. G. Baraniuk, V. Cevher, D. Dunson, M. I. Jordan, G. Sapiro, and M. B. Wakin. IEEE Signal Processing Magazine, 28, 39-51, 2011.
• Bayesian generalized kernel mixed models. Z. Zhang, G. Dai, and M. I. Jordan. Journal of Machine Learning Research, 12, 111-139, 2011.
• Bayesian nonparametric inference of switching linear dynamical models. E. Fox, E. Sudderth, M. I. Jordan, and A. Willsky. IEEE Transactions on Signal Processing, 59, 1569-1585, 2011.
• Message from the President: What are the open problems in Bayesian statistics? M. I. Jordan. ISBA Bulletin, 18(1), 1-4, 2011.
• Learning dependency-based compositional semantics. P. Liang, M. I. Jordan, and D. Klein. The 49th Annual Meeting of the Association for Computational Linguistics (ACL), [Long version].
• Nonparametric Bayesian co-clustering ensembles. P. Wang, K. B. Laskey, C. Domeniconi, and M. I. Jordan. SIAM International Conference on Data Mining (SDM), Phoenix, AZ, 2011.
• Dimensionality reduction for spectral clustering. D. Niu, J. Dy, and M. I. Jordan. In G. Gordon and D. Dunson (Eds.), Proceedings of the Fourteenth Conference on Artificial Intelligence and Statistics (AISTATS), Ft. Lauderdale, FL, 2011.
• Nonparametric combinatorial sequence models. F. Wauthier, M. I. Jordan, and N. Jojic. 15th Annual International Conference on Research in Computational Molecular Biology (RECOMB), Vancouver, BC, 2011.
• Supervised hierarchical Pitman-Yor process for natural scene segmentation. A. Shyr, T. Darrell, M. I. Jordan, and R. Urtasun. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Colorado Springs, CO, 2011.
• A unified probabilistic model for global and local unsupervised feature selection. Y. Guan, J. Dy, and M. I. Jordan. In L. Getoor and T. Scheffer (Eds.), Proceedings of the 28th International Conference on Machine Learning (ICML), Bellevue, WA, 2011.
• Managing data transfers in computer clusters with Orchestra. M. Chowdhury, M. Zaharia, J. Ma, M. I. Jordan, and I. Stoica (2011). ACM SIGCOMM, Toronto, Canada, 2011.
• Visually relating gene expression and in vivo DNA binding data. M.-Y. Huang, L. Mackey, S. Keranen, G. Weber, M. I. Jordan, D. Knowles, M. Biggin, and B. Hamann. IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM) Atlanta, GA, 2011.
• Variational inference over combinatorial spaces. A. Bouchard-Cote and M. I. Jordan. In J. Shawe-Taylor, R. Zemel, J. Lafferty, and C. Williams (Eds.), Advances in Neural Information Processing Systems (NIPS) 23, 2011. [Supplementary information].
• Random conic pursuit for semidefinite programming. A. Kleiner, A. Rahimi, and M. I. Jordan. In J. Shawe-Taylor, R. Zemel, J. Lafferty, and C. Williams (Eds.), Advances in Neural Information Processing Systems (NIPS) 23, 2011. [Supplementary information].
• Heavy-tailed process priors for selective shrinkage. F. L. Wauthier and M. I. Jordan. In J. Shawe-Taylor, R. Zemel, J. Lafferty, and C. Williams (Eds.), Advances in Neural Information Processing Systems (NIPS) 23, 2011.
• Tree-structured stick breaking for hierarchical data. R. Adams, Z. Ghahramani, and M. I. Jordan. In J. Shawe-Taylor, R. Zemel, J. Lafferty, and C. Williams (Eds.), Advances in Neural Information Processing Systems (NIPS) 23, 2011.
• Unsupervised kernel dimension reduction. M. Wang, F. Sha, and M. I. Jordan. In J. Shawe-Taylor, R. Zemel, J. Lafferty, and C. Williams (Eds.), Advances in Neural Information Processing Systems (NIPS) 23, 2011. [Supplementary information].

2010

• Bayesian nonparametric learning: Expressive priors for intelligent systems. M. I. Jordan. In R. Dechter, H. Geffner, and J. Halpern (Eds.), Heuristics, Probability and Causality: A Tribute to Judea Pearl, College Publications, 2010.
• Hierarchical models, nested models and completely random measures. M. I. Jordan. In M.-H. Chen, D. Dey, P. Mueller, D. Sun, and K. Ye (Eds.), Frontiers of Statistical Decision Making and Bayesian Analysis: In Honor of James O. Berger, New York: Springer, 2010.
• Feature space resampling for protein conformational search. B. Blum, M. I. Jordan, and D. Baker. Proteins: Structure, Function, and Bioinformatics, 78, 1583-1593, 2010. [Supplementary information].
• Neighbor-dependent Ramachandran probability distributions of amino acids developed from a hierarchical Dirichlet process model. D. Ting, G. Wang, M. Shapovalov, R. Mitra, M. I. Jordan, and R. Dunbrack. PLoS Computational Biology, 6, e1000763, 2010.
• The nested Chinese restaurant process and Bayesian inference of topic hierarchies. D. M. Blei, T. Griffiths, and M. I. Jordan. Journal of the ACM, 57, 1-30, 2010. [Software].
• Estimating divergence functionals and the likelihood ratio by convex risk minimization. X. Nguyen, M. J. Wainwright and M. I. Jordan. IEEE Transactions on Information Theory, 56, 5847-5861, 2010.
• Joint covariate selection and joint subspace selection for multiple classification problems. G. Obozinski, B. Taskar, and M. I. Jordan. Statistics and Computing, 20, 231-252, 2010.
• Convex and semi-nonnegative matrix factorizations. C. Ding, T. Li, and M. I. Jordan. IEEE Transactions on Pattern Analysis and Machine Intelligence, 32, 45-55, 2010.
• Active site prediction using evolutionary and structural information. S. Sankararaman, F. Sha, J. Kirsch, M. I. Jordan, and K. Sjolander. Bioinformatics, 26, 617-624, 2010.
• Regularized discriminant analysis, ridge regression and beyond. Z. Zhang, G. Dai, C. Xu, and M. I. Jordan. Journal of Machine Learning Research, 11, 2141-2170, 2010.
• Bayesian nonparametric methods for learning Markov switching processes. E. Fox, E. Sudderth, M. I. Jordan, and A. Willsky. IEEE Signal Processing Magazine, 27, 43-54, 2010.
• Leo Breiman. M. I. Jordan. Annals of Applied Statistics, 4, 1642-1643, 2010.
• Hierarchical Bayesian nonparametric models with applications. Y. W. Teh and M. I. Jordan. In N. Hjort, C. Holmes, P. Mueller, and S. Walker (Eds.), Bayesian Nonparametrics: Principles and Practice, Cambridge, UK: Cambridge University Press, 2010.
• Probabilistic grammars and hierarchical Dirichlet processes. P. Liang, M. I. Jordan, and D. Klein. In T. O'Hagan and M. West (Eds.), The Handbook of Applied Bayesian Analysis, Oxford University Press, 2010.
• Nonparametrics and graphical models: Discussion of Ickstadt et al. M. I. Jordan. In: J. M. Bernardo, M. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, A. F. M. Smith, and M. West (Eds.), Bayesian Statistics 9, 2010.
• An analysis of the convergence of graph Laplacians. D. Ting, L. Huang, and M. I. Jordan. Proceedings of the 27th International Conference on Machine Learning (ICML), Haifa, Israel, 2010.
• Multiple non-redundant spectral clustering views. D. Niu, J. Dy, and M. I. Jordan. Proceedings of the 27th International Conference on Machine Learning (ICML), Haifa, Israel, 2010.
• On the consistency of ranking algorithms. J. Duchi, L. Mackey, and M. I. Jordan. Proceedings of the 27th International Conference on Machine Learning (ICML), Haifa, Israel, 2010. [Best Student Paper Award].
• Mixed membership matrix factorization. L. Mackey, D. Weiss, and M. I. Jordan. Proceedings of the 27th International Conference on Machine Learning (ICML), Haifa, Israel, 2010. [Software].
• Learning programs: A hierarchical Bayesian approach. P. Liang, M. I. Jordan, and D. Klein. Proceedings of the 27th International Conference on Machine Learning (ICML), Haifa, Israel, 2010.
• Detecting large-scale system problems by mining console logs. W. Xu, L. Huang, A. Fox, D. Patterson, and M. I. Jordan. Proceedings of the 27th International Conference on Machine Learning (ICML), Haifa, Israel, 2010.
• Modeling events with cascades of Poisson processes. A. Simma and M. I. Jordan. In Uncertainty in Artificial Intelligence (UAI), Proceedings of the Twenty-Sixth Conference, Catalina Island, CA, 2010.
• Matrix-variate Dirichlet process mixture models. Z. Zhang, G. Dai, and M. I. Jordan. Proceedings of the Thirteenth Conference on Artificial Intelligence and Statistics (AISTATS), Sardinia, Italy, 2010.
• Inference and learning in networks of queues. C. Sutton and M. I. Jordan. Proceedings of the Thirteenth Conference on Artificial Intelligence and Statistics (AISTATS), Sardinia, Italy, 2010.
• Bayesian generalized kernel models. Z. Zhang, G. Dai, D. Wang, and M. I. Jordan. Proceedings of the Thirteenth Conference on Artificial Intelligence and Statistics (AISTATS), Sardinia, Italy, 2010.
• Characterizing, modeling, and generating workload spikes for stateful services. P. Bodik, A. Fox, M. Franklin, M. I. Jordan, and D. Patterson. First ACM Symposium on Cloud Computing (SOCC), Indianapolis, IN, 2010.
• Sufficient dimension reduction for visual sequence classification. A. Shyr, R. Urtasun, and M. I. Jordan. IEEE Conference on Computer Vision and Pattern Recognition (CVPR), San Francisco, CA, 2010.
• Type-based MCMC. P. Liang, M. I. Jordan, and D. Klein. The 11th Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT), Los Angeles, CA, 2010.
• Sharing features among dynamical systems with beta processes. E. Fox, E. Sudderth, M. I. Jordan, and A. S. Willsky. In Y. Bengio, D. Schuurmans, J. Lafferty and C. Williams (Eds.), Advances in Neural Information Processing Systems (NIPS) 22, 2010.
• Nonparametric latent feature models for link prediction. K. Miller, T. Griffiths, and M. I. Jordan. In Y. Bengio, D. Schuurmans, J. Lafferty and C. Williams (Eds.), Advances in Neural Information Processing Systems (NIPS) 22, 2010.
• An asymptotic analysis of smooth regularizers. P. Liang, F. Bach, G. Bouchard, and M. I. Jordan. In Y. Bengio, D. Schuurmans, J. Lafferty and C. Williams (Eds.), Advances in Neural Information Processing Systems (NIPS) 22, 2010.

