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Michael I. Jordan: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Lawrence K. Saul, Tommi Jaakkola, Michael I. Jordan
    Mean Field Theory for Sigmoid Belief Networks. [Citation Graph (1, 0)][DBLP]
    J. Artif. Intell. Res. (JAIR), 1996, v:4, n:, pp:61-76 [Journal]
  2. Michael I. Jordan
    A Statistical Approach to Decision Tree Modeling. [Citation Graph (0, 0)][DBLP]
    COLT, 1994, pp:13-20 [Conf]
  3. Eric P. Xing, Wei Wu, Michael I. Jordan, Richard M. Karp
    LOGOS: a modular Bayesian model for de novo motif detection. [Citation Graph (0, 0)][DBLP]
    CSB, 2003, pp:266-276 [Conf]
  4. Fernando De Bernardinis, M. I. Jordan, Alberto L. Sangiovanni-Vincentelli
    Support vector machines for analog circuit performance representation. [Citation Graph (0, 0)][DBLP]
    DAC, 2003, pp:964-969 [Conf]
  5. Neil D. Lawrence, John C. Platt, Michael I. Jordan
    Extensions of the Informative Vector Machine. [Citation Graph (0, 0)][DBLP]
    Deterministic and Statistical Methods in Machine Learning, 2004, pp:56-87 [Conf]
  6. Peter Bodíc, Greg Friedman, Lukas Biewald, Helen Levine, George Candea, Kayur Patel, Gilman Tolle, Jonathan Hui, Armando Fox, Michael I. Jordan, David A. Patterson
    Combining Visualization and Statistical Analysis to Improve Operator Confidence and Efficiency for Failure Detection and Localization. [Citation Graph (0, 0)][DBLP]
    ICAC, 2005, pp:89-100 [Conf]
  7. Mike Y. Chen, Alice X. Zheng, Jim Lloyd, Michael I. Jordan, Eric A. Brewer
    Failure Diagnosis Using Decision Trees. [Citation Graph (0, 0)][DBLP]
    ICAC, 2004, pp:36-43 [Conf]
  8. Francis R. Bach, Gert R. G. Lanckriet, Michael I. Jordan
    Multiple kernel learning, conic duality, and the SMO algorithm. [Citation Graph (0, 0)][DBLP]
    ICML, 2004, pp:- [Conf]
  9. David M. Blei, Michael I. Jordan
    Variational methods for the Dirichlet process. [Citation Graph (0, 0)][DBLP]
    ICML, 2004, pp:- [Conf]
  10. Francis R. Bach, Michael I. Jordan
    Predictive low-rank decomposition for kernel methods. [Citation Graph (0, 0)][DBLP]
    ICML, 2005, pp:33-40 [Conf]
  11. Barbara E. Engelhardt, Michael I. Jordan, Steven E. Brenner
    A graphical model for predicting protein molecular function. [Citation Graph (0, 0)][DBLP]
    ICML, 2006, pp:297-304 [Conf]
  12. Michael I. Jordan
    A Statistical Approach to Decision Tree Modeling. [Citation Graph (0, 0)][DBLP]
    ICML, 1994, pp:363-370 [Conf]
  13. Michael I. Jordan, Robert A. Jacobs
    Supervised Learning and Divide-and-Conquer: A Statistical Approach. [Citation Graph (0, 0)][DBLP]
    ICML, 1993, pp:159-166 [Conf]
  14. Michael I. Jordan, David E. Rumelhart
    Internal World Models and Supervised Learning. [Citation Graph (0, 0)][DBLP]
    ML, 1991, pp:70-74 [Conf]
  15. Gert R. G. Lanckriet, Nello Cristianini, Peter L. Bartlett, Laurent El Ghaoui, Michael I. Jordan
    Learning the Kernel Matrix with Semi-Definite Programming. [Citation Graph (0, 0)][DBLP]
    ICML, 2002, pp:323-330 [Conf]
  16. Andrew Y. Ng, Michael I. Jordan
    Convergence rates of the Voting Gibbs classifier, with application to Bayesian feature selection. [Citation Graph (0, 0)][DBLP]
    ICML, 2001, pp:377-384 [Conf]
  17. XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan
    Decentralized detection and classification using kernel methods. [Citation Graph (0, 0)][DBLP]
    ICML, 2004, pp:- [Conf]
  18. Satinder P. Singh, Tommi Jaakkola, Michael I. Jordan
    Learning Without State-Estimation in Partially Observable Markovian Decision Processes. [Citation Graph (0, 0)][DBLP]
    ICML, 1994, pp:284-292 [Conf]
  19. Eric P. Xing, Michael I. Jordan, Richard M. Karp
    Feature selection for high-dimensional genomic microarray data. [Citation Graph (0, 0)][DBLP]
