Papers

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List of papers, machine learning, computational biology, information retrieval, or reinforcement learning

List of papers

• D. Sontag, A. Globerson, and T. Jaakkola.
  Introduction to dual decomposition for inference.
  In S. Sra, S. Nowozin, and S. Wright, Eds., Optimization for Machine Learning. MIT Press, 2010. To appear.
  [pdf]

• Y. Guo, G. Papachristoudis, R. Altshuler, G. Gerber, T. Jaakkola, D. Gifford, and S. Mahony.
  Discovering homotypic binding events at high spatial resolution.
  Bioinformatics, 2010. To appear.
  [link to paper]

• D. Sontag, O. Meshi, T. Jaakkola, and A. Globerson.
  More data means less inference: A pseudo-max approach to structured learning.
  In Advances in Neural Information Processing Systems 24, 2010. To appear.
  [pdf]

• A. Rush, D. Sontag, M. Collins, and T. Jaakkola.
  On dual decomposition and linear programming relaxations for natural language processing.
  In Conference on Empirical Methods in Natural Language Processing (EMNLP), 2010.
  [pdf]

• T. Koo, A. Rush, M. Collins, T. Jaakkola, and D. Sontag.
  Dual decomposition for parsing with non-projective head automata.
  In Conference on Empirical Methods in Natural Language Processing (EMNLP), 2010. Best paper award.
  [pdf]

• E. Minkov, B. Charrow, J. Ledlie, S. Teller, and T. Jaakkola.
  Collaborative future event recommendation.
  In International Conference on Information and Knowledge Management, 2010. To appear.
  [pdf]

• O. Meshi, D. Sontag, T. Jaakkola, and A. Globerson.
  Learning efficiently with approximate inference via dual losses.
  In Proceedings of the 27th International Conference on Machine Learning, 2010.
  [pdf]

• T. Jaakkola, D. Sontag, A. Globerson, and M. Meila.
  Learning bayesian network structure using lp relaxations.
  In Proceedings of the 13th International Conference on Artificial Intelligence and Statistics, 2010.
  [pdf], [related pdf slides]

• D. Sontag and T. Jaakkola.
  Tree block coordinate descent for map in graphical models.
  In Proceedings of the 12th International Conference on Artificial Intelligence and Statistics, 2009.
  [pdf]

• D. Sontag, A. Globerson, and T. Jaakkola.
  Clusters and coarse partitions in lp relaxations.
  In Advances in Neural Information Processing Systems 21, 2008.
  [pdf]

• D. Sontag, T. Meltzer, A. Globerson, T. Jaakkola, and Y. Weiss.
  Tightening lp relaxations for map using message passing.
  In Proceedings of the 24rd Conference on Uncertainty in Artificial Intelligence, 2008.
  [pdf]

• D. Sontag and T. Jaakkola.
  New outer bounds on the marginal polytope.
  In Advances in Neural Information Processing Systems 20, 2007.
  [pdf]

• A. Globerson and T. Jaakkola.
  Fixing max-product: Convergent message passing algorithms for map lp-relaxations.
  In Advances in Neural Information Processing Systems 20, 2007.
  [pdf]

• G. Gerber, R. Dowell, T. Jaakkola, and D. Gifford.
  Automated discovery of functional generality of human gene expression programs.
  PloS Computational biology, 2007.
  [link to paper]

• A. Globerson and T. Jaakkola.
  Convergent propagation algorithms via oriented trees.
  In Proceedings of the 23rd Conference on Uncertainty in Artificial Intelligence, 2007.
  [pdf]

• D. Sontag and T. Jaakkola.
  On iteratively constraining the marginal polytope for approximate inference and map.
  Technical report, 2007.
  [pdf]

• A. Globerson and T. Jaakkola.
  Approximate inference using conditional entropy decompositions.
  In Proceedings of the 11th International Conference on Artificial Intelligence and Statistics, 2007.
  [pdf]

• H. Steck and T. Jaakkola.
  Predictive discretization during model selection.
  In Proceedings of the 11th International Conference on Artificial Intelligence and Statistics, 2007.
  [pdf]

• A. Globerson and T. Jaakkola.
  Approximate inference using planar graph decomposition.
  In Advances in Neural Information Processing Systems 19, 2006.
  [pdf]

