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Title:
Author:
Tommi Jaakkola: [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. Chen-Hsiang Yeang, Tommi Jaakkola
    Time Series Analysis of Gene Expression and Location Data. [Citation Graph (0, 0)][DBLP]
    BIBE, 2003, pp:305-312 [Conf]
  3. Karen Sachs, Omar D. Perez, Dana Pe'er, Garry P. Nolan, David K. Gifford, Tommi Jaakkola, Douglas A. Lauffenburger
    Analysis of Signaling Pathways in Human T-Cells Using Bayesian Network Modeling of Single Cell Data. [Citation Graph (0, 0)][DBLP]
    CSB, 2004, pp:644- [Conf]
  4. Harald Steck, Tommi Jaakkola
    Predictive Discretization During Model Selection. [Citation Graph (0, 0)][DBLP]
    DAGM-Symposium, 2004, pp:1-8 [Conf]
  5. 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]
  6. Nathan Srebro, Tommi Jaakkola
    Weighted Low-Rank Approximations. [Citation Graph (0, 0)][DBLP]
    ICML, 2003, pp:720-727 [Conf]
  7. Ziv Bar-Joseph, David K. Gifford, Tommi Jaakkola
    Fast optimal leaf ordering for hierarchical clustering. [Citation Graph (0, 0)][DBLP]
    ISMB (Supplement of Bioinformatics), 2001, pp:22-29 [Conf]
  8. Tommi Jaakkola, Mark Diekhans, David Haussler
    Using the Fisher Kernel Method to Detect Remote Protein Homologies. [Citation Graph (0, 0)][DBLP]
    ISMB, 1999, pp:149-158 [Conf]
  9. Yuan Qi, Patrycja E. Missiuro, Ashish Kapoor, Craig P. Hunter, Tommi Jaakkola, David K. Gifford, Hui Ge
    Semi-supervised analysis of gene expression profiles for lineage-specific development in the Caenorhabditis elegans embryo. [Citation Graph (0, 0)][DBLP]
    ISMB (Supplement of Bioinformatics), 2006, pp:417-423 [Conf]
  10. 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]
  11. Adrian Corduneanu, Tommi Jaakkola
    Distributed Information Regularization on Graphs. [Citation Graph (0, 0)][DBLP]
    NIPS, 2004, pp:- [Conf]
  12. Brendan J. Frey, Relu Patrascu, Tommi Jaakkola, Jodi Moran
    Sequentially Fitting ``Inclusive'' Trees for Inference in Noisy-OR Networks. [Citation Graph (0, 0)][DBLP]
    NIPS, 2000, pp:493-499 [Conf]
  13. Tommi Jaakkola, David Haussler
    Exploiting Generative Models in Discriminative Classifiers. [Citation Graph (0, 0)][DBLP]
    NIPS, 1998, pp:487-493 [Conf]
  14. Tommi Jaakkola, Michael I. Jordan
    Recursive Algorithms for Approximating Probabilities in Graphical Models. [Citation Graph (0, 0)][DBLP]
    NIPS, 1996, pp:487-493 [Conf]
  15. 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]
  16. Tommi Jaakkola, Marina Meila, Tony Jebara
    Maximum Entropy Discrimination. [Citation Graph (0, 0)][DBLP]
    NIPS, 1999, pp:470-476 [Conf]
  17. Tommi Jaakkola, Hava T. Siegelmann
    Active Information Retrieval. [Citation Graph (0, 0)][DBLP]
    NIPS, 2001, pp:777-784 [Conf]
  18. 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]
  19. 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]
  20. Claire Monteleoni, Tommi Jaakkola
    Online Learning of Non-stationary Sequences. [Citation Graph (0, 0)][DBLP]
    NIPS, 2003, pp:- [Conf]
  21. 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]
  22. Nathan Srebro, Noga Alon, Tommi Jaakkola
    Generalization Error Bounds for Collaborative Prediction with Low-Rank Matrices. [Citation Graph (0, 0)][DBLP]
    NIPS, 2004, pp:- [Conf]
  23. Nathan Srebro, Tommi Jaakkola
    Linear Dependent Dimensionality Reduction. [Citation Graph (0, 0)][DBLP]
    NIPS, 2003, pp:- [Conf]
  24. Nathan Srebro, Jason D. M. Rennie, Tommi Jaakkola
    Maximum-Margin Matrix Factorization. [Citation Graph (0, 0)][DBLP]
    NIPS, 2004, pp:- [Conf]
  25. Harald Steck, Tommi Jaakkola
    On the Dirichlet Prior and Bayesian Regularization. [Citation Graph (0, 0)][DBLP]
    NIPS, 2002, pp:697-704 [Conf]