2009

• On surrogate loss functions and f-divergences. X. Nguyen, M. J. Wainwright and M. I. Jordan. Annals of Statistics, 37, 876-904, 2009.
• Genomic privacy and the limits of individual detection in a pool. S. Sankararaman, G. Obozinski, M. I. Jordan, and E. Halperin, Nature Genetics, 41, 965-967, 2009.
• Kernel dimension reduction in regression. K. Fukumizu, F. R. Bach, and M. I. Jordan. Annals of Statistics, 37, 1871-1905, 2009.
• Joint estimation of gene conversion rates and mean conversion tract lengths from population SNP data. J. Yin, M. I. Jordan, and Y. Song. Bioinformatics, 25, i231-i239, 2009.
• Nonparametric Bayesian identification of jump systems with sparse dependencies. E. Fox, E. Sudderth, M. I. Jordan, and A. Willsky. 15th IFAC Symposium on System Identification (SYSID), St. Malo, France, 2009.
• Large-scale system problems detection by mining console logs. W. Xu, L. Huang, A. Fox, D. Patterson, and M. I. Jordan. 22nd ACM Symposium on Operating Systems Principles (SOSP), Big Sky, MT, 2009.
• Learning semantic correspondences with less supervision. P. Liang, M. I. Jordan, and D. Klein. Proceedings of the 47th Annual Meeting of the Association for Computational Linguistics (ACL), Singapore, 2009.
• Optimization of structured mean field objectives. A. Bouchard-Cote and M. I. Jordan. In Uncertainty in Artificial Intelligence (UAI), Proceedings of the Twenty-Fifth Conference, Montreal, Canada, 2009.
• Learning from measurements in exponential families. P. Liang, M. I. Jordan, and D. Klein. Proceedings of the 26th International Conference on Machine Learning (ICML), Montreal, Canada, 2009.
• Fast approximate spectral clustering. D. Yan, L. Huang, and M. I. Jordan. 15th ACM Conference on Knowledge Discovery and Data Mining (SIGKDD), Paris, France, 2009. [Software]. [Long version].
• Coherence functions for multicategory margin-based classification methods. Z. Zhang, M. I. Jordan, W-J. Li, and D-Y. Yeung. Proceedings of the Twelfth Conference on Artificial Intelligence and Statistics (AISTATS), Clearwater Beach, FL, 2009.
• A flexible and efficient algorithm for regularized Fisher discriminant analysis. Z. Zhang, G. Dai, and M. I. Jordan. In W. Buntine, M. Grobelnik, D. Mladenic, J. Shawe-Taylor (Eds.), Machine Learning and Knowledge Discovery in Databases: European Conference (ECML PKDD), Bled, Slovenia, 2009.
• Automatic exploration of datacenter performance regimes. P. Bodik, R. Griffith, C. Sutton, A. Fox, M. I. Jordan, and D. Patterson. First Workshop on Automated Control for Datacenters and Clouds (ACDC), Barcelona, Spain, 2009.
• Latent variable models for dimensionality reduction. Z. Zhang and M. I. Jordan. Proceedings of the Twelfth Conference on Artificial Intelligence and Statistics (AISTATS), Clearwater Beach, FL, 2009.
• Online system problem detection by mining patterns of console logs. W. Xu, L. Huang, A. Fox, D. Patterson, and M. I. Jordan. IEEE International Conference on Data Mining (ICDM), Miami, FL, 2009.
• Predicting multiple performance metrics for queries: Better decisions enabled by machine learning. A. Ganapathi, H. Kuno, U. Dayal, J. Wiener, A. Fox, M. I. Jordan, and D. Patterson. IEEE International Conference on Data Engineering (ICDE), 2009.
• Statistical machine learning makes automatic control practical for Internet datacenters. P. Bodik, R. Griffith, C. Sutton, A. Fox, M. I. Jordan, and D. Patterson. Workshop on Hot Topics in Cloud Computing (HotCloud), San Diego, CA, 2009.
• Shared segmentation of natural scenes using dependent Pitman-Yor processes. E. Sudderth and M. I. Jordan. In D. Koller, Y. Bengio, D. Schuurmans and L. Bottou (Eds.), Advances in Neural Information Processing Systems (NIPS) 21, 2009.
• Efficient inference in phylogenetic InDel trees. A. Bouchard-Cote, M. I. Jordan, and D. Klein. In D. Koller, Y. Bengio, D. Schuurmans and L. Bottou (Eds.), Advances in Neural Information Processing Systems (NIPS) 21, 2009.
• High-dimensional union support recovery in multivariate regression. G. Obozinski, M. J. Wainwright and M. I. Jordan. In D. Koller, Y. Bengio, D. Schuurmans and L. Bottou (Eds.), Advances in Neural Information Processing Systems (NIPS) 21, 2009. [Appendix].
• Nonparametric Bayesian learning of switching linear dynamical systems. E. B. Fox, E. Sudderth, M. I. Jordan, and A. S. Willsky. In D. Koller, Y. Bengio, D. Schuurmans and L. Bottou (Eds.), Advances in Neural Information Processing Systems (NIPS) 21, 2009.
• Spectral clustering with perturbed data. L. Huang, D. Yan, M. I. Jordan, and N. Taft. In D. Koller, Y. Bengio, D. Schuurmans and L. Bottou (Eds.), Advances in Neural Information Processing Systems (NIPS) 21, 2009. [Long version].
• DiscLDA: Discriminative learning for dimensionality reduction and classification. S. Lacoste-Julien, F. Sha, and M. I. Jordan. In D. Koller, Y. Bengio, D. Schuurmans and L. Bottou (Eds.), Advances in Neural Information Processing Systems (NIPS) 21, 2009.
• Posterior consistency of the Silverman g-prior in Bayesian model choice. Z. Zhang and M. I. Jordan. In D. Koller, Y. Bengio, D. Schuurmans and L. Bottou (Eds.), Advances in Neural Information Processing Systems (NIPS) 21, 2009.

2008

• Graphical models, exponential families, and variational inference. M. J. Wainwright and M. I. Jordan. Foundations and Trends in Machine Learning, 1, 1-305, 2008. [Substantially revised and expanded version of a 2003 technical report.]
• On the inference of ancestries in admixed populations. S. Sankararaman, G. Kimmel, E. Halperin, and M. I. Jordan. Genome Research, 18, 668-675, 2008.
• Multiway spectral clustering: A maximum margin perspective. Z. Zhang and M. I. Jordan. Statistical Science, 23, 383-403, 2008.
• A dual receptor cross-talk model of G protein-coupled signal transduction. P. Flaherty, M. A. Radhakrishnan, T. Dinh, M. I. Jordan, and A. P. Arkin. PLoS Computational Biology, 4, e1000185, 2008.
• Association mapping and significance estimation via the coalescent. G. Kimmel, R. Karp, M. I. Jordan, and E. Halperin. American Journal of Human Genetics, 83, 675-683, 2008.
• On optimal quantization rules for some sequential decision problems. X. Nguyen, M. J. Wainwright, and M. I. Jordan. IEEE Transactions on Information Theory, 54, 3285-3295, 2008.
• Consistent probabilistic outputs for protein function prediction. G. Obozinski, C. E. Grant, G. R. G. Lanckriet, M. I. Jordan, and W. S. Noble. Genome Biology, 9, S7, 2008.
• Quantitative gene function assignment from genomic datasets in M. musculus. L. Pena-Castillo, et al. Genome Biology, 9, S2, 2008.
• Probabilistic inference in queueing networks. C. A. Sutton and M. I. Jordan. Workshop on Tackling Computer Systems Problems with Machine Learning Techniques (SYSML), 2008.
• The phylogenetic Indian buffet process: A non-exchangeable nonparametric prior for latent features. K. Miller, T. Griffiths and M. I. Jordan. In Uncertainty in Artificial Intelligence (UAI), Proceedings of the Twenty-Fourth Conference, 2008.
• An HDP-HMM for systems with state persistence. E. Fox, E. Sudderth, M. I. Jordan, and A. Willsky. Proceedings of the 25th International Conference on Machine Learning (ICML), Helsinki, Finland, 2008.
• An analysis of generative, discriminative, and pseudolikelihood estimators. P. Liang and M. I. Jordan. Proceedings of the 25th International Conference on Machine Learning (ICML), Helsinki, Finland, 2008. [Best Student Paper Award].
• Mining console logs for large-scale system problem detection. W. Xu, L. Huang, A. Fox, D. Patterson, and M. I. Jordan. Workshop on Tackling Computer Systems Problems with Machine Learning Techniques (SYSML), 2008.
• Spectral clustering for speech separation. F. R. Bach and M. I. Jordan. In J. Keshet and S. Bengio (Eds.), Automatic Speech and Speaker Recognition: Large Margin and Kernel Methods. New York: John Wiley, 2008.
• Feature selection methods for improving protein structure prediction with Rosetta. B. Blum, M. I. Jordan, D. Kim, R. Das, P. Bradley, and D. Baker. In J. Platt, D. Koller, Y. Singer and A. McCallum (Eds.), Advances in Neural Information Processing Systems (NIPS) 20, 2008.
• Agreement-based learning. P. Liang, D. Klein and M. I. Jordan. In J. Platt, D. Koller, Y. Singer and A. McCallum (Eds.), Advances in Neural Information Processing Systems (NIPS) 20, 2008.
• Nonnegative matrix factorization for combinatorial optimization: Spectral clustering, graph matching, and clique finding. C. Ding, T. Li, and M. I. Jordan. IEEE International Conference on Data Mining (ICDM), 2008.
• Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization. X. Nguyen, M. J. Wainwright and M. I. Jordan. In J. Platt, D. Koller, Y. Singer and A. McCallum (Eds.), Advances in Neural Information Processing Systems (NIPS) 20, 2008.

