Tommi S. Jaakkola, Ph.D.
Thomas Siebel Professor of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society

MIT Computer Science and Artificial Intelligence Laboratory
Stata Center, Bldg 32-G470
Cambridge, MA 02139

tommi at csail dot mit dot edu

[home]   [papers]   [research]   [people]   [courses]


You can view all the papers in reverse chronological order, sets of papers related to broad categories such as machine learning, natural language processing, computational biology, or physics, or papers in more specific areas including inference, semi-supervised learning , information retrieval, or reinforcement learning.

Machine learning papers

  • T. Lei, W. Jin, R. Barzilay, and T. Jaakkola.
    Deriving neural architectures from sequence and graph kernels.
    In International Conference on Machine Learning (ICML), 2017.
  • J. Mueller and T. Jaakkola.
    Sequence to better sequence: Continuous revision of combinatorial structures.
    In International Conference on Machine Learning (ICML), 2017.
  • M. Zhao, S. Yue, D. Katabi, and T. Jaakkola.
    Learning sleep stages from radio signals: A deep adversarial architecture.
    In International Conference on Machine Learning (ICML), 2017.
  • C. W. Coley, R. Barzilay, T. Jaakkola, W. H. Green, and K. F. Jensen.
    Prediction of organic reaction outcomes using machine learning.
    ACS Central Science, 2017.
    [pdf]
  • D. Alvarez-Melis and T. Jaakkola.
    Tree structured decoding with doubly recurrent neural networks.
    In International Conference on Learning Representations (ICLR), 2017.
    [pdf]
  • J. Mueller, D. Reshef, G. Du, and T. Jaakkola.
    Learning optimal interventions.
    In Artificial Intelligence and Statistics (AISTATS), 2017.
    [pdf]
  • V. Garg and T. Jaakkola.
    Learning tree structured potential games.
    In Advances in Neural Information Processing Systems (NIPS), 2016.
    [pdf]
  • J. Honorio and T. Jaakkola.
    Structured prediction: From gaussian perturbations to linear-time principled algorithms.
    In Uncertainty in Artificial Intelligence (UAI), 2016.
    [pdf]
  • T. Hashimoto, D. Alvarez-Melis, and T. Jaakkola.
    Word embeddings as metric recovery in semantic spaces.
    Transactions of the Association for Computational Linguistics (TACL), 4, 2016.
    [pdf]
  • T. Hashimoto, T. Jaakkola, and D. Gifford.
    Learning population-level diffusions with generative {RNN}s.
    In International Conference on Machine Learning (ICML), 2016.
    [pdf]
  • V. Garg, C. Rudin, and T. Jaakkola.
    Craft: Cluster-specific assorted feature selection.
    In Artificial Intelligence and Statistics (AISTATS), 2016.
    [pdf]
  • T. Hashimoto, D. Alvarez-Melis, and T. Jaakkola.
    Word, graph and manifold embedding from markov processes.
    In arXiv:1509.05808, 2015.
    [link]
  • T. Hashimoto, Y. Sun, and T. Jaakkola.
    From random walks to distances on unweighted graphs.
    In Advances in Neural Information Processing Systems (NIPS), 2015.
    [pdf]
  • J. Mueller and T. Jaakkola.
    Principal differences analysis: Interpretable characterization of differences between distributions.
    In Advances in Neural Information Processing Systems (NIPS), 2015.
    [pdf]
  • T. Hashimoto, Y. Sun, and T. Jaakkola.
    Metric recovery from directed unweighted graphs.
    In Artificial Intelligence and Statistics, 2015.
    [pdf]
  • Y. Xin and T. Jaakkola.
    Controlling privacy in recommender systems.
    In Advances in Neural Information Processing Systems, 2014.
    [pdf]
  • T. Lei, Y. Xin, Y. Zhang, R. Barzilay, and T. Jaakkola.
    Low-rank tensors for scoring dependency structures.
    In Association for Computational Linguistics, 2014.
    [pdf]
  • Y. Zhang, T. Lei, R. Barzilay, T. Jaakkola, and A. Globerson.
    Steps to excellence: Simple inference with refined scoring of dependency trees.
    In Association for Computational Linguistics, 2014.
    [pdf]
  • J. Honorio and T. Jaakkola.
    A unified framework for consistency of regularized loss minimizers.
    In Proceedings of the 31th International Conference on Machine Learning, 2014.
    [pdf]
  • A. Gane, T. Hazan, and T. Jaakkola.
    Learning with maximum a-posteriori perturbation models.
    In Artificial Intelligence and Statistics, 2014.
    [pdf]
  • S. Maji, T. Hazan, and T. Jaakkola.
    Active boundary annotation using random map perturbations.
    In Artificial Intelligence and Statistics, 2014.
    [pdf]
  • J. Honorio and T. Jaakkola.
    Tight bounds for the expected risk of linear classifiers and pac-bayes finite-sample guarantees.
    In Artificial Intelligence and Statistics, 2014.
    [pdf]
  • F. Orabona, T. Hazan, A. Sarwate, and T. Jaakkola.
    On measure concentration of random maximum a-posteriori perturbations.
    In Proceedings of the 31th International Conference on Machine Learning, 2014.
  • O. Meshi, T. Jaakkola, and A. Globerson.
    Smoothed coordinate descent for map inference.
    In S. Nowozin, P. V. Gehler, J. Jancsary, and C. Lampert, editors, Advanced Structured Prediction. MIT Press, 2014.
    [pdf]
  • T. Hazan, S. Maji, J. Keshet, and T. Jaakkola.
    Learning efficient random maximum a-posteriori predictors with non-decomposable loss functions.
    In Advances of Neural Information Processing Systems, 2013.
    [pdf]
  • T. Hazan, S. Maji, and T. Jaakkola.
    On sampling from the gibbs distribution with random maximum a posteriori perturbations.
    In Advances of Neural Information Processing Systems, 2013.
    [pdf]
  • J. Honorio and T. Jaakkola.
    Two-sided exponential concentration bounds for bayes error rate and shannon entropy.
    In Proceedings of the 30th International Conference on Machine Learning, 2013.
    [pdf]
  • J. Honorio and T. Jaakkola.