    ICML, 2001, pp:601-608 [Conf]
  20. Eric P. Xing, Roded Sharan, Michael I. Jordan
    Bayesian haplo-type inference via the dirichlet process. [Citation Graph (0, 0)][DBLP]
    ICML, 2004, pp:- [Conf]
  21. Eric P. Xing, Kyung-Ah Sohn, Michael I. Jordan, Yee Whye Teh
    Bayesian multi-population haplotype inference via a hierarchical dirichlet process mixture. [Citation Graph (0, 0)][DBLP]
    ICML, 2006, pp:1049-1056 [Conf]
  22. Alice X. Zheng, Michael I. Jordan, Ben Liblit, Mayur Naik, Alex Aiken
    Statistical debugging: simultaneous identification of multiple bugs. [Citation Graph (0, 0)][DBLP]
    ICML, 2006, pp:1105-1112 [Conf]
  23. Andrew Y. Ng, Alice X. Zheng, Michael I. Jordan
    Link Analysis, Eigenvectors and Stability. [Citation Graph (0, 0)][DBLP]
    IJCAI, 2001, pp:903-910 [Conf]
  24. Simon Lacoste-Julien, Benjamin Taskar, Dan Klein, Michael I. Jordan
    Word Alignment via Quadratic Assignment. [Citation Graph (0, 0)][DBLP]
    HLT-NAACL, 2006, pp:- [Conf]
  25. Francis R. Bach, Michael I. Jordan
    Thin Junction Trees. [Citation Graph (0, 0)][DBLP]
    NIPS, 2001, pp:569-576 [Conf]
  26. Francis R. Bach, Michael I. Jordan
    Learning Graphical Models with Mercer Kernels. [Citation Graph (0, 0)][DBLP]
    NIPS, 2002, pp:1009-1016 [Conf]
  27. Francis R. Bach, Michael I. Jordan
    Learning Spectral Clustering. [Citation Graph (0, 0)][DBLP]
    NIPS, 2003, pp:- [Conf]
  28. Francis R. Bach, Michael I. Jordan
    Blind One-microphone Speech Separation: A Spectral Learning Approach. [Citation Graph (0, 0)][DBLP]
    NIPS, 2004, pp:- [Conf]
  29. Francis R. Bach, Romain Thibaux, Michael I. Jordan
    Computing regularization paths for learning multiple kernels. [Citation Graph (0, 0)][DBLP]
    NIPS, 2004, pp:- [Conf]
  30. Peter L. Bartlett, Michael I. Jordan, Jon D. McAuliffe
    Large Margin Classifiers: Convex Loss, Low Noise, and Convergence Rates. [Citation Graph (0, 0)][DBLP]
    NIPS, 2003, pp:- [Conf]
  31. Daphne Bavelier, Michael I. Jordan
    A Dynamical Model of Priming and Repetition Blindness. [Citation Graph (0, 0)][DBLP]
    NIPS, 1992, pp:879-886 [Conf]
  32. Christopher M. Bishop, Neil D. Lawrence, Tommi Jaakkola, Michael I. Jordan
    Approximating Posterior Distributions in Belief Networks Using Mixtures. [Citation Graph (0, 0)][DBLP]
    NIPS, 1997, pp:- [Conf]
  33. David M. Blei, Thomas L. Griffiths, Michael I. Jordan, Joshua B. Tenenbaum
    Hierarchical Topic Models and the Nested Chinese Restaurant Process. [Citation Graph (0, 0)][DBLP]
    NIPS, 2003, pp:- [Conf]
  34. David M. Blei, Andrew Y. Ng, Michael I. Jordan
    Latent Dirichlet Allocation. [Citation Graph (0, 0)][DBLP]
    NIPS, 2001, pp:601-608 [Conf]
  35. David A. Cohn, Zoubin Ghahramani, Michael I. Jordan
    Active Learning with Statistical Models. [Citation Graph (0, 0)][DBLP]
    NIPS, 1994, pp:705-712 [Conf]
  36. Patrick Flaherty, Michael I. Jordan, Adam P. Arkin
    Robust design of biological experiments. [Citation Graph (0, 0)][DBLP]
    NIPS, 2005, pp:- [Conf]
  37. Kenji Fukumizu, Francis R. Bach, Michael I. Jordan
    Kernel Dimensionality Reduction for Supervised Learning. [Citation Graph (0, 0)][DBLP]
    NIPS, 2003, pp:- [Conf]
  38. Zoubin Ghahramani, Michael I. Jordan
    Supervised learning from incomplete data via an EM approach. [Citation Graph (0, 0)][DBLP]
    NIPS, 1993, pp:120-127 [Conf]
  39. Zoubin Ghahramani, Michael I. Jordan
    Factorial Hidden Markov Models. [Citation Graph (0, 0)][DBLP]
    NIPS, 1995, pp:472-478 [Conf]
  40. Zoubin Ghahramani, Daniel M. Wolpert, Michael I. Jordan
    Computational Structure of coordinate transformations: A generalization study. [Citation Graph (0, 0)][DBLP]
    NIPS, 1994, pp:1125-1132 [Conf]
  41. Makoto Hirayama, Eric Vatikiotis-Bateson, Mitsuo Kawato, Michael I. Jordan