• L. Perez-Breva, L. Ortiz, C-H. Yeang, and T. Jaakkola.
  Game theoretic algorithms for protein-dna binding.
  In Advances in Neural Information Processing Systems 19, 2006.
  [pdf]

• A. Qi and T. Jaakkola.
  Parameter expanded variational bayesian methods.
  In Advances in Neural Information Processing Systems 19, 2006.
  [pdf]

• Y Qi, A. Rolfe, K. MacIsaac, G. Gerber, D. Pokholok, J. Zeitlinger, T. Danford, R. Dowell, E. Fraenkel, T. Jaakkola, R. Young, and D. Gifford.
  High-resolution computational models of genome binding events.
  Nature Biotechnology, 24:963--970, 2006.
  [pdf], [pdf]

• Y. Qi, P. Missiuro, A. Kapoor, C. Hunter, T. Jaakkola, D. Gifford, and H. Ge.
  Semi-supervised analysis of gene expression profiles for lineage-specific development in the caenorhabditis elegans embryo.
  Bioinformatics, 22(14):417--423, 2006.
  [pdf]

• L. Perez-Breva, L. Ortiz, C-H. Yeang, and T. Jaakkola.
  Dna binding and games.
  MIT CSAIL Technical Report TR-2006-018, 2006.
  [pdf]

• A. Corduneanu and T. Jaakkola.
  Data dependent regularization.
  In Semi-supervised learning. MIT Press, 2006.
  [pdf]

• M. Wainwright, T. Jaakkola, and A. Willsky.
  Map estimation via agreement on (hyper)trees: Message-passing and linear-programming approaches.
  IEEE Transactions on Information Theory, 51(11):3697--3717, 2005.
  [pdf]

• J. Rennie and T. Jaakkola.
  Using term informativeness for named entity detection.
  In Proceedings of the 28th Annual Conference on Research and Development in Information Retrieval (SIGIR), 2005.
  [pdf]

• C-H. Yeang and T. Jaakkola.
  Modeling the combinatorial functions of multiple transcription factors.
  In The Ninth Annual International Conference on Research in Computational Molecular Biology, 2005.
  [pdf]

• C-H. Yeang, H. Mak, S. McCuine, C. Workman, T. Jaakkola, and T. Ideker.
  Validation and refinement of gene-regulatory pathways on a network of physical interactions.
  Genome Biology, 6(7):R62, 2005.
  [pdf], [link to paper]

• M. Wainwright, T. Jaakkola, and A. Willsky.
  A new class of upper bounds on the log partition function.
  IEEE Transactions on Information Theory, 51:2313--2335, 2005.
  [pdf]

• R. Rosales and T. Jaakkola.
  Focused inference.
  In Proceedings of the Tenth International Workshop on Artificial Intelligence and Statistics, 2005.
  [pdf]

• N. Srebro, N. Alon, and T. Jaakkola.
  Generalization error bounds for collaborative prediction with low-rank matrices.
  In Advances in Neural Information Processing Systems 17, 2004.
  [pdf]

• N. Srebro, J. Rennie, and T. Jaakkola.
  Maximum margin matrix factorization.
  In Advances in Neural Information Processing Systems 17, 2004.
  [pdf]

• A. Corduneanu and T. Jaakkola.
  Distributed information regularization on graphs.
  In Advances in Neural Information Processing Systems 17, 2004.
  [pdf]

• M. Wainwright, T. Jaakkola, and A. Willsky.
  Tree consistency and bounds on the performance of the max-product algorithm and its generalizations.
  Statistics and Computing, 14(2):143--166, 2004.
  [pdf]

• C-H. Yeang, T. Ideker, and T. Jaakkola.
  Physical network models.
  Journal of Computational Biology, 11(2-3):243--263, 2004.
  [pdf]

• N. Srebro and T. Jaakkola.
  Linear dependent dimensionality reduction.
  In Advances in Neural Information Processing Systems 16, 2003.
  [pdf]

• C. Monteleoni and T. Jaakkola.
  Online learning of non-stationary sequences.
  In Advances in Neural Information Processing Systems 16, 2003.
  [gzipped postscript]

• H. Steck and T. Jaakkola.
  Bias-corrected bootstrap and model uncertainty.
  In Advances in Neural Information Processing Systems 16, 2003.
  [pdf]