2007

• A direct formulation for sparse PCA using semidefinite programming. A. d'Aspremont, L. El Ghaoui, M. I. Jordan, and G. R. G. Lanckriet. SIAM Review, 49, 434-448, 2007. [Winner of the 2008 SIAM Activity Group on Optimization Prize]. [Software].
• A randomization test for controlling population stratification in whole-genome association studies. G. Kimmel, M. I. Jordan, E. Halperin, R. Shamir, and R. Karp. American Journal of Human Genetics, 81, 895-905, 2007.
• Bayesian haplotype inference via the Dirichlet process. E. P. Xing, M. I. Jordan and R. Sharan. Journal of Computational Biology, 14, 267-284, 2007.
• Hierarchical beta processes and the Indian buffet process. R. Thibaux and M. I. Jordan. Proceedings of the Tenth Conference on Artificial Intelligence and Statistics (AISTATS), 2007.
• Regression on manifolds using kernel dimension reduction. J. Nilsson, F. Sha, and M. I. Jordan. Proceedings of the 24th International Conference on Machine Learning (ICML), 2007.
• The infinite PCFG using hierarchical Dirichlet processes. P. Liang, S. Petrov, M. I. Jordan, and D. Klein. Empirical Methods in Natural Language Processing (EMNLP), 2007.
• A permutation-augmented sampler for DP mixture models. P. Liang, M. I. Jordan, and B. Taskar. Proceedings of the 24th International Conference on Machine Learning (ICML), 2007.
• Nonparametric estimation of the likelihood ratio and divergence functionals. X. Nguyen, M. J. Wainwright and M. I. Jordan. International Symposium on Information Theory (ISIT), Nice, France, 2007.
• Learning multiscale representations of natural scenes using Dirichlet processes. J. J. Kivinen, E. B. Sudderth, and M. I. Jordan. IEEE International Conference on Computer Vision (ICCV), 2007.
• Communication-efficient online detection of network-wide anomalies. L. Huang, X. Nguyen, M. Garofalakis, J. M. Hellerstein, M. I. Jordan, A. Joseph, and N. Taft. 26th Annual IEEE Conference on Computer Communications (INFOCOM'07), 2007.
• Image denoising with nonparametric hidden Markov trees. J. J. Kivinen, E. B. Sudderth, and M. I. Jordan. IEEE International Conference on Image Processing (ICIP), 2007.
• Response-time modeling for resource allocation and energy-informed SLAs. P. Bodik, C. Sutton, A. Fox, D. Patterson, and M. I. Jordan. Workshop on Statistical Learning Techniques for Solving Systems Problems, Whistler, BC, 2007.
• Solving consensus and semi-supervised clustering problems using nonnegative matrix factorization. T. Li, C. Ding, and M. I. Jordan. IEEE International Conference on Data Mining (ICDM), 2007.
• In-network PCA and anomaly detection. L. Huang, X. Nguyen, M. Garofalakis, M. I. Jordan, A. Joseph, and N. Taft. In B. Schoelkopf, J. Platt and T. Hofmann (Eds.), Advances in Neural Information Processing Systems (NIPS) 19, 2007. [Long version].

2006

• Hierarchical Dirichlet processes. Y. W. Teh, M. I. Jordan, M. J. Beal and D. M. Blei. Journal of the American Statistical Association, 101, 1566-1581, 2006. [Software].
• Learning spectral clustering, with application to speech separation. F. R. Bach and M. I. Jordan. Journal of Machine Learning Research, 7, 1963-2001, 2006.
• Convexity, classification, and risk bounds. P. L. Bartlett, M. I. Jordan, and J. D. McAuliffe. Journal of the American Statistical Association, 101, 138-156, 2006.
• Log-determinant relaxation for approximate inference in discrete Markov random fields. M. J. Wainwright and M. I. Jordan. IEEE Transactions on Signal Processing, 54, 2099-2109, 2006.
• Nonparametric empirical Bayes for the Dirichlet process mixture model. J. D. McAuliffe, D. M. Blei and M. I. Jordan. Statistics and Computing, 16, 5-14, 2006.
• Structured prediction, dual extragradient and Bregman projections. B. Taskar, S. Lacoste-Julien and M. I. Jordan. Journal of Machine Learning Research, 7, 1627-1653, 2006.
• Mining the Caenorhabditis Genetic Center bibliography for genes related to life span. D. M. Blei, M. I. Jordan, and S. Mian. BMC Bioinformatics, 7, 250-269, 2006.
• Bayesian multi-population haplotype inference via a hierarchical Dirichlet process mixture. E. P. Xing, K.-A. Song, M. I. Jordan, and Y. W. Teh. Proceedings of the 23rd International Conference on Machine Learning (ICML), 2006.
• Statistical debugging: Simultaneous identification of multiple bugs. A. Zheng, M. I. Jordan, B. Liblit, M. Nayur, and A. Aiken. Proceedings of the 23rd International Conference on Machine Learning (ICML), 2006.
• A statistical graphical model for predicting protein molecular function. B. Engelhardt, M. I. Jordan, and S. Brenner. Proceedings of the 23rd International Conference on Machine Learning (ICML), 2006.
• Word alignment via quadratic assignment. S. Lacoste-Julien, B. Taskar, D. Klein, and M. I. Jordan. Proceedings of the North American Chapter of the Association for Computational Linguistics Annual Meeting (HLT-NAACL), 2006.
• Bayesian multicategory support vector machines. Z. Zhang, and M. I. Jordan. In Uncertainty in Artificial Intelligence (UAI), Proceedings of the Twenty-Second Conference, 2006.
• On optimal quantization rules for sequential decision problems. X. Nguyen, M. J. Wainwright and M. I. Jordan. International Symposium on Information Theory (ISIT), Seattle, WA, 2006. [Long version].
• Advanced tools for operators at Amazon.com. P. Bodik, A. Fox, M. I. Jordan, D. Patterson, A. Banerjee, R. Jagannathan, T. Su, S. Tenginakai, B. Turner, and J. Ingalls. First Workshop on Hot Topics in Autonomic Computing (HotAC), Dublin, Ireland, 2006.
• Comment on 'Support vector machines with applications'. P. L. Bartlett, M. I. Jordan, and J. D. McAuliffe. Statistical Science, 21, 341-346, 2006.
• Robust design of biological experiments. P. Flaherty, M. I. Jordan and A. P. Arkin. In Y. Weiss and B. Schoelkopf and J. Platt (Eds.), Advances in Neural Information Processing Systems (NIPS) 18, 2006.
• Structured prediction via the extragradient method. B. Taskar, S. Lacoste-Julien and M. I. Jordan. In Y. Weiss and B. Schoelkopf and J. Platt (Eds.), Advances in Neural Information Processing Systems (NIPS) 18, 2006. [Long version].
• Divergences, surrogate loss functions and experimental design. X. Nguyen, M. J. Wainwright and M. I. Jordan. In Y. Weiss and B. Schoelkopf and J. Platt (Eds.), Advances in Neural Information Processing Systems (NIPS) 18, 2006, [Long version].