    Forward Dynamics Modeling of Speech Motor Control Using Physiological Data. [Citation Graph (0, 0)][DBLP]
    NIPS, 1991, pp:191-198 [Conf]
  42. Thomas Hofmann, Jan Puzicha, Michael I. Jordan
    Learning from Dyadic Data. [Citation Graph (0, 0)][DBLP]
    NIPS, 1998, pp:466-472 [Conf]
  43. John F. Houde, Michael I. Jordan
    Adaptation in Speech Motor Control. [Citation Graph (0, 0)][DBLP]
    NIPS, 1997, pp:- [Conf]
  44. Tommi Jaakkola, Michael I. Jordan
    Recursive Algorithms for Approximating Probabilities in Graphical Models. [Citation Graph (0, 0)][DBLP]
    NIPS, 1996, pp:487-493 [Conf]
  45. Tommi Jaakkola, Michael I. Jordan, Satinder P. Singh
    Convergence of Stochastic Iterative Dynamic Programming Algorithms. [Citation Graph (0, 0)][DBLP]
    NIPS, 1993, pp:703-710 [Conf]
  46. Tommi Jaakkola, Satinder P. Singh, Michael I. Jordan
    Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems. [Citation Graph (0, 0)][DBLP]
    NIPS, 1994, pp:345-352 [Conf]
  47. Tommi Jaakkola, Lawrence K. Saul, Michael I. Jordan
    Fast Learning by Bounding Likelihoods in Sigmoid Type Belief Networks. [Citation Graph (0, 0)][DBLP]
    NIPS, 1995, pp:528-534 [Conf]
  48. Robert A. Jacobs, Michael I. Jordan
    A Competitive Modular Connectionist Architecture. [Citation Graph (0, 0)][DBLP]
    NIPS, 1990, pp:767-773 [Conf]
  49. Michael I. Jordan, Zoubin Ghahramani, Lawrence K. Saul
    Hidden Markov Decision Trees. [Citation Graph (0, 0)][DBLP]
    NIPS, 1996, pp:501-507 [Conf]
  50. Michael I. Jordan, Robert A. Jacobs
    Learning to Control an Unstable System with Forward Modeling. [Citation Graph (0, 0)][DBLP]
    NIPS, 1989, pp:324-331 [Conf]
  51. Michael I. Jordan, Robert A. Jacobs
    Hierarchies of Adaptive Experts. [Citation Graph (0, 0)][DBLP]
    NIPS, 1991, pp:985-992 [Conf]
  52. Gert R. G. Lanckriet, Laurent El Ghaoui, Chiranjib Bhattacharyya, Michael I. Jordan
    Minimax Probability Machine. [Citation Graph (0, 0)][DBLP]
    NIPS, 2001, pp:801-807 [Conf]
  53. Gert R. G. Lanckriet, Laurent El Ghaoui, Michael I. Jordan
    Robust Novelty Detection with Single-Class MPM. [Citation Graph (0, 0)][DBLP]
    NIPS, 2002, pp:905-912 [Conf]
  54. Neil D. Lawrence, Michael I. Jordan
    Semi-supervised Learning via Gaussian Processes. [Citation Graph (0, 0)][DBLP]
    NIPS, 2004, pp:- [Conf]
  55. Andrew Y. Ng, Michael I. Jordan
    On Discriminative vs. Generative Classifiers: A comparison of logistic regression and naive Bayes. [Citation Graph (0, 0)][DBLP]
    NIPS, 2001, pp:841-848 [Conf]
  56. Marina Meila, Michael I. Jordan
    Learning Fine Motion by Markov Mixtures of Experts. [Citation Graph (0, 0)][DBLP]
    NIPS, 1995, pp:1003-1009 [Conf]
  57. Marina Meila, Michael I. Jordan
    Triangulation by Continuous Embedding. [Citation Graph (0, 0)][DBLP]
    NIPS, 1996, pp:557-563 [Conf]
  58. Marina Meila, Michael I. Jordan
    Estimating Dependency Structure as a Hidden Variable. [Citation Graph (0, 0)][DBLP]
    NIPS, 1997, pp:- [Conf]
  59. Andrew Y. Ng, Michael I. Jordan, Yair Weiss
    On Spectral Clustering: Analysis and an algorithm. [Citation Graph (0, 0)][DBLP]
    NIPS, 2001, pp:849-856 [Conf]
  60. Andrew Y. Ng, Michael I. Jordan
    Approximate Inference A lgorithms for Two-Layer Bayesian Networks. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:533-539 [Conf]
  61. Andrew Y. Ng, H. Jin Kim, Michael I. Jordan, Shankar Sastry
    Autonomous Helicopter Flight via Reinforcement Learning. [Citation Graph (0, 0)][DBLP]
    NIPS, 2003, pp:- [Conf]
  62. XuanLong Nguyen, Michael I. Jordan
    On the Concentration of Expectation and Approximate Inference in Layered Networks. [Citation Graph (0, 0)][DBLP]
    NIPS, 2003, pp:- [Conf]
  63. XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan
    Divergences, surrogate loss functions and experimental design. [Citation Graph (0, 0)][DBLP]
    NIPS, 2005, pp:- [Conf]
  64. Philip N. Sabes, Michael I. Jordan
    Reinforcement Learning by Probability Matching. [Citation Graph (0, 0)][DBLP]
    NIPS, 1995, pp:1080-1086 [Conf]
  65. Lawrence K. Saul, Michael I. Jordan
    Boltzmann Chains and Hidden Markov Models. [Citation Graph (0, 0)][DBLP]
    NIPS, 1994, pp:435-442 [Conf]
  66. Lawrence K. Saul, Michael I. Jordan
    Exploiting Tractable Substructures in Intractable Networks. [Citation Graph (0, 0)][DBLP]
    NIPS, 1995, pp:486-492 [Conf]
  67. Lawrence K. Saul, Michael I. Jordan
    A Variational Principle for Model-based Morphing. [Citation Graph (0, 0)][DBLP]
    NIPS, 1996, pp:267-273 [Conf]
  68. Satinder P. Singh, Tommi Jaakkola, Michael I. Jordan
    Reinforcement Learning with Soft State Aggregation. [Citation Graph (0, 0)][DBLP]
    NIPS, 1994, pp:361-368 [Conf]
  69. Benjamin Taskar, Simon Lacoste-Julien, Michael I. Jordan
    Structured Prediction via the Extragradient Method. [Citation Graph (0, 0)][DBLP]
    NIPS, 2005, pp:- [Conf]
  70. Yee Whye Teh, Michael I. Jordan, Matthew J. Beal, David M. Blei
    Sharing Clusters among Related Groups: Hierarchical Dirichlet Processes. [Citation Graph (0, 0)][DBLP]
    NIPS, 2004, pp:- [Conf]
  71. Emanuel Todorov, Michael I. Jordan
    A Minimal Intervention Principle for Coordinated Movement. [Citation Graph (0, 0)][DBLP]
    NIPS, 2002, pp:27-34 [Conf]
  72. Martin J. Wainwright, Michael I. Jordan
    Semidefinite Relaxations for Approximate Inference on Graphs with Cycles. [Citation Graph (0, 0)][DBLP]
    NIPS, 2003, pp:- [Conf]
  73. Daniel M. Wolpert, Zoubin Ghahramani, Michael I. Jordan
    Forward dynamic models in human motor control: Psychophysical evidence. [Citation Graph (0, 0)][DBLP]
    NIPS, 1994, pp:43-50 [Conf]
  74. Eric P. Xing, Michael I. Jordan, Richard M. Karp, Stuart J. Russell
    A Hierarchical Bayesian Markovian Model for Motifs in Biopolymer Sequences. [Citation Graph (0, 0)][DBLP]
    NIPS, 2002, pp:1489-1496 [Conf]
  75. Eric P. Xing, Andrew Y. Ng, Michael I. Jordan, Stuart J. Russell
    Distance Metric Learning with Application to Clustering with Side-Information. [Citation Graph (0, 0)][DBLP]
    NIPS, 2002, pp:505-512 [Conf]
  76. Lei Xu, Michael I. Jordan, Geoffrey E. Hinton
    An Alternative Model for Mixtures of Experts. [Citation Graph (0, 0)][DBLP]
    NIPS, 1994, pp:633-640 [Conf]
  77. Alice X. Zheng, Michael I. Jordan, Ben Liblit, Alexander Aiken
    Statistical Debugging of Sampled Programs. [Citation Graph (0, 0)][DBLP]
    NIPS, 2003, pp:- [Conf]
  78. Alexandre d'Aspremont, Laurent El Ghaoui, Michael I. Jordan, Gert R. G. Lanckriet
    A Direct Formulation for Sparse PCA Using Semidefinite Programming. [Citation Graph (0, 0)][DBLP]
    NIPS, 2004, pp:- [Conf]
  79. Ben Liblit, Alexander Aiken, Alice X. Zheng, Michael I. Jordan
    Bug isolation via remote program sampling. [Citation Graph (0, 0)][DBLP]
    PLDI, 2003, pp:141-154 [Conf]
  80. Ben Liblit, Mayur Naik, Alice X. Zheng, Alexander Aiken, Michael I. Jordan
    Scalable statistical bug isolation. [Citation Graph (0, 0)][DBLP]
    PLDI, 2005, pp:15-26 [Conf]
  81. Gert R. G. Lanckriet, Minghua Deng, Nello Cristianini, Michael I. Jordan, William Stafford Noble
    Kernel-Based Data Fusion and Its Application to Protein Function Prediction in Yeast. [Citation Graph (0, 0)][DBLP]
    Pacific Symposium on Biocomputing, 2004, pp:300-311 [Conf]
  82. Robert A. Jacobs, Michael I. Jordan, Andrew G. Barto
    Task Decompostiion Through Competition in a Modular Connectionist Architecture: The What and Where Vision Tasks. [Citation Graph (0, 0)][DBLP]
    Machine Learning: From Theory to Applications, 1993, pp:175-202 [Conf]