  26. Harald Steck, Tommi Jaakkola
    Bias-Corrected Bootstrap and Model Uncertainty. [Citation Graph (0, 0)][DBLP]
    NIPS, 2003, pp:- [Conf]
  27. Martin Szummer, Tommi Jaakkola
    Kernel Expansions with Unlabeled Examples. [Citation Graph (0, 0)][DBLP]
    NIPS, 2000, pp:626-632 [Conf]
  28. Martin Szummer, Tommi Jaakkola
    Partially labeled classification with Markov random walks. [Citation Graph (0, 0)][DBLP]
    NIPS, 2001, pp:945-952 [Conf]
  29. Martin Szummer, Tommi Jaakkola
    Information Regularization with Partially Labeled Data. [Citation Graph (0, 0)][DBLP]
    NIPS, 2002, pp:1025-1032 [Conf]
  30. Martin J. Wainwright, Tommi Jaakkola, Alan S. Willsky
    Tree-based reparameterization for approximate inference on loopy graphs. [Citation Graph (0, 0)][DBLP]
    NIPS, 2001, pp:1001-1008 [Conf]
  31. Martin J. Wainwright, Tommi Jaakkola, Alan S. Willsky
    Exact MAP Estimates by (Hyper)tree Agreement. [Citation Graph (0, 0)][DBLP]
    NIPS, 2002, pp:809-816 [Conf]
  32. Alexander J. Hartemink, David K. Gifford, Tommi Jaakkola, Richard A. Young
    Using Graphical Models and Genomic Expression Data to Statistically Validate Models of Genetic Regulatory Networks. [Citation Graph (0, 0)][DBLP]
    Pacific Symposium on Biocomputing, 2001, pp:422-433 [Conf]
  33. Alexander J. Hartemink, David K. Gifford, Tommi Jaakkola, Richard A. Young
    Combining Location and Expression Data for Principled Discovery of Genetic Regulatory Network Models. [Citation Graph (0, 0)][DBLP]
    Pacific Symposium on Biocomputing, 2002, pp:437-449 [Conf]
  34. Ziv Bar-Joseph, Georg Gerber, David K. Gifford, Tommi Jaakkola, Itamar Simon
    A new approach to analyzing gene expression time series data. [Citation Graph (0, 0)][DBLP]
    RECOMB, 2002, pp:39-48 [Conf]
  35. Chen-Hsiang Yeang, Tommi Jaakkola
    Physical network models and multi-source data integration. [Citation Graph (0, 0)][DBLP]
    RECOMB, 2003, pp:312-321 [Conf]
  36. Chen-Hsiang Yeang, Tommi Jaakkola
    Modeling the Combinatorial Functions of Multiple Transcription Factors. [Citation Graph (0, 0)][DBLP]
    RECOMB, 2005, pp:506-521 [Conf]
  37. Jason D. M. Rennie, Tommi Jaakkola
    Using term informativeness for named entity detection. [Citation Graph (0, 0)][DBLP]
    SIGIR, 2005, pp:353-360 [Conf]
  38. Adrian Corduneanu, Tommi Jaakkola
    Continuation Methods for Mixing Heterogenous Sources. [Citation Graph (0, 0)][DBLP]
    UAI, 2002, pp:111-118 [Conf]
  39. Adrian Corduneanu, Tommi Jaakkola
    On Information Regularization. [Citation Graph (0, 0)][DBLP]
    UAI, 2003, pp:151-158 [Conf]
  40. 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]
  41. Tony Jebara, Tommi Jaakkola
    Feature Selection and Dualities in Maximum Entropy Discrimination. [Citation Graph (0, 0)][DBLP]
    UAI, 2000, pp:291-300 [Conf]
  42. Marina Meila, Tommi Jaakkola
    Tractable Bayesian Learning of Tree Belief Networks. [Citation Graph (0, 0)][DBLP]
    UAI, 2000, pp:380-388 [Conf]
  43. Harald Steck, Tommi Jaakkola
    Unsupervised Active Learning in Large Domains. [Citation Graph (0, 0)][DBLP]
    UAI, 2002, pp:469-476 [Conf]
  44. Martin J. Wainwright, Tommi Jaakkola, Alan S. Willsky
    A New Class of upper Bounds on the Log Partition Function. [Citation Graph (0, 0)][DBLP]
    UAI, 2002, pp:536-543 [Conf]
  45. Ziv Bar-Joseph, Erik D. Demaine, David K. Gifford, Angèle M. Hamel, Tommi Jaakkola, Nathan Srebro
    K-ary Clustering with Optimal Leaf Ordering for Gene Expression Data. [Citation Graph (0, 0)][DBLP]
    WABI, 2002, pp:506-520 [Conf]
  46. Ziv Bar-Joseph, Erik D. Demaine, David K. Gifford, Nathan Srebro, Angèle M. Hamel, Tommi Jaakkola
    K-ary Clustering with Optimal Leaf Ordering for Gene Expression Data. [Citation Graph (0, 0)][DBLP]