• A. Corduneanu and T. Jaakkola.
  On information regularization.
  In Proceedings of the Nineteenth Annual Conference on Uncertainty in Artificial Intelligence, 2003.
  [gzipped postscript]

• Z. Bar-Joseph, G. Gerber, Simon, Gifford I., D., and T. Jaakkola.
  Comparing continuous representations of time series expression profiles to identify differentially expressed genes.
  Proceedings of the National Academy of Sciences, 100(18):10146--10151, 2003.
  [pdf], [link to paper]

• Z. Bar-Joseph, G. Gerber, T. Lee, N. Rinaldi, J. Yoo, F. Robert, B. Gordon, E. Fraenkel, T. Jaakkola, R. Young, and D. Gifford.
  Computational discovery of gene modules and regulatory networks.
  Nature Biotechnology, 21(11):1337--1342, 2003.
  [pdf], [link to paper]

• H. Steck and T. Jaakkola.
  Semi-predictive discretization during model selection.
  AI Memo AIM-2003-002, 2003.
  [pdf]

• Z. Bar-Joseph, G. Gerber, D. Gifford, T. Jaakkola, and I. Simon.
  Continuous representations of time series gene expression data.
  Journal of Computational Biology, 10(3-4):241--256, 2003.
  [pdf]

• N. Srebro and T. Jaakkola.
  Weighted low-rank approximations.
  In Proceedings of the Twentieth International Conference on Machine Learning, 2003.
  [pdf]

• C-H. Yeang and T. Jaakkola.
  Time series analysis of gene expression and location data.
  In Proceedings of the Third IEEE Symposium on Bioinformatics and Bioengineering, pages 305--312, 2003.
  [pdf]

• C-H. Yeang and T. Jaakkola.
  Physical network models and multi-source data integration.
  In The Seventh Annual International Conference on Research in Computational Molecular Biology, 2003.
  [pdf]

• N. Srebro and T. Jaakkola.
  Generalized low-rank approximations.
  AI Memo AIM-2003-001, 2003.
  [pdf]

• H. Steck and T. Jaakkola.
  On the dirichlet prior and bayesian regularization.
  In Advances in Neural Information processing systems 15, 2002.
  [gzipped postscript]

• M. Wainwright, T. Jaakkola, and A. Willsky.
  Tree-based parameterization framework for analysis of belief propagation and related algorithms.
  IEEE Transactions on information theory, 2002.
  

• M. J. Wainwright, T. Jaakkola, and A. S. Willsky.
  Exact map estimates by (hyper)tree agreement.
  In Advances in Neural Information processing systems 15, 2002.
  [gzipped postscript]

• M. Szummer and T. Jaakkola.
  Information regularization with partially labeled data.
  In Advances in Neural Information processing systems 15, 2002.
  [pdf]

• Ziv Bar-Joseph, Erik D. Demaine, David K. Gifford, Angele M. Hamel, Tommi S. Jaakkola, and Nathan Srebro.
  K-ary clustering with optimal leaf ordering for gene expression data.
  Bioinformatics (to appear), 2002.
  [pdf]

• A. Corduneanu and T. Jaakkola.
  Continuation methods for mixing heterogeneous sources.
  In Proceedings of the Eighteenth Annual Conference on Uncertainty in Artificial Intelligence, 2002.
  [gzipped postscript]

• H. Steck and T. Jaakkola.
  Unsupervised active learning in large domains.
  In Proceedings of the Eighteenth Annual Conference on Uncertainty in Artificial Intelligence, 2002.
  [gzipped postscript]

• M. J. Wainwright, T. Jaakkola, and A. S. Willsky.
  A new class of upper bounds on the log partition function.
  In Proceedings of the Eighteenth Annual Conference on Uncertainty in Artificial Intelligence, 2002.
  [gzipped postscript]

• Z. Bar-Joseph, G. Gerber, D. Gifford, and T. Jaakkola.
  A new approach to analyzing gene expression time series data.
  In The Sixth Annual International Conference on Research in Computational Molecular Biology, 2002.
  [pdf]

• A. Corduneanu and T. Jaakkola.
  Stable mixing of complete and incomplete information.
  MIT AI Memo AIM-2001-030, 2001.
  [pdf]