  83. David M. Blei, Michael I. Jordan
    Modeling annotated data. [Citation Graph (0, 0)][DBLP]
    SIGIR, 2003, pp:127-134 [Conf]
  84. Alice X. Zheng, Andrew Y. Ng, Michael I. Jordan
    Stable Algorithms for Link Analysis. [Citation Graph (0, 0)][DBLP]
    SIGIR, 2001, pp:258-266 [Conf]
  85. Francis R. Bach, Michael I. Jordan
    Tree-dependent Component Analysis. [Citation Graph (0, 0)][DBLP]
    UAI, 2002, pp:36-44 [Conf]
  86. Amol Deshpande, Minos N. Garofalakis, Michael I. Jordan
    Efficient Stepwise Selection in Decomposable Models. [Citation Graph (0, 0)][DBLP]
    UAI, 2001, pp:128-135 [Conf]
  87. Tommi Jaakkola, Michael I. Jordan
    Computing upper and lower bounds on likelihoods in intractable networks. [Citation Graph (0, 0)][DBLP]
    UAI, 1996, pp:340-348 [Conf]
  88. Neil D. Lawrence, Christopher M. Bishop, Michael I. Jordan
    Mixture Representations for Inference and Learning in Boltzmann Machines. [Citation Graph (0, 0)][DBLP]
    UAI, 1998, pp:320-327 [Conf]
  89. Kevin P. Murphy, Yair Weiss, Michael I. Jordan
    Loopy Belief Propagation for Approximate Inference: An Empirical Study. [Citation Graph (0, 0)][DBLP]
    UAI, 1999, pp:467-475 [Conf]
  90. Andrew Y. Ng, Michael I. Jordan
    PEGASUS: A policy search method for large MDPs and POMDPs. [Citation Graph (0, 0)][DBLP]
    UAI, 2000, pp:406-415 [Conf]
  91. Sekhar Tatikonda, Michael I. Jordan
    Loopy Belief Propogation and Gibbs Measures. [Citation Graph (0, 0)][DBLP]
    UAI, 2002, pp:493-500 [Conf]
  92. Eric P. Xing, Michael I. Jordan, Stuart J. Russell
    A generalized mean field algorithm for variational inference in exponential families. [Citation Graph (0, 0)][DBLP]
    UAI, 2003, pp:583-591 [Conf]
  93. L. R. Grate, Chiranjib Bhattacharyya, Michael I. Jordan, I. Saira Mian
    Simultaneous Relevant Feature Identification and Classification in High-Dimensional Spaces. [Citation Graph (0, 0)][DBLP]
    WABI, 2002, pp:1-9 [Conf]
  94. Patrick Flaherty, Guri Giaever, Jochen Kumm, Michael I. Jordan, Adam P. Arkin
    A latent variable model for chemogenomic profiling. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2005, v:21, n:15, pp:3286-3293 [Journal]
  95. Gert R. G. Lanckriet, Tijl De Bie, Nello Cristianini, Michael I. Jordan, William Stafford Noble
    A statistical framework for genomic data fusion. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2004, v:20, n:16, pp:2626-2635 [Journal]
  96. Jon D. McAuliffe, Lior Pachter, Michael I. Jordan
    Multiple-sequence functional annotation and the generalized hidden Markov phylogeny. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2004, v:20, n:12, pp:1850-1860 [Journal]
  97. David M. Blei, K. Franks, Michael I. Jordan, I. Saira Mian
    Statistical modeling of biomedical corpora: mining the Caenorhabditis Genetic Center Bibliography for genes related to life span. [Citation Graph (0, 0)][DBLP]
    BMC Bioinformatics, 2006, v:7, n:, pp:250- [Journal]
  98. Robert A. Jacobs, Michael I. Jordan, Andrew G. Barto
    Task Decomposition Through Competition in a Modular Connectionist Architecture: The What and Where Vision Tasks. [Citation Graph (0, 0)][DBLP]
    Cognitive Science, 1991, v:15, n:2, pp:219-250 [Journal]
  99. Michael I. Jordan, David E. Rumelhart
    Forward Models: Supervised Learning with a Distal Teacher. [Citation Graph (0, 0)][DBLP]
    Cognitive Science, 1992, v:16, n:3, pp:307-354 [Journal]
  100. Lawrence K. Saul, Tommi Jaakkola, Michael I. Jordan
    Mean Field Theory for Sigmoid Belief Networks [Citation Graph (0, 0)][DBLP]
    CoRR, 1996, v:0, n:, pp:- [Journal]
  101. David A. Cohn, Zoubin Ghahramani, Michael I. Jordan
    Active Learning with Statistical Models [Citation Graph (0, 0)][DBLP]
    CoRR, 1996, v:0, n:, pp:- [Journal]