2005

• Dirichlet processes, Chinese restaurant processes and all that. M. I. Jordan. Tutorial presentation at the NIPS Conference, 2005.
• Subtree power analysis and species selection for comparative genomics. J. D. McAuliffe, M. I. Jordan, and L. Pachter. Proceedings of the National Academy of Sciences, 102, 7900-7905, 2005.
• Variational inference for Dirichlet process mixtures. D. M. Blei and M. I. Jordan. Bayesian Analysis, 1, 121-144, 2005.
• Protein function prediction by Bayesian phylogenomics. B. E. Engelhardt, M. I. Jordan, K. E. Muratore, and S. E. Brenner. PLoS Computational Biology, e45, 2005.
• Nonparametric decentralized detection using kernel methods. X. Nguyen, M. J. Wainwright, and M. I. Jordan. IEEE Transactions on Signal Processing, 53, 4053-4066, 2005.
• Genome-wide requirements for resistance to functionally distinct DNA-damaging agents. L. William, R. P. St. Onge, M. Proctor, P. Flaherty, M. I. Jordan, A. P. Arkin, R. W. Davis, C. Nislow, and G. Giaever. PLoS Genetics, 1, 235-246, 2005.
• A kernel-based learning approach to ad hoc sensor network localization. X. Nguyen, M. I. Jordan, and B. Sinopoli. ACM Transactions on Sensor Networks, 1, 134-152, 2005.
• Sulfur and nitrogen limitation in Escherichia coli K12: specific homeostatic responses. P. Gyaneshwar, O. Paliy, J. McAuliffe, A. Jones, M. I. Jordan, and S. Kustu. Journal of Bacteriology, 187, 1074-1090, 2005.
• A latent variable model for chemogenomic profiling. P. Flaherty, G. Giaever, J. Kumm, M. I. Jordan, and A. P. Arkin. Bioinformatics, 21, 3286-3293, 2005.
• Predictive low-rank decomposition for kernel methods. F. R. Bach and M. I. Jordan. Proceedings of the 22nd International Conference on Machine Learning (ICML), 2005. [Matlab code]
• The DLR hierarchy of approximate inference. M. Rosen-Zvi, M. I. Jordan, and A. Yuille. In Uncertainty in Artificial Intelligence (UAI), Proceedings of the Twenty-First Conference, 2005.
• A variational principle for graphical models. M. J. Wainwright and M. I. Jordan. New Directions in Statistical Signal Processing: From Systems to Brain. Cambridge, MA: MIT Press, 2005.
• Scalable statistical bug isolation. B. Liblit, M. Naik, A. X. Zheng, A. Aiken, and M. I. Jordan. ACM SIGPLAN Conference on Programming Language Design and Implementation (PLDI), 2005. [Software]
• A probabilistic interpretation of canonical correlation analysis. F. R. Bach and M. I. Jordan. Technical Report 688, Department of Statistics, University of California, Berkeley, 2005.
• Extensions of the informative vector machine. N. D. Lawrence, J. C. Platt, & M. I. Jordan. In J. Winkler and N. D. Lawrence and M. Niranjan (Eds.), Proceedings of the Sheffield Machine Learning Workshop, Lecture Notes in Computer Science, New York: Springer, 2005.
• Discriminative training of Hidden Markov models for multiple pitch tracking. F. R. Bach and M. I. Jordan. Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2005.
• Multi-instrument musical transcription using a dynamic graphical model. B. Vogel, M. I. Jordan and D. Wessel. Proceedings of the International Conference on Acoustics, Speech and Signal Processing (ICASSP), 2005.
• Combining visualization and statistical analysis to improve operator confidence and efficiency for failure detection and localization. P. Bodik, G. Friedman, L. Biewald, H. Levine, G. Candea, K. Patel, G. Tolle, J. Hui, A. Fox, M. I. Jordan, and D. Patterson. International Conference on Autonomic Computing (ICAC), 2005.
• On information divergence measures, surrogate loss functions and decentralized hypothesis testing. X. Nguyen, M. J. Wainwright and M. I. Jordan. Forty-third Annual Allerton Conference on Communication, Control, and Computing, Urbana-Champaign, IL, 2005.
• Gaussian processes and the null-category noise model. N. D. Lawrence and M. I. Jordan. In O. Chapelle, B. Schoelkopf & A. Zien (Eds), Semi-Supervised Learning, Cambridge, MA: MIT Press, 2005.
• Semiparametric latent factor models. Y. W. Teh, M. Seeger, and M. I. Jordan. Proceedings of the Eighth Conference on Artificial Intelligence and Statistics (AISTATS), 2005.
• Sharing clusters among related groups: Hierarchical Dirichlet processes. Y. W. Teh, M. I. Jordan, M. J. Beal and D. M. Blei. In L. Saul, Y. Weiss, and L. Bottou (Eds.), Advances in Neural Information Processing Systems (NIPS) 17, 2005. [Long version]. [Software]
• Blind one-microphone speech separation: A spectral learning approach. F. R. Bach and M. I. Jordan. In L. Saul, Y. Weiss, and L. Bottou (Eds.), Advances in Neural Information Processing Systems (NIPS) 17, 2005.
• A direct formulation for sparse PCA using semidefinite programming. A. d'Aspremont, L. El Ghaoui, M. I. Jordan, and G. R. G. Lanckriet. In L. Saul, Y. Weiss, and L. Bottou (Eds.), Advances in Neural Information Processing Systems (NIPS) 17, 2005.
• Semi-supervised learning via Gaussian processes. N. D. Lawrence and M. I. Jordan. In L. Saul, Y. Weiss, and L. Bottou (Eds.), Advances in Neural Information Processing Systems (NIPS) 17, 2005.
• Computing regularization paths for learning multiple kernels. F. R. Bach, R. Thibaux, and M. I. Jordan. In L. Saul, Y. Weiss, and L. Bottou (Eds.), Advances in Neural Information Processing Systems (NIPS) 17, 2005. [Matlab code]

2004

• Graphical models. M. I. Jordan. Statistical Science (Special Issue on Bayesian Statistics), 19, 140-155, 2004.
• Multiple-sequence functional annotation and the generalized hidden Markov phylogeny. J. D. McAuliffe, L. Pachter, and M. I. Jordan. Bioinformatics, 20, 1850-1860, 2004.
• Learning graphical models for stationary time series. F. R. Bach and M. I. Jordan. IEEE Transactions on Signal Processing, 52, 2189-2199, 2004.
• Kalman filtering with intermittent observations. B. Sinopoli, L. Schenato, M. Franceschetti, K. Poolla, M. I. Jordan, and S. Sastry. IEEE Transactions on Automatic Control, 49, 1453-1464, 2004.
• Chemogenomic profiling: Identifying the functional interactions of small molecules in yeast. G. Giaever, P. Flaherty, J. Kumm, M. Proctor, D. F. Jaramillo, A. M. Chu, M. I. Jordan, A. P. Arkin, and R. W. Davis. Proceedings of the National Academy of Sciences, 3, 793-798, 2004.
• A statistical framework for genomic data fusion. G. R. G. Lanckriet, T. De Bie, N. Cristianini, M. I. Jordan, and W. S. Noble. Bioinformatics, 20, 2626-2635, 2004.
• Learning the kernel matrix with semidefinite programming. G. R. G. Lanckriet, N. Cristianini, L. El Ghaoui, P. L. Bartlett, and M. I. Jordan. Journal of Machine Learning Research, 5, 27-72, 2004.
• Dimensionality reduction for supervised learning with reproducing kernel Hilbert spaces. K. Fukumizu, F. R. Bach, and M. I. Jordan. Journal of Machine Learning Research, 5, 73-79, 2004.
• Robust sparse hyperplane classifiers: application to uncertain molecular profiling data. C. Bhattacharyya, L. R. Grate, M. I. Jordan, L. El Ghaoui, and Mian, I. S. Journal of Computational Biology, 11, 1073-1089, 2004. [Software]
• Discussion of boosting. P. L. Bartlett, M. I. Jordan, and J. D. McAuliffe. Annals of Statistics, 32, 85-91, 2004.
• LOGOS: A modular Bayesian model for de novo motif detection. E. P. Xing, W. Wu, M. I. Jordan, and R. M. Karp. Journal of Bioinformatics and Computational Biology, 2, 127-154, 2004.
• Treewidth-based conditions for exactness of the Sherali-Adams and Lasserre relaxations. M. J. Wainwright and M. I. Jordan. Technical Report 671, Department of Statistics, University of California, Berkeley, 2004.
• Multiple kernel learning, conic duality, and the SMO algorithm. F. R. Bach, G. R. G. Lanckriet, and M. I. Jordan. Proceedings of the 21st International Conference on Machine Learning (ICML), 2004. [Long version]. [Software].
• Bayesian haplotype inference via the Dirichlet process. E. P. Xing, R. Sharan, and M. I. Jordan. Proceedings of the 21st International Conference on Machine Learning (ICML), 2004.
• Decentralized detection and classification using kernel methods. X. Nguyen, M. J. Wainwright, and M. I. Jordan. Proceedings of the 21st International Conference on Machine Learning (ICML), 2004.
• Variational methods for the Dirichlet process. D. M. Blei and M. I. Jordan. Proceedings of the 21st International Conference on Machine Learning (ICML), 2004. [Long version].
• Sparse Gaussian process classification with multiple classes. M. Seeger and M. I. Jordan. Technical Report 661, Department of Statistics, University of California, Berkeley, 2004.
• Graph partition strategies for generalized mean field inference. E. P. Xing, M. I. Jordan, and S. Russell. In Uncertainty in Artificial Intelligence (UAI), Proceedings of the Twentieth Conference, 2004.
• Kernel-based data fusion and its application to protein function prediction in yeast. G. R. G. Lanckriet, M. Deng, N. Cristianini, M. I. Jordan, and W. S. Noble. Pacific Symposium on Biocomputing (PSB), 2004. [Supplementary information].
• Combining statistical monitoring and predictable recovery for self-management. A. Fox, E. Kiciman, D. A. Patterson, R. H. Katz and M. I. Jordan. ACM SIGSOFT Proceedings of the Workshop on Self-Managed Systems (WOSS), 2004.
• Public deployment of cooperative bug isolation. B. Liblit, A. Aiken, A. X. Zheng, and M. I. Jordan. Workshop on Remote Analysis and Measurement of Software Systems (RAMSS), 2004.
• Failure diagnosis using decision trees. M. Chen, A. X. Zheng, J. Lloyd, M. I. Jordan, and E. Brewer. International Conference on Autonomic Computing (ICAC), 2004.
• Semidefinite relaxations for approximate inference on graphs with cycles. M. J. Wainwright and M. I. Jordan. In S. Thrun, L. Saul, and B. Schoelkopf (Eds.), Advances in Neural Information Processing Systems (NIPS) 16, (long version), 2004.
• Learning spectral clustering. F. R. Bach and M. I. Jordan. In S. Thrun, L. Saul, and B. Schoelkopf (Eds.), Advances in Neural Information Processing Systems (NIPS) 16, (long version), 2004.
• Hierarchical topic models and the nested Chinese restaurant process. D. M. Blei, T. Griffiths, M. I. Jordan, and J. Tenenbaum. In S. Thrun, L. Saul, and B. Schoelkopf (Eds.), Advances in Neural Information Processing Systems (NIPS) 16, 2004.
• Kernel dimensionality reduction for supervised learning. K. Fukumizu, F. R. Bach, and M. I. Jordan. In S. Thrun, L. Saul, and B. Schoelkopf (Eds.), Advances in Neural Information Processing Systems (NIPS) 16, 2004.
• Large margin classifiers: convex loss, low noise, and convergence rates. P. L. Bartlett, M. I. Jordan, and J. D. McAuliffe. In S. Thrun, L. Saul, and B. Schoelkopf (Eds.), Advances in Neural Information Processing Systems (NIPS) 16, 2004.
• On the concentration of expectation and approximate inference in layered Bayesian networks. X. Nguyen and M. I. Jordan. In S. Thrun, L. Saul, and B. Schoelkopf (Eds.), Advances in Neural Information Processing Systems (NIPS) 16, (long version), 2004.
• Statistical debugging of sampled programs. A. X. Zheng, M. I. Jordan, B. Liblit, and A. Aiken. In S. Thrun, L. Saul, and B. Schoelkopf (Eds.), Advances in Neural Information Processing Systems (NIPS) 16, 2004.
• Autonomous helicopter flight via reinforcement learning. A. Y. Ng, H. J. Kim, M. I. Jordan, and S. Sastry. In S. Thrun, L. Saul, and B. Schoelkopf (Eds.), Advances in Neural Information Processing Systems (NIPS) 16, 2004.