• M. Wainwright, T. Jaakkola, and A. Willsky.
  Tree-based reparameterization for approximate estimation on loopy graphs.
  In Advances in Neural Information processing systems 14, 2001.
  [pdf]

• M. J. Wainwright, T. Jaakkola, and A. S. Willsky.
  Tree-based reparameterization framework for approximate estimation in graphs with cycles.
  LIDS Technical Report P-2510, 2001.
  [gzipped postscript]

• M. Szummer and T. Jaakkola.
  Partially labeled classification with markov random walks.
  In Advances in Neural Information processing systems 14, 2001.
  [postscript]

• T. Jaakkola and H. Siegelmann.
  Active information retrieval.
  In Advances in Neural Information processing systems 14, pages 777--784, 2001.
  [gzipped postscript]

• A. Hartemink, D. Gifford, T. Jaakkola, and R. Young.
  Combining location and expression data for principled discovery of genetic regulatory network models.
  In Pacific Symposium on Biocomputing, 2002.
  [pdf]

• Z. Bar-Joseph, D. Gifford, and T. Jaakkola.
  Fast optimal leaf ordering for hierarchical clustering.
  In Proceedings of the Ninth International Conference on Intelligent Systems for Molecular Biology, 2001.
  [pdf]

• A. Hartemink, D. Gifford, T. Jaakkola, and R. Young.
  Maximum likelihood estimation of optimal scaling factors for expression array normalization.
  In Microarrays: Optical Technologies and Informatics, Proceedings of SPIE, volume 4266, 2001.
  [postscript], [gzipped postscript]

• A. Hartemink, D. Gifford, T. Jaakkola, and R. Young.
  Using graphical models and genomic expression data to statistically validate models of genetic regulatory networks.
  In Pacific Symposium on Biocomputing, volume 6, pages 422--433, 2001.
  [pdf]

• T. Jaakkola.
  Tutorial on variational approximation methods.
  In Advanced mean field methods: theory and practice. MIT Press, 2000.
  [postscript], [gzipped postscript]

• T. Jaakkola and M. Jordan.
  Bayesian parameter estimation via variational methods.
  Statistics and Computing, 10:25--37, 2000.
  [postscript], [gzipped postscript]

• T. Jaakkola, M. Diekhans, and D. Haussler.
  A discriminative framework for detecting remote protein homologies.
  Journal of Computational Biology, 7(1,2):95--114, 2000.
  [postscript], [gzipped postscript]

• B. Frey, R. Patrascu, T. Jaakkola, and J. Moran.
  Sequentially fitting inclusive trees for inference in noisy-or networks.
  In Advances in Neural Information Processing Systems 13. MIT Press, 2000.
  [postscript], [gzipped postscript]

• M. Szummer and T. Jaakkola.
  Kernel expansions with unlabeled examples.
  In Advances in Neural Information Processing Systems 13. MIT Press, 2000.
  [postscript], [gzipped postscript]

• M. Meila and T. Jaakkola.
  Tractable bayesian learning of tree belief networks.
  In Proceedings of the Sixteenth Annual Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann, 2000.
  [postscript], [gzipped postscript]

• T. Jebara and T. Jaakkola.
  Feature selection and dualities in maximum entropy discrimination.
  In Proceedings of the Sixteenth Annual Conference on Uncertainty in Artificial Intelligence. Morgan Kaufmann, 2000.
  [postscript], [gzipped postscript]

• S. Singh, T. Jaakkola, M. Littman, and C. Szepesvari.
  Convergence results for single-step on-policy reinforcement-learning algorithms.
  Machine Learning, 38(3):287, 2000.
  [postscript], [gzipped postscript]

• T. Jaakkola, M. Meila, and T. Jebara.
  Maximum entropy discrimination.
  In Advances in Neural Information Processing Systems 12. MIT Press, 1999.
  [postscript], [gzipped postscript]

• T. Jaakkola, M. Meila, and T. Jebara.
  Maximum entropy discrimination.
  Technical Report AITR-1668, MIT, 1999.
  [postscript], [gzipped postscript]

• T. Jaakkola, M. Diekhans, and D. Haussler.
  Using the fisher kernel method to detect remote protein homologies.
  In Proceedings of the Seventh International Conference on Intelligent Systems for Molecular Biology, 1999.
  [postscript], [gzipped postscript]