  102. Alexandre d'Aspremont, Laurent El Ghaoui, Michael I. Jordan, Gert R. G. Lanckriet
    A direct formulation for sparse PCA using semidefinite programming [Citation Graph (0, 0)][DBLP]
    CoRR, 2004, v:0, n:, pp:- [Journal]
  103. Michael I. Jordan, Christopher M. Bishop
    Neural Networks. [Citation Graph (0, 0)][DBLP]
    ACM Comput. Surv., 1996, v:28, n:1, pp:73-75 [Journal]
  104. David A. Cohn, Zoubin Ghahramani, Michael I. Jordan
    Active Learning with Statistical Models. [Citation Graph (0, 0)][DBLP]
    J. Artif. Intell. Res. (JAIR), 1996, v:4, n:, pp:129-145 [Journal]
  105. Tommi Jaakkola, Michael I. Jordan
    Variational Probabilistic Inference and the QMR-DT Network. [Citation Graph (0, 0)][DBLP]
    J. Artif. Intell. Res. (JAIR), 1999, v:10, n:, pp:291-322 [Journal]
  106. Eric P. Xing, Wei Wu, Michael I. Jordan, Richard M. Karp
    Logos: a Modular Bayesian Model for de Novo Motif Detection. [Citation Graph (0, 0)][DBLP]
    J. Bioinformatics and Computational Biology, 2004, v:2, n:1, pp:127-154 [Journal]
  107. Chiranjib Bhattacharyya, L. R. Grate, Michael I. Jordan, Laurent El Ghaoui, I. Saira Mian
    Robust Sparse Hyperplane Classifiers: Application to Uncertain Molecular Profiling Data. [Citation Graph (0, 0)][DBLP]
    Journal of Computational Biology, 2004, v:11, n:6, pp:1073-1089 [Journal]
  108. Francis R. Bach, Michael I. Jordan
    Kernel Independent Component Analysis. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2002, v:3, n:, pp:1-48 [Journal]
  109. Francis R. Bach, Michael I. Jordan
    Beyond Independent Components: Trees and Clusters. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2003, v:4, n:, pp:1205-1233 [Journal]
  110. Kobus Barnard, Pinar Duygulu, David A. Forsyth, Nando de Freitas, David M. Blei, Michael I. Jordan
    Matching Words and Pictures. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2003, v:3, n:, pp:1107-1135 [Journal]
  111. David M. Blei, Andrew Y. Ng, Michael I. Jordan
    Latent Dirichlet Allocation. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2003, v:3, n:, pp:993-1022 [Journal]
  112. Gert R. G. Lanckriet, Nello Cristianini, Peter L. Bartlett, Laurent El Ghaoui, Michael I. Jordan
    Learning the Kernel Matrix with Semidefinite Programming. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2004, v:5, n:, pp:27-72 [Journal]
  113. Gert R. G. Lanckriet, Laurent El Ghaoui, Chiranjib Bhattacharyya, Michael I. Jordan
    A Robust Minimax Approach to Classification. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2002, v:3, n:, pp:555-582 [Journal]
  114. Kenji Fukumizu, Francis R. Bach, Michael I. Jordan
    Dimensionality Reduction for Supervised Learning with Reproducing Kernel Hilbert Spaces. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2004, v:5, n:, pp:73-99 [Journal]
  115. Marina Meila, Michael I. Jordan
    Learning with Mixtures of Trees. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2000, v:1, n:, pp:1-48 [Journal]
  116. Benjamin Taskar, Simon Lacoste-Julien, Michael I. Jordan
    Structured Prediction, Dual Extragradient and Bregman Projections. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2006, v:7, n:, pp:1627-1653 [Journal]
  117. Francis R. Bach, Michael I. Jordan
    Learning Spectral Clustering, With Application To Speech Separation. [Citation Graph (0, 0)][DBLP]
    Journal of Machine Learning Research, 2006, v:7, n:, pp:1963-2001 [Journal]
  118. Christophe Andrieu, Nando de Freitas, Arnaud Doucet, Michael I. Jordan
    An Introduction to MCMC for Machine Learning. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2003, v:50, n:1-2, pp:5-43 [Journal]
  119. Zoubin Ghahramani, Michael I. Jordan
    Factorial Hidden Markov Models. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1997, v:29, n:2-3, pp:245-273 [Journal]