2003

• Latent Dirichlet allocation. D. M. Blei, A. Y. Ng, and M. I. Jordan. Journal of Machine Learning Research, 3, 993-1022, 2003. [software].
• Toward a protein profile of Escherichia coli: Comparison to its transcription profile. R. W. Corbin, O. Paliy, F. Yang, J. Shabanowitz, M. Platt, C. E. Lyons, Jr., K. Root, J. D. McAuliffe, M. I. Jordan, S. Kustu, E. Soupene, and D. F. Hunt. Proceedings of the National Academy of Sciences, 100, 9232-9237, 2003.
• Beyond independent components: Trees and clusters. F. R. Bach and M. I. Jordan. Journal of Machine Learning Research, 4, 1205-1233, 2003. [Matlab code]
• Matching words and pictures. K. Barnard, P. Duygulu, N. de Freitas, D. A. Forsyth, D. M. Blei, and M. I. Jordan. Journal of Machine Learning Research, 3, 1107-1135, 2003.
• Hierarchical Bayesian models for applications in information retrieval. D. M. Blei, M. I. Jordan and A. Y. Ng. In: J. M. Bernardo, M. Bayarri, J. O. Berger, A. P. Dawid, D. Heckerman, A. F. M. Smith, and M. West (Eds.), Bayesian Statistics 7, 2003.
• Simultaneous relevant feature identification and classification in high-dimensional spaces: Application to molecular profiling data.. C. Bhattacharyya, L. R. Grate, A. Rizki, D. Radisky, F. J. Molina, M. I. Jordan, M. J. Bissell, and I. S. Mian. Signal Processing, 83, 729-743, 2003.
• An introduction to MCMC for machine learning. C. Andrieu, N. de Freitas, A. Doucet and M. I. Jordan. Machine Learning, 50, 5-43, 2003.
• Modeling annotated data. D. M. Blei and M. I. Jordan. 26th International Conference on Research and Development in Information Retrieval (SIGIR), New York: ACM Press, 2003.
• Bug isolation via remote program sampling. B. Liblit, A. Aiken, A. X. Zheng, and M. I. Jordan. ACM SIGPLAN 2003 Conference on Programming Language Design and Implementation (PLDI), San Diego, 2003.
• Variational inference in graphical models: The view from the marginal polytope. M. J. Wainwright and M. I. Jordan. Forty-first Annual Allerton Conference on Communication, Control, and Computing, Urbana-Champaign, IL, 2003.
• Kernel-based integration of genomic data using semidefinite programming. G. R. G. Lanckriet, N. Cristianini, M. I. Jordan, and W. S. Noble. In B. Schoelkopf, K. Tsuda and J-P. Vert (Eds.), Kernel Methods in Computational Biology, Cambridge, MA: MIT Press, 2003.
• On semidefinite relaxation for normalized k-cut and connections to spectral clustering. E. P. Xing and M. I. Jordan. Technical Report CSD-03-1265, Computer Science Division, University of California, Berkeley, 2003.
• Support vector machines for analog circuit performance representation. F. De Bernardinis, M. I. Jordan, and A. L. Sangiovanni-Vincentelli. Proceedings of the Design Automation Conference (DAC), 2003.
• Robust novelty detection with single-class MPM. G. R. G. Lanckriet, L. El Ghaoui, and M. I. Jordan. In S. Becker, S. Thrun, and K. Obermayer (Eds.), Advances in Neural Information Processing Systems (NIPS) 15, 2003.
• A generalized mean field algorithm for variational inference in exponential families. E. P. Xing, M. I. Jordan, and S. Russell. In C. Meek and U. Kjaerulff, Uncertainty in Artificial Intelligence (UAI), Proceedings of the Eighteenth Conference, 2003.
• Kernel independent component analysis. F. R. Bach and M. I. Jordan. International Conference on Acoustics, Speech, and Signal Processing (ICASSP), 2003, [Long version]. [Matlab code]
• Kalman filtering with intermittent observations. B. Sinopoli, L. Schenato, M. Franceschetti, K. Poolla, M. I. Jordan, and S. Sastry. 42nd IEEE Conference on Decision and Control (CDC), 2004.
• Learning graphical models with Mercer kernels. F. R. Bach and M. I. Jordan. In S. Becker, S. Thrun, and K. Obermayer (Eds.), Advances in Neural Information Processing Systems (NIPS) 15, 2003.
• A minimal intervention principle for coordinated movement. E. Todorov and M. I. Jordan. In S. Becker, S. Thrun, and K. Obermayer (Eds.), Advances in Neural Information Processing Systems (NIPS) 15, 2003.
• Finding clusters in independent component analysis. F. R. Bach and M. I. Jordan. Fourth International Symposium on Independent Component Analysis and Blind Signal Separation (ICA), 2003.
• A hierarchical Bayesian Markovian model for motifs in biopolymer sequences. E. P. Xing, M. I. Jordan, R. M. Karp and S. Russell. In S. Becker, S. Thrun, and K. Obermayer (Eds.), Advances in Neural Information Processing Systems (NIPS) 15, 2003.
• Integrated analysis of transcript profiling and protein sequence data. L. R. Grate, C. Bhattacharyya, M. I. Jordan, and I. S. Mian. Mechanisms of Ageing and Development, 124, 109-114, 2003.
• Distance metric learning, with application to clustering with side-information. E. P. Xing, A. Y. Ng, M. I. Jordan and S. Russell. In S. Becker, S. Thrun, and K. Obermayer (Eds.), Advances in Neural Information Processing Systems (NIPS) 15, 2003.
• Sampling user executions for bug isolation. B. Liblit, A. Aiken, A. X. Zheng, and M. I. Jordan. Workshop on Remote Analysis and Measurement of Software Systems (RAMSS), 2003.
• LOGOS: A modular Bayesian model for de novo motif detection. E. P. Xing, W. Wu, M. I. Jordan, and R. M. Karp. IEEE Computer Society Bioinformatics Conference (CSB), 2004.

2002

• Kernel independent component analysis. F. R. Bach and M. I. Jordan. Journal of Machine Learning Research, 3, 1-48, 2002. [Matlab code]
• Optimal feedback control as a theory of motor coordination. E. Todorov and M. I. Jordan. Nature Neuroscience, 5, 1226-1235, 2002. [Supplementary information]. [News and views].
• A robust minimax approach to classification. G. R. G. Lanckriet, L. El Ghaoui, C. Bhattacharyya, and M. I. Jordan. Journal of Machine Learning Research, 3, 552-582, 2002. [Matlab code]
• Sensorimotor adaptation of speech I: Compensation and adaptation. J. F. Houde and M. I. Jordan. Journal of Speech, Language, and Hearing Research, 45, 239-262, 2002.
• Graphical models: Probabilistic inference. M. I. Jordan and Y. Weiss. In M. Arbib (Ed.), The Handbook of Brain Theory and Neural Networks, 2nd edition. Cambridge, MA: MIT Press, 2002.
• Loopy belief propagation and Gibbs measures. S. Tatikonda and M. I. Jordan. In D. Koller and A. Darwiche (Eds)., Uncertainty in Artificial Intelligence (UAI), Proceedings of the Eighteenth Conference, 2002.
• Tree-dependent component analysis. F. R. Bach and M. I. Jordan. In D. Koller and A. Darwiche (Eds)., Uncertainty in Artificial Intelligence (UAI), Proceedings of the Eighteenth Conference, 2002. [Matlab code]
• Random sampling of a continuous-time stochastic dynamical system. M. Micheli and M. I. Jordan. Proceedings of the Fifteenth International Symposium on Mathematical Theory of Networks and Systems, 2002.
• Learning the kernel matrix with semidefinite programming. G. R. G. Lanckriet, P. L. Bartlett, N. Cristianini, L. El Ghaoui, and M. I. Jordan. Machine Learning: Proceedings of the Nineteenth International Conference (ICML), San Mateo, CA: Morgan Kaufmann, 2002.
• Thin junction trees. F. R. Bach and M. I. Jordan. In T. Dietterich, S. Becker and Z. Ghahramani (Eds.), Advances in Neural Information Processing Systems (NIPS) 14, 2002.
• On spectral clustering: Analysis and an algorithm. A. Y. Ng, M. I. Jordan, and Y. Weiss. In T. Dietterich, S. Becker and Z. Ghahramani (Eds.), Advances in Neural Information Processing Systems (NIPS) 14, 2002.
• Minimax probability machine. G. R. G. Lanckriet, L. El Ghaoui, C. Bhattacharyya, and M. I. Jordan. In T. Dietterich, S. Becker and Z. Ghahramani (Eds.), Advances in Neural Information Processing Systems (NIPS) 14, 2002.
• On discriminative vs. generative classifiers: A comparison of logistic regression and naive Bayes. A. Y. Ng and M. I. Jordan. In T. Dietterich, S. Becker and Z. Ghahramani (Eds.), Advances in Neural Information Processing Systems (NIPS) 14, 2002.
• Latent Dirichlet allocation. D. M. Blei, A. Y. Ng and M. I. Jordan. In T. Dietterich, S. Becker and Z. Ghahramani (Eds.), Advances in Neural Information Processing Systems (NIPS) 14, 2002, [Long version], [software].
• Simultaneous relevant feature identification and classification in high-dimensional spaces. L. R. Grate, C. Bhattacharyya, M. I. Jordan and I. S. Mian. Workshop on Algorithms in Bioinformatics, 2002. [matlab code], [perl/lp_solve code].
• Learning in modular and hierarchical systems. M. I. Jordan and R. A. Jacobs. In M. Arbib (Ed.), The Handbook of Brain Theory and Neural Networks, 2nd edition. Cambridge, MA: MIT Press, 2002.