    Bioinformatics, 2003, v:19, n:9, pp:1070-1078 [Journal]
  47. 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]
  48. Alexander J. Hartemink, David K. Gifford, Tommi Jaakkola, Richard A. Young
    Bayesian Methods for Elucidating Genetic Regulatory Networks. [Citation Graph (0, 0)][DBLP]
    IEEE Intelligent Systems, 2002, v:17, n:2, pp:37-43 [Journal]
  49. Chen-Hsiang Yeang, Tommi Jaakkola
    Time Series Analysis of Gene Expression and Location Data. [Citation Graph (0, 0)][DBLP]
    International Journal on Artificial Intelligence Tools, 2005, v:14, n:5, pp:755-770 [Journal]
  50. 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]
  51. Ziv Bar-Joseph, Georg Gerber, David K. Gifford, Tommi Jaakkola, Itamar Simon
    Continuous Representations of Time-Series Gene Expression Data. [Citation Graph (0, 0)][DBLP]
    Journal of Computational Biology, 2003, v:10, n:3/4, pp:341-356 [Journal]
  52. Tommi Jaakkola, Mark Diekhans, David Haussler
    A Discriminative Framework for Detecting Remote Protein Homologies. [Citation Graph (0, 0)][DBLP]
    Journal of Computational Biology, 2000, v:7, n:1-2, pp:95-114 [Journal]
  53. Chen-Hsiang Yeang, Trey Ideker, Tommi Jaakkola
    Physical Network Models. [Citation Graph (0, 0)][DBLP]
    Journal of Computational Biology, 2004, v:11, n:2/3, pp:243-262 [Journal]
  54. 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]
  55. Satinder P. Singh, Tommi Jaakkola, Michael L. Littman, Csaba Szepesvári
    Convergence Results for Single-Step On-Policy Reinforcement-Learning Algorithms. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2000, v:38, n:3, pp:287-308 [Journal]
  56. Martin J. Wainwright, Tommi Jaakkola, Alan S. Willsky
    Tree-based reparameterization framework for analysis of sum-product and related algorithms. [Citation Graph (0, 0)][DBLP]
    IEEE Transactions on Information Theory, 2003, v:49, n:5, pp:1120-1146 [Journal]
  57. Martin J. Wainwright, Tommi Jaakkola, Alan S. Willsky
    A new class of upper bounds on the log partition function. [Citation Graph (0, 0)][DBLP]
    IEEE Transactions on Information Theory, 2005, v:51, n:7, pp:2313-2335 [Journal]
  58. Martin J. Wainwright, Tommi Jaakkola, Alan S. Willsky
    MAP estimation via agreement on trees: message-passing and linear programming. [Citation Graph (0, 0)][DBLP]
    IEEE Transactions on Information Theory, 2005, v:51, n:11, pp:3697-3717 [Journal]
  59. Marina Meila, Tommi Jaakkola
    Tractable Bayesian learning of tree belief networks. [Citation Graph (0, 0)][DBLP]
    Statistics and Computing, 2006, v:16, n:1, pp:77-92 [Journal]
  60. Yuan (Alan) Qi, Tommi Jaakkola
    Parameter Expanded Variational Bayesian Methods. [Citation Graph (0, 0)][DBLP]
    NIPS, 2006, pp:1097-1104 [Conf]
  61. Luis Pérez-Breva, Luis E. Ortiz, Chen-Hsiang Yeang, Tommi Jaakkola
    Game Theoretic Algorithms for Protein-DNA binding. [Citation Graph (0, 0)][DBLP]
    NIPS, 2006, pp:1081-1088 [Conf]
  62. Amir Globerson, Tommi Jaakkola
    Approximate inference using planar graph decomposition. [Citation Graph (0, 0)][DBLP]
    NIPS, 2006, pp:473-480 [Conf]

  63. Learning Efficiently with Approximate Inference via Dual Losses. [Citation Graph (, )][DBLP]


  64. New Outer Bounds on the Marginal Polytope. [Citation Graph (, )][DBLP]


  65. Fixing Max-Product: Convergent Message Passing Algorithms for MAP LP-Relaxations. [Citation Graph (, )][DBLP]


  66. Clusters and Coarse Partitions in LP Relaxations. [Citation Graph (, )][DBLP]


  67. Tightening LP Relaxations for MAP using Message Passing. [Citation Graph (, )][DBLP]


  68. MAP estimation via agreement on (hyper)trees: Message-passing and linear programming [Citation Graph (, )][DBLP]


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