  120. Michael I. Jordan, Zoubin Ghahramani, Tommi Jaakkola, Lawrence K. Saul
    An Introduction to Variational Methods for Graphical Models. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1999, v:37, n:2, pp:183-233 [Journal]
  121. Lawrence K. Saul, Michael I. Jordan
    Mixed Memory Markov Models: Decomposing Complex Stochastic Processes as Mixtures of Simpler Ones. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1999, v:37, n:1, pp:75-87 [Journal]
  122. Jinwen Ma, Lei Xu, Michael I. Jordan
    Asymptotic Convergence Rate of the EM Algorithm for Gaussian Mixtures. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 2001, v:12, n:12, pp:2881-2907 [Journal]
  123. Lawrence K. Saul, Michael I. Jordan
    Attractor Dynamics in Feedforward Neural Networks. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 2000, v:12, n:6, pp:1313-1335 [Journal]
  124. Padhraic Smyth, David Heckerman, Michael I. Jordan
    Probabilistic Independence Networks for Hidden Markov Probability Models. [Citation Graph (0, 0)][DBLP]
    Neural Computation, 1997, v:9, n:2, pp:227-269 [Journal]
  125. Michael I. Jordan, Lei Xu
    Convergence results for the EM approach to mixtures of experts architectures. [Citation Graph (0, 0)][DBLP]
    Neural Networks, 1995, v:8, n:9, pp:1409-1431 [Journal]
  126. Michael I. Jordan, Terrence J. Sejnowski
    Graphical Models: Foundations of Neural Computation. [Citation Graph (0, 0)][DBLP]
    Pattern Anal. Appl., 2002, v:5, n:4, pp:401-402 [Journal]
  127. XuanLong Nguyen, Michael I. Jordan, Bruno Sinopoli
    A kernel-based learning approach to ad hoc sensor network localization. [Citation Graph (0, 0)][DBLP]
    TOSN, 2005, v:1, n:1, pp:134-152 [Journal]
  128. Chiranjib Bhattacharyya, L. R. Grate, A. Rizki, D. Radisky, F. J. Molina, Michael I. Jordan, Mina J. Bissell, I. Saira Mian
    Simultaneous classification and relevant feature identification in high-dimensional spaces: application to molecular profiling data. [Citation Graph (0, 0)][DBLP]
    Signal Processing, 2003, v:83, n:4, pp:729-743 [Journal]
  129. Jon D. McAuliffe, David M. Blei, Michael I. Jordan
    Nonparametric empirical Bayes for the Dirichlet process mixture model. [Citation Graph (0, 0)][DBLP]
    Statistics and Computing, 2006, v:16, n:1, pp:5-14 [Journal]
  130. Percy Liang, Michael I. Jordan, Benjamin Taskar
    A permutation-augmented sampler for DP mixture models. [Citation Graph (0, 0)][DBLP]
    ICML, 2007, pp:545-552 [Conf]
  131. Jens Nilsson, Fei Sha, Michael I. Jordan
    Regression on manifolds using kernel dimension reduction. [Citation Graph (0, 0)][DBLP]
    ICML, 2007, pp:697-704 [Conf]
  132. Ling Huang, XuanLong Nguyen, Minos N. Garofalakis, Joseph M. Hellerstein, Michael I. Jordan, Anthony D. Joseph, Nina Taft
    Communication-Efficient Online Detection of Network-Wide Anomalies. [Citation Graph (0, 0)][DBLP]
    INFOCOM, 2007, pp:134-142 [Conf]
  133. Ling Huang, XuanLong Nguyen, Minos N. Garofalakis, Michael I. Jordan, Anthony Joseph, Nina Taft
    In-Network PCA and Anomaly Detection. [Citation Graph (0, 0)][DBLP]
    NIPS, 2006, pp:617-624 [Conf]
  134. Michal Rosen-Zvi, Michael I. Jordan, Alan L. Yuille
    The DLR Hierarchy of Approximate Inference. [Citation Graph (0, 0)][DBLP]
    UAI, 2005, pp:493-500 [Conf]
  135. Eric P. Xing, Michael I. Jordan
    Graph Partition Strategies for Generalized Mean Field Inference. [Citation Graph (0, 0)][DBLP]
    UAI, 2004, pp:602-610 [Conf]
  136. Zhihua Zhang, Michael I. Jordan
    Bayesian Multicategory Support Vector Machines. [Citation Graph (0, 0)][DBLP]
    UAI, 2006, pp:- [Conf]
  137. XuanLong Nguyen, Martin J. Wainwright, Michael I. Jordan
    On optimal quantization rules for some sequential decision problems [Citation Graph (0, 0)][DBLP]
    CoRR, 2006, v:0, n:, pp:- [Journal]