2001

• Stable algorithms for link analysis. A. Y. Ng, A. X. Zheng, and M. I. Jordan. Proceedings of the 24th International Conference on Research and Development in Information Retrieval (SIGIR), New York, NY: ACM Press, 2001.
• Efficient stepwise selection in decomposable models. A. Deshpande, M. N. Garofalakis, and M. I. Jordan. In J. Breese and D. Koller (Ed)., Uncertainty in Artificial Intelligence (UAI), Proceedings of the Seventeenth Conference, 2001.
• Convergence rates of the Voting Gibbs classifier, with application to Bayesian feature selection. A. Y. Ng and M. I. Jordan. Machine Learning: Proceedings of the Eighteenth International Conference, San Mateo, CA: Morgan Kaufmann, 2001.
• Link analysis, eigenvectors, and stability. A. Y. Ng, A. X. Zheng, and M. I. Jordan. International Joint Conference on Artificial Intelligence (IJCAI), 2001.
• Variational MCMC. N. de Freitas, P. Højen-Sørensen, M. I. Jordan, and S. Russell. In J. Breese and D. Koller (Ed)., Uncertainty in Artificial Intelligence (UAI), Proceedings of the Seventeenth Conference, 2001.
• Feature selection for high-dimensional genomic microarray data. E. P. Xing, M. I. Jordan, and R. M. Karp. Machine Learning: Proceedings of the Eighteenth International Conference, San Mateo, CA: Morgan Kaufmann, 2001.
• Discorsi sulle reti neurali e l'apprendimento. C. Domeniconi and M. I. Jordan. Milan: Edizioni Franco Angeli, 2001.

2000

• Learning with mixtures of trees. M. Meila and M. I. Jordan. Journal of Machine Learning Research, 1, 1-48, 2000.
• Attractor dynamics for feedforward neural networks. L. K. Saul and M. I. Jordan. Neural Computation, 12, 1313-1335, 2000.
• Bayesian logistic regression: a variational approach. T. S. Jaakkola and M. I. Jordan. Statistics and Computing, 10, 25-37, 2000.
• Asymptotic convergence rate of the EM algorithm for gaussian mixtures. J. Ma, L. Xu, and M. I. Jordan. Neural Computation, 12, 2881-290, 2000.
• PEGASUS: A policy search method for large MDPs and POMDPs. A. Y. Ng and M. I. Jordan. Uncertainty in Artificial Intelligence (UAI), Proceedings of the Sixteenth Conference, 2000.
• Approximate inference algorithms for two-layer Bayesian networks. A. Y. Ng and M. I. Jordan. Advances in Neural Information Processing Systems (NIPS) 12, Cambridge MA: MIT Press, 2000.

1999

• Mixed memory Markov models: Decomposing complex stochastic processes as mixture of simpler ones. L. K. Saul and M. I. Jordan. Machine Learning, 37, 75-87, 1999.
• Variational probabilistic inference and the QMR-DT network. T. S. Jaakkola and M. I. Jordan. Journal of Artificial Intelligence Research, 10, 291-322, 1999.
• Are reaching movements planned to be straight and invariant in the extrinsic space? M. Desmurget, C. Prablanc, M. I. Jordan, and M. Jeannerod, M. Quarterly Journal of Experimental Psychology, 52, 981-1020, 1999.
• Loopy belief-propagation for approximate inference: An empirical study. K. Murphy, Y. Weiss, and M. I. Jordan. In K. B. Laskey and H. Prade (Eds.), Uncertainty in Artificial Intelligence (UAI), Proceedings of the Fifteenth Conference, San Mateo, CA: Morgan Kaufmann, 1999.
• Learning from dyadic data. T. Hofmann, J. Puzicha, and M. I. Jordan. In Kearns, M. S., Solla, S. A., and Cohn, D. (Eds.), Advances in Neural Information Processing Systems (NIPS) 11, Cambridge MA: MIT Press, 1999.
• An introduction to variational methods for graphical models. M. I. Jordan, Z. Ghahramani, T. S. Jaakkola, and L. K. Saul. In M. I. Jordan (Ed.), Learning in Graphical Models, Cambridge: MIT Press, 1999.
• Computational motor control. M. I. Jordan and D. M. Wolpert. In M. Gazzaniga (Ed.), The Cognitive Neurosciences, 2nd edition, Cambridge: MIT Press, 1999.
• Improving the mean field approximation via the use of mixture distributions. T. S. Jaakkola and M. I. Jordan. In M. I. Jordan (Ed.), Learning in Graphical Models, Cambridge: MIT Press, 1999.
• Learning in graphical models. M. I. Jordan (Ed.), Cambridge MA: MIT Press, 1999.
• Recurrent networks. M. I. Jordan. In R. A. Wilson and F. C. Keil (Eds.), The MIT Encyclopedia of the Cognitive Sciences, Cambridge, MA: MIT Press, 1999.
• Neural networks. M. I. Jordan. In R. A. Wilson and F. C. Keil (Eds.), The MIT Encyclopedia of the Cognitive Sciences, Cambridge, MA: MIT Press, 1999.
• Computational intelligence. M. I. Jordan, and S. Russell In R. A. Wilson and F. C. Keil (Eds.), The MIT Encyclopedia of the Cognitive Sciences, Cambridge, MA: MIT Press, 1999.

1998

• Adaptation in speech production. J. Houde and M. I. Jordan. Science, 279, 1213-1216, 1998.
• Smoothness maximization along a predefined path accurately predicts the speed profiles of complex arm movements. E. Todorov and M. I. Jordan. Journal of Neurophysiology, 80, 696-714, 1998.
• The role of inertial sensitivity in motor planning. P. N. Sabes, M. I. Jordan and D. M. Wolpert. Journal of Neuroscience, 18, 5948-5959, 1998.
• Approximating posterior distributions in belief networks using mixtures. C. M. Bishop, N. D. Lawrence, T. S. Jaakkola, and M. I. Jordan. In Jordan, M. I., Kearns, M. J. and Solla, S. A. (Eds.), Advances in Neural Information Processing Systems (NIPS) 10, Cambridge, MA: MIT Press, 1998.
• Estimating dependency structure as a hidden variable. M. Meila and M. I. Jordan. In Jordan, M. I., Kearns, M. J. and Solla, S. A. (Eds.), Advances in Neural Information Processing Systems (NIPS) 10, Cambridge, MA: MIT Press, 1998.
• Advances in neural information processing systems 10, M. I. Jordan, M. J. Kearns, and S. A. Solla, (Eds.), Cambridge MA: MIT Press, 1998.
• Adaptation in speech motor control. J. F. Houde and M. I. Jordan. In Jordan, M. I., Kearns, M. J. and Solla, S. A. (Eds.), Advances in Neural Information Processing Systems (NIPS) 10, Cambridge, MA: MIT Press, 1998.
• Mixture representations for inference and learning in Boltzmann machines. N. D. Lawrence, C. M. Bishop and M. I. Jordan. In G. F. Cooper and S. Moral (Eds.), Uncertainty in Artificial Intelligence (UAI), Proceedings of the Fourteenth Conference, San Mateo, CA: Morgan Kaufman, 1998.