    Inverse covariance estimation for high-dimensional data in linear time and space: Spectral methods for riccati and sparse models.
    In Proceedings of the 29th Conference on Uncertainty in Artificial Intelligence, 2013.
    [pdf]
  • T. Hazan and T. Jaakkola.
    On the partition function and random maximum a-posteriori perturbations.
    In Proceedings of the 29th International Conference on Machine Learning (ICML), 2012.
    [pdf]
  • O. Meshi, T. Jaakkola, and A. Globerson.
    Convergence rate analysis of map coordinate minimization algorithms.
    In Advances in Neural Information Processing Systems, 2012.
  • Z. Kolter and T. Jaakkola.
    Approximate inference in additive factorial hmms with application to energy disaggregation.
    Proceedings of the 15th International Conference on Artificial Intelligence and Statistics, JMLR WCP, 22:1472--1482, 2012.
    [pdf]
  • Y. Xin and T. Jaakkola.
    Primal-dual methods for sparse constrained matrix completion.
    Proceedings of the 15th International Conference on Artificial Intelligence and Statistics, JMLR WCP, 22:1323--1331, 2012.
    [pdf]
  • 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.
    [pdf]
  • 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.
    [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.
    [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.
    [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] [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]
  • 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]
  • 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]
  • 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]
  • 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.
    [ps.gz]
  • 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.
    [ps.gz]
  • H. Steck and T. Jaakkola.
    Semi-predictive discretization during model selection.
    2003.
    [pdf]
  • N. Srebro and T. Jaakkola.
    Weighted low-rank approximations.
    In Proceedings of the Twentieth International Conference on Machine Learning, 2003.
    [pdf]
  • N. Srebro and T. Jaakkola.
    Generalized low-rank approximations.
    2003.
    [pdf]
  • H. Steck and T. Jaakkola.
    On the dirichlet prior and bayesian regularization.
    In Advances in Neural Information processing systems 15, 2002.
    [ps.gz]
  • 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.
    [ps.gz]
  • M. Szummer and T. Jaakkola.
    Information regularization with partially labeled data.
    In Advances in Neural Information processing systems 15, 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.
    [ps.gz]
  • H. Steck and T. Jaakkola.
    Unsupervised active learning in large domains.
    In Proceedings of the Eighteenth Annual Conference on Uncertainty in Artificial Intelligence, 2002.
    [ps.gz]
  • 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.
    [ps.gz]
  • 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.
    [ps.gz]
  • M. Szummer and T. Jaakkola.
    Partially labeled classification with markov random walks.
    In Advances in Neural Information processing systems 14, 2001.
    [ps]
  • T. Jaakkola and H. Siegelmann.
    Active information retrieval.
    In Advances in Neural Information processing systems 14, pages 777--784, 2001.
    [ps.gz]
  • T. Jaakkola.
    Tutorial on variational approximation methods.
    In Advanced mean field methods: theory and practice. MIT Press, 2000.
    [ps]
  • T. Jaakkola and M. Jordan.
    Bayesian parameter estimation via variational methods.
    Statistics and Computing, 10:25--37, 2000.
    [ps]
  • 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.
    [ps]
  • M. Szummer and T. Jaakkola.
    Kernel expansions with unlabeled examples.
    In Advances in Neural Information Processing Systems 13. MIT Press, 2000.
    [ps]
  • 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.
    [ps]
  • 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.
    [ps]
  • T. Jaakkola, M. Meila, and T. Jebara.
    Maximum entropy discrimination.
    In Advances in Neural Information Processing Systems 12. MIT Press, 1999.
    [ps]
  • T. Jaakkola, M. Meila, and T. Jebara.
    Maximum entropy discrimination.
    Technical report, MIT, 1999.
    [ps]
  • T. Jaakkola and M. Jordan.
    Variational probabilistic inference and the qmr-dt database.
    Journal of Artificial Intelligence Research, 10:291--322, 1999.
    [ps] [pdf]
  • M. Jordan, Z. Ghahramani, T. Jaakkola, and L. Saul.
    An introduction to variational methods for graphical models.
    Machine Learning, 37(2):183, 1999.
    [ps]
  • T. Jaakkola and D. Haussler.
    Probabilistic kernel regression models.
    In Proceedings of the Seventh International Workshop on Artificial Intelligence and Statistics, 1999.
    [ps]
  • T. Jaakkola and D. Haussler.
    Exploiting generative models in discriminative classifiers.
    In Advances in Neural Information Processing Systems 11, 1998.
    [ps]
  • 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.
    [ps]
  • 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.
    [ps]
  • T. Jaakkola.
    Variational methods for inference and estimation in graphical models.
    PhD thesis, MIT, 1997.
    [ps]
  • 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.
    [ps]
  • L. Saul, T. Jaakkola, and M. Jordan.
    Mean field theory for sigmoid belief networks.
    Journal of Artificial Intelligence Research, 4:61--76, 1996.
    [ps] [pdf]
  • T. Jaakkola and M. Jordan.
    Recursive algorithms for approximating probabilities in graphical models.
    In Advances in Neural Information Processing Systems 9, 1996.
    [ps]
  • 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.
    [ps]
  • 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.
    [ps]