• T. Jaakkola and M. Jordan.
  Variational probabilistic inference and the qmr-dt database.
  Journal of Artificial Intelligence Research, 10:291--322, 1999.
  [postscript], [gzipped postscript], [pdf]

• M. Jordan, Z. Ghahramani, T. Jaakkola, and L. Saul.
  An introduction to variational methods for graphical models.
  Machine Learning, 37(2):183, 1999.
  [postscript], [gzipped postscript]

• T. Jaakkola and D. Haussler.
  Probabilistic kernel regression models.
  In Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, 1999.
  [postscript], [gzipped postscript]

• T. Jaakkola and D. Haussler.
  Exploiting generative models in discriminative classifiers.
  In Advances in Neural Information Processing Systems 11, 1998.
  [postscript], [gzipped postscript]

• C. Bishop, N. Lawrence, T. Jaakkola, and M. Jordan.
  Approximating posterior distributions in belief networks using mixtures.
  In Advances in Neural Information Processing Systems 10, 1997.
  [postscript], [gzipped postscript]

• T. Jaakkola and M. Jordan.
  A variational approach to bayesian logistic regression models and their extensions.
  In Proceedings of the Sixth International Workshop on Artificial Intelligence and Statistics, 1997.
  [postscript], [gzipped postscript]

• T. Jaakkola.
  Variational methods for inference and estimation in graphical models.
  PhD thesis, MIT, 1997.
  [postscript], [gzipped postscript]

• T. Jaakkola and M. Jordan.
  Improving the mean field approximation via the use of mixture distributions.
  In Proceedings of the NATO ASI on Learning in Graphical Models. Kluwer, 1997.
  [postscript], [gzipped postscript]

• L. Saul, T. Jaakkola, and M. Jordan.
  Mean field theory for sigmoid belief networks.
  Journal of Artificial Intelligence Research, 4:61--76, 1996.
  [postscript], [gzipped postscript], [pdf]

• T. Jaakkola and M. Jordan.
  Recursive algorithms for approximating probabilities in graphical models.
  In Advances in Neural Information Processing Systems 9, 1996.
  [postscript], [gzipped postscript]

• T. Jaakkola and M. Jordan.
  Computing upper and lower bounds on likelihoods in intractable networks.
  In Proceedings of the Twelfth Annual Conference on Uncertainty in Artificial Intelligence, pages 340--348, 1996.
  [postscript], [gzipped postscript]

• T. Jaakkola, L. Saul, and M. Jordan.
  Fast learning by bounding likelihoods in sigmoid type belief networks.
  In Advances in Neural Information Processing Systems 8, 1995.
  [postscript], [gzipped postscript]

• S. Singh, T. Jaakkola, and M. Jordan.
  Reinforcement learning with soft state aggregation.
  In Advances in Neural Information Processing Systems 7, 1994.
  [postscript], [gzipped postscript]

• T. Jaakkola, S. Singh, and M. Jordan.
  Reinforcement learning algorithm for partially observable markov decision problems.
  In Advances in Neural Information Processing Systems 7, 1994.
  [postscript], [gzipped postscript]

• S. Singh, T. Jaakkola, and M. Jordan.
  Learning without state estimation in partially observable environments.
  In Proceedings of the Eleventh Machine Learning Conference, 1994.
  [postscript], [gzipped postscript]

• T. Jaakkola, M. Jordan, and S. Singh.
  On the convergence of stochastic iterative dynamic programming algorithms.
  Neural Computation, 6(6):1185--1201, 1994.
  [postscript], [gzipped postscript]

• T. Jaakkola, M. Jordan, and S. Singh.
  Convergence of stochastic iterative dynamic programming algorithms.
  In Advances in Neural Information Processing Systems 6, 1993.
  [postscript], [gzipped postscript]

• A. Friberg, T. Jaakkola, and J. Tuovinen.
  Electromagnetic gaussian beam beyond the paraxial regime.
  IEEE Transactions of Antennas and Propagation, 1992.
  

• A. Vasara, M. Taghizadeh, J. Turunen, Westerholmand E. Noponen J., H. Ichikawa, J. Miller, T. Jaakkola, and S. Kuisma.
  Binary surface-relief gratings for array illumination in digital optics.
  Applied Optics, 31(17):3320--3336, 1992.