1997

• Factorial hidden Markov models. Z. Ghahramani and M. I. Jordan. Machine Learning, 29, 245--273, 1997.
• Obstacle avoidance and a perturbation sensitivity model for motor planning. P. N. Sabes and M. I. Jordan. Journal of Neuroscience, 17, 7119-7128, 1997.
• Probabilistic independence networks for hidden Markov probability models. P. Smyth, D. Heckerman, and M. I. Jordan. Neural Computation, 9, 227-270, 1997.
• Viewing the hand prior to movement improves accuracy of pointing performed toward the unseen contralateral hand. M. Desmurget, Y. Rossetti, M. I. Jordan, C. Meckler, and C. Prablanc. Experimental Brain Research, 115, 180--186, 1997.
• Constrained and unconstrained movements involve different control strategies. M. Desmurget, M. I. Jordan, C. Prablanc, and M. Jeannerod. Journal of Neurophysiology, 77, 1644--1650, 1997.
• Optimal triangulation with continuous cost functions. M. Meila and M. I. Jordan. In M. C. Mozer, M. I. Jordan, and T. Petsche (Eds.), Advances in Neural Information Processing Systems (NIPS) 9, Cambridge MA: MIT Press, 1997.
• A variational principle for model-based interpolation. L. K. Saul and M. I. Jordan. In M. C. Mozer, M. I. Jordan, and T. Petsche (Eds.), Advances in Neural Information Processing Systems (NIPS) 9, Cambridge MA: MIT Press, 1997.
• Recursive algorithms for approximating probabilities in graphical models. T. S. Jaakkola and M. I. Jordan. In M. C. Mozer, M. I. Jordan, and T. Petsche (Eds.), Advances in Neural Information Processing Systems (NIPS) 9, Cambridge MA: MIT Press, 1997.
• Hidden Markov decision trees. M. I. Jordan, Z. Ghahramani, and L. K. Saul. In M. C. Mozer, M. I. Jordan, and T. Petsche (Eds.), Advances in Neural Information Processing Systems (NIPS) 9, Cambridge MA: MIT Press, 1997.
• Neural networks. M. I. Jordan and C. Bishop. In Tucker, A. B. (Ed.), CRC Handbook of Computer Science, Boca Raton, FL: CRC Press, 1997.
• Computational models of sensorimotor organization. Z. Ghahramani, D. M. Wolpert, and M. I. Jordan. In P. Morasso and V. Sanguineti (Eds.), Self-Organization Computational Maps and Motor Control, Amsterdam: North-Holland, 1997.
• Advances in neural information processing systems 9, M. Mozer, M. I. Jordan, and T. Petsche, (Eds.), Cambridge MA: MIT Press, 1997.
• Mixture models for learning from incomplete data. Z. Ghahramani and M. I. Jordan. In Greiner, R., Petsche, T., and Hanson, S. J. (Eds.), Computational Learning Theory and Natural Learning Systems, Cambridge, MA: MIT Press, 1997.
• Active learning with statistical models. D. Cohn, Z. Ghahramani, and M. I. Jordan. In Murray-Smith, R., and Johansen, T. A. (Eds.), Multiple Model Approaches to Modelling and Control, London: Taylor and Francis, 1997.
• An objective function for belief net triangulation. M. Meila and M. I. Jordan. In D. Madigan and P. Smyth (Eds.), Proceedings of the 1997 Conference on Artificial Intelligence and Statistics, Ft. Lauderdale, FL, 1997.
• Markov mixtures of experts. M. Meila and M. I. Jordan. In Murray-Smith, R., and Johansen, T. A. (Eds.), Multiple Model Approaches to Modelling and Control, London: Taylor and Francis, 1997.
• Serial order: A parallel, distributed processing approach. M. I. Jordan. In J. W. Donahoe and V. P. Dorsel, (Eds.). Neural-network Models of Cognition: Biobehavioral Foundations, Amsterdam: Elsevier Science Press, 1997.

1996

• Mean field theory for sigmoid belief networks. L. K. Saul, T. Jaakkola, and M. I. Jordan. Journal of Artificial Intelligence Research, 4, 61-76, 1996.
• Generalization to local remappings of the visuomotor coordinate representation. Z. Ghahramani, D. M. Wolpert, and M. I. Jordan. Journal of Neuroscience, 16, 7085-7096, 1996.
• Active learning with statistical models. D. Cohn, Z. Ghahramani, and M. I. Jordan. Journal of Artificial Intelligence Research, 4, 129-145, 1996.
• On convergence properties of the EM Algorithm for Gaussian mixtures. L. Xu and M. I. Jordan. Neural Computation, 8, 129-151, 1996.
• Local linear perceptrons for classification. E. Alpaydin and M. I. Jordan. IEEE Transactions on Neural Networks, 7, 788--792, 1996.
• Computational aspects of motor control and motor learning. M. I. Jordan. In H. Heuer and S. Keele (Eds.), Handbook of Perception and Action: Motor Skills, New York: Academic Press, 1996.
• Fast learning by bounding likelihoods in sigmoid belief networks. T. S. Jaakkola, L. K. Saul, and M. I. Jordan. In D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo (Eds.), Advances in Neural Information Processing Systems (NIPS) 8, Cambridge MA: MIT Press, 1996.
• Reinforcement learning by probability matching. P. N. Sabes and M. I. Jordan. In D. S. Touretzky, M. C. Mozer, and M. E. Hasselmo (Eds.), Advances in Neural Information Processing Systems (NIPS) 8, Cambridge MA: MIT Press, 1996.
• Computing upper and lower bounds on likelihoods in intractable networks. T. S. Jaakkola and M. I. Jordan. In E. Horvitz (Ed.), Uncertainty in Artificial Intelligence (UAI), Proceedings of the Twelth Conference, Portland, Oregon, 1996.
• Exploiting tractable substructures in intractable networks. L. K. Saul and M. I. Jordan. In D. Touretzky, M. Mozer, and M. Hasselmo (Eds.), Advances in Neural Information Processing Systems (NIPS) 8, MIT Press, 1996.
• Markov mixtures of experts. M. Meila and M. I. Jordan. In D. Touretzky, M. Mozer, and M. Hasselmo (Eds.), Advances in Neural Information Processing Systems (NIPS) 8, MIT Press, 1996.
• Factorial Hidden Markov models. Z. Ghahramani and M. I. Jordan. In D. Touretzky, M. Mozer, and M. Hasselmo (Eds.), Advances in Neural Information Processing Systems (NIPS) 8, MIT Press, 1996.

1995

• An internal forward model for sensorimotor integration. D. M. Wolpert, Z. Ghahramani, and M. I. Jordan. Science, 269, 1880--1882, 1995.
• Are arm trajectories planned in kinematic or dynamic coordinates? An adaptation study. D. M. Wolpert, Z. Ghahramani, and M. I. Jordan. Experimental Brain Research, 103, 460-470, 1995.
• Convergence results for the EM approach to mixtures of experts architectures. M. I. Jordan and L. Xu. Neural Networks, 8, 1409-1431, 1995.
• The organization of action sequences: Evidence from a relearning task. M. I. Jordan. Journal of Motor Behavior, 27, 179--192, 1995.
• Adaptation in speech production to transformed auditory feedback. J. Houde and M. I. Jordan. Journal of the Acoustical Society of America, 97, 3243.
• Boltzmann chains and hidden Markov Models. L. K. Saul and M. I. Jordan. In G. Tesauro, D. S. Touretzky and T. K. Leen, (Eds.), Advances in Neural Information Processing Systems (NIPS) 7, MIT Press, 1995.
• Reinforcement learning algorithm for partially observable Markov decision problems. T. S. Jaakkola, S. P. Singh, and M. I. Jordan. In G. Tesauro, D. S. Touretzky and T. K. Leen, (Eds.), Advances in Neural Information Processing Systems (NIPS) 7, Cambridge, MA: MIT Press, 1995.
• Learning in modular and hierarchical systems. M. I. Jordan and R. A. Jacobs. In M. Arbib (Ed.), The Handbook of Brain Theory and Neural Networks, Cambridge, MA: MIT Press, 1995.
• Reinforcement learning with soft state aggregation. S. P. Singh, T. S. Jaakkola, and M. I. Jordan. In G. Tesauro, D. S. Touretzky and T. K. Leen, (Eds.), Advances in Neural Information Processing Systems (NIPS) 7, Cambridge, MA: MIT Press, 1995.
• Why the logistic function? A tutorial discussion on probabilities and neural networks. M. I. Jordan. MIT Computational Cognitive Science Report 9503, August 1995.
• Computational structure of coordinate transformations: A generalization study. Z. Ghahramani, D. M. Wolpert, and M. I. Jordan. In G. Tesauro, D. S. Touretzky and T. K. Leen, (Eds.), Advances in Neural Information Processing Systems (NIPS) 7, Cambridge, MA: MIT Press, 1995.
• Neural forward dynamic models in human motor control: Psychophysical evidence. D. M. Wolpert, Z. Ghahramani, and M. I. Jordan. In G. Tesauro, D. S. Touretzky and T. K. Leen, (Eds.), Advances in Neural Information Processing Systems (NIPS) 7, Cambridge, MA: MIT Press, 1995.
• An alternative model for mixtures of experts. L. Xu, M. I. Jordan, and G. E. Hinton. In G. Tesauro, D. S. Touretzky and T. K. Leen, (Eds.), Advances in Neural Information Processing Systems (NIPS) 7, Cambridge, MA: MIT Press, 1995.
• Active learning with statistical models. D. Cohn, Z. Ghahramani, and M. I. Jordan. In G. Tesauro, D. S. Touretzky and T. K. Leen, (Eds.), Advances in Neural Information Processing Systems (NIPS) 7, Cambridge, MA: MIT Press, 1995.
• The moving basin: Effective action-search in adaptive control. W. Fun and M. I. Jordan, M. I. Proceedings of the World Conference on Neural Networks, Washington, DC, 1995.
• Goal-based speech motor control: A theoretical framework and some preliminary data. J. S. Perkell, M. L. Matthies, M. A. Svirsky, and M. I. Jordan. In D. A. Robin, K. M. Yorkston, and D. R. Beukelman (Eds.), Disorders of Motor Speech: Assessment, Treatment, and Clinical Characterization, Baltimore, MD: Brookes Publishing Co, 1993.