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  142. Online System Problem Detection by Mining Patterns of Console Logs. [Citation Graph (, )][DBLP]


  143. Image Denoising with Nonparametric Hidden Markov Trees. [Citation Graph (, )][DBLP]


  144. An asymptotic analysis of generative, discriminative, and pseudolikelihood estimators. [Citation Graph (, )][DBLP]


  145. An HDP-HMM for systems with state persistence. [Citation Graph (, )][DBLP]


  146. Learning from measurements in exponential families. [Citation Graph (, )][DBLP]


  147. Mixed Membership Matrix Factorization. [Citation Graph (, )][DBLP]


  148. An Analysis of the Convergence of Graph Laplacians. [Citation Graph (, )][DBLP]


  149. Detecting Large-Scale System Problems by Mining Console Logs. [Citation Graph (, )][DBLP]


  150. On the Consistency of Ranking Algorithms. [Citation Graph (, )][DBLP]


  151. Learning Programs: A Hierarchical Bayesian Approach. [Citation Graph (, )][DBLP]


  152. Multiple Non-Redundant Spectral Clustering Views. [Citation Graph (, )][DBLP]


  153. Fast approximate spectral clustering. [Citation Graph (, )][DBLP]


  154. Agreement-Based Learning. [Citation Graph (, )][DBLP]


  155. Feature Selection Methods for Improving Protein Structure Prediction with Rosetta. [Citation Graph (, )][DBLP]


  156. Estimating divergence functionals and the likelihood ratio by penalized convex risk minimization. [Citation Graph (, )][DBLP]


  157. Spectral Clustering with Perturbed Data. [Citation Graph (, )][DBLP]


  158. High-dimensional support union recovery in multivariate regression. [Citation Graph (, )][DBLP]


  159. Shared Segmentation of Natural Scenes Using Dependent Pitman-Yor Processes. [Citation Graph (, )][DBLP]


  160. DiscLDA: Discriminative Learning for Dimensionality Reduction and Classification. [Citation Graph (, )][DBLP]


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  165. Mining Console Logs for Large-Scale System Problem Detection. [Citation Graph (, )][DBLP]


  166. A Flexible and Efficient Algorithm for Regularized Fisher Discriminant Analysis. [Citation Graph (, )][DBLP]


  167. On the Inference of Ancestries in Admixed Populations. [Citation Graph (, )][DBLP]


  168. Combinatorial stochastic processes and nonparametric Bayesian modeling. [Citation Graph (, )][DBLP]


  169. Detecting large-scale system problems by mining console logs. [Citation Graph (, )][DBLP]


  170. The Phylogenetic Indian Buffet Process: A Non-Exchangeable Nonparametric Prior for Latent Features. [Citation Graph (, )][DBLP]


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  172. The Infinite PCFG Using Hierarchical Dirichlet Processes. [Citation Graph (, )][DBLP]


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  174. Joint estimation of gene conversion rates and mean conversion tract lengths from population SNP data. [Citation Graph (, )][DBLP]


  175. Active site prediction using evolutionary and structural information. [Citation Graph (, )][DBLP]


  176. On divergences, surrogate loss functions, and decentralized detection [Citation Graph (, )][DBLP]


  177. Estimating divergence functionals and the likelihood ratio by convex risk minimization [Citation Graph (, )][DBLP]


  178. Bayesian Inference in Queueing Networks [Citation Graph (, )][DBLP]


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