1994

• Hierarchical mixtures of experts and the EM algorithm. M. I. Jordan and R. A. Jacobs. Neural Computation, 6, 181-214, 1994.
• Learning in Boltzmann trees. L. K. Saul and M. I. Jordan. Neural Computation, 6, 1173-1183, 1994.
• Perceptual distortion contributes to the curvature of human reaching movements. D. M. Wolpert, Z. Ghahramani, and M. I. Jordan. Experimental Brain Research, 98, 153-156, 1994.
• On the convergence of stochastic iterative dynamic programming algorithms. T. Jaakkola, M. I. Jordan and S. Singh. Neural Computation, 6, 1183--1190, 1994.
• A model of the learning of arm trajectories from spatial targets. M. I. Jordan, T. Flash, and Y. Arnon. Journal of Cognitive Neuroscience, 6, 359--376, 1994.
• Learning without state estimation in partially observable Markovian decision processes. S. P. Singh, T. S. Jaakkola, and M. I. Jordan. Machine Learning: Proceedings of the Eleventh International Conference, San Mateo, CA: Morgan Kaufmann, 284--292, 1994.
• Supervised learning from incomplete data via the EM approach. Z. Ghahramani and M. I. Jordan. In Cowan, J., Tesauro, G., and Alspector, J., (Eds.), Advances in Neural Information Processing Systems 6, San Mateo, CA: Morgan Kaufmann, 1994.
• A statistical approach to decision tree modeling. M. I. Jordan. In M. Warmuth (Ed.), Proceedings of the Seventh Annual ACM Conference on Computational Learning Theory, New York: ACM Press, 1994.
• Learning from incomplete data. Z. Ghahramani and M. I. Jordan. MIT Center for Biological and Computational Learning Technical Report 108, 1994.
• Theoretical and experimental studies of convergence properties of EM algorithm based on finite Gaussian mixtures. L. Xu and M. I. Jordan, M. I. Proceedings of the 1994 International Symposium on Artificial Neural Networks, Tainan, Taiwan, pp. 380--385, 1994.
• A statistical approach to decision tree modeling. M. I. Jordan. In M. Warmuth (Ed.), Proceedings of the Seventh Annual ACM Conference on Computational Learning Theory, New York: ACM Press, 1994.

Pre-1994

• Forward models: Supervised learning with a distal teacher. M. I. Jordan and D. E. Rumelhart. Cognitive Science, 16, 307-354, 1992.
• Adaptive mixtures of local experts. R. A. Jacobs, M. I. Jordan, S. Nowlan, and G. E. Hinton. Neural Computation, 3, 1-12, 1991.
• Learning piecewise control strategies in a modular neural network architecture. R. A. Jacobs and M. I. Jordan. IEEE Transactions on Systems, Man, and Cybernetics, 23, 337--345, 1993.
• Trading relations between tongue-body raising and lip rounding in production of the vowel /u/: A pilot motor equivalence study. J. S. Perkell, M. L. Matthies, M. A. Svirsky, and M. I. Jordan. Journal of the Acoustical Society of America, 93, 2948--2961, 1993.
• Supervised learning and divide-and-conquer: A statistical approach. M. I. Jordan, and R. A. Jacobs. In P. E. Utgoff, (Ed.), Machine Learning: Proceedings of the Tenth International Workshop, San Mateo, CA: Morgan Kaufmann, 1993.
• A dynamical model of priming and repetition blindness. D. Bavelier and M. I. Jordan. In Hanson, S. J., Cowan, J. D., and Giles, C. L., (Eds.), Advances in Neural Information Processing Systems (NIPS) 5, San Mateo, CA: Morgan Kaufmann, 1993.
• EM learning of a generalized finite mixture model for combining multiple classifiers. L. Xu and M. I. Jordan. Proceedings of the World Conference on Neural Networks, Portland, OR, pp. 431--434, 1993.
• The cascade neural network model and a speed-accuracy tradeoff of arm movement. M. Hirayama, M. Kawato, and M. I. Jordan. Journal of Motor Behavior, 25, 162--175, 1993.
• Constrained supervised learning. M. I. Jordan. Journal of Mathematical Psychology, 36, 396--425, 1992.
• Computational consequences of a bias towards short connections. R. A. Jacobs and M. I. Jordan. Journal of Cognitive Neuroscience, 4, 331--344, 1992.
• Hierarchies of adaptive experts. M. I. Jordan and R. A. Jacobs. In J. Moody, S. Hanson, and R. Lippmann (Eds.), Advances in Neural Information Processing Systems (NIPS) 4, San Mateo, CA: Morgan Kaufmann, 1992.
• Forward dynamics modeling of speech motor control using physiological data. M. Hirayama, E. Vatikiotis-Bateson, M. Kawato, and M. I. Jordan. In J. Moody, S. Hanson, and R. Lippmann (Eds.), Advances in Neural Information Processing Systems (NIPS) 4, San Mateo, CA: Morgan Kaufmann, 1992.
• Supervised learning and excess degrees of freedom. Jordan, M. I. In P. Mehra, and B. Wah, (Eds.), Artificial Neural Networks: Concepts and Theory, Los Alamitos, CA: IEEE Computer Society Press, 1992.
• Optimal control: A foundation for intelligent control. D. A. White and M. I. Jordan. In D. A. White, and D. A. Sofge (Eds.), Handbook of Intelligent Control, Amsterdam: Van Nostrand, 1992.
• Constraints on underspecified target trajectories. M. I. Jordan. In P. Dario, G. Sandini, and P. Aebischer, (Eds.), Robots and Biological Systems: Toward a New Bionics, Heidelberg: Springer-Verlag, 1992.
• A more biologically plausible learning network model for neural networks. P. Mazzoni, R. Andersen, and M. I. Jordan. Proceedings of the National Academy of Sciences, 88, 4433--4437, 1991.
• Task decomposition through competition in a modular connectionist architecture: The what and where vision tasks. R. A. Jacobs, M. I. Jordan, and A. G. Barto. Cognitive Science, 15, 219--250, 1991.
• Internal world models and supervised learning. M. I. Jordan, and D. E. Rumelhart. In L. Birnbaum and G. Collins, (Eds.), Machine Learning: Proceedings of the Eighth International Workshop, San Mateo, CA: Morgan Kaufmann, pp. 70--75, 1991.
• A competitive modular connectionist architecture. R. A. Jacobs and M. I. Jordan. In D. Touretzky (Ed.), Advances in Neural Information Processing Systems (NIPS) 3, San Mateo, CA: Morgan Kaufmann, 1991.
• Speech motor control model using electromyography. M. Hirayama, E. Vatikiotis-Bateson, M. Kawato, and M. I. Jordan. INCN Conference on Speech Communications, 39--46, 1991.
• A modular connectionist architecture for learning piecewise control strategies. R. A. Jacobs and M. I. Jordan. Proceedings of the 1991 American Control Conference, Boston, MA, pp. 343--351, 1991.
• A more biologically plausible learning rule than backpropagation applied to a network model of cortical area 7a. P. Mazzoni, R. Andersen, and M. I. Jordan. Cerebral Cortex, 1, 293--307, 1991.
• Modularity, supervised learning, and unsupervised learning. M. I. Jordan, and R. A. Jacobs. In S. Davis (Ed.), Connectionism: Theory and practice, Oxford: Oxford University Press, 1991.
• A non-empiricist perspective on learning in layered networks. M. I. Jordan. Behavioral and Brain Sciences, 13, 497--498, 1990.
• Simulation of vocalic gestures using an articulatory model driven by a sequential neural network. G. Bailly, M. I. Jordan, M. Mantakas, J-L. Schwartz, M. Bach, and O. Olesen. Journal of the Acoustical Society of America, 87:S105, 1990.
• Learning to control an unstable system with forward modeling. M. I. Jordan, and R. A. Jacobs. In D. Touretzky (Ed.), Advances in Neural Information Processing Systems (NIPS) 2, San Mateo, CA: Morgan Kaufmann, pp. 324--331, 1990.
• AR-P learning applied to a network model of cortical area 7a. P. Mazzoni, R. Andersen, and M. I. Jordan. Proceedings of the International Joint Conference On Neural Networks, San Diego, CA, pp. 373--379, 1990.
• Motor learning and the degrees of freedom problem. M. I. Jordan. Attention and Performance, XIII, 796--836, 1990.
• Learning inverse mappings with forward models. M. I. Jordan. In K. S. Narendra (Ed.), Proceedings of the Sixth Yale Workshop on Adaptive and Learning Systems, New York: Plenum Press, 1990.
• Action. M. I. Jordan, and D. A. Rosenbaum. In M. I. Posner (Ed.), Foundations of Cognitive Science, Cambridge, MA: MIT Press, 1989.
• Gradient following without backpropagation in layered networks. A. G. Barto and M. I. Jordan. Proceedings of the IEEE First Annual International Conference on Neural Networks, New York: IEEE Publishing Services, 1987.
• An introduction to linear algebra in parallel, distributed processing. M. I. Jordan. In D. E. Rumelhart and J. L. McClelland, (Eds.), Parallel Distributed Processing: Explorations in the Microstructure of Cognition, Cambridge, MA: MIT Press, 1986.
• Attractor dynamics and parallelism in a connectionist sequential machine. M. I. Jordan. Proceedings of the Eighth Annual Conference of the Cognitive Science Society, Englewood Cliffs, NJ: Erlbaum, pp. 531--546. [Reprinted in IEEE Tutorials Series, New York: IEEE Publishing Services, 1990], 1986.

Books

• Domeniconi, C., & Jordan, M. I. (2001). Discorsi sulle Reti Neurali e l’Apprendimento. Milan: Franco Angeli Editore.
• Jordan, M. I., LeCun, Y. & Solla, S. A. (Eds.). (2001). Advances in Neural Information Processing Systems, Proceedings of the First Twelve Conferences on CD-ROM, Cambridge MA: MIT Press.
• Jordan, M. I., & Sejnowski, T. J. (Eds.). (2001). Graphical Models: Foundations of Neural Computation. Cambridge MA: MIT Press.
• Jordan, M. I. (Ed.). (1999). Learning in Graphical Models, Cambridge, MA: MIT Press.
• Jordan, M. I., Kearns, M. J. & Solla, S. A. (Eds.). (1998). Advances in Neural Information Processing Systems 10, Cambridge MA: MIT Press.
• Mozer, M. C., Jordan, M. I., & Petsche, T. (Eds.). (1997). Advances in Neural Information Processing Systems 9, Cambridge MA: MIT Press.