Tommi S. Jaakkola, Ph.D.
Professor of Electrical Engineering and Computer Science

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

E-mail: tommi at csail.mit.edu

Home

Papers

Research

Courses

People

Research synopsis (more...)

On the theoretical side, our research focuses on statistical inference and estimation, development of principled approximation methods for problems with limited computational resources, analysis and development of algorithms for various modern estimation problems such as those involving predominantly incomplete data. The applied side of our work involves primarily functional genomics (transcriptional regulation), large scale inference problems, and information retrieval.

Students/postdocs* (more...)

David Alvarez, Andreea Gane, Vikas Garg, Tatsu Hashimoto, Jean Honorio*, Paresh Malalur, Jonas Mueller, David Reshef, Yu Xin

Tutorials

NIPS*2011 Tutorial with Amir Globerson on LP relaxations: Part 1 (Jaakkola) [pdf], Part 2 (Globerson) [pdf]

Recent papers (more...)

  • Y. Xin and T. Jaakkola.
    Controlling privacy in recommender systems.
    In Advanced in Neural Information Processing Systems, 2014.

  • Y. Zhang, T. Lei, R. Barzilay, and T. Jaakkola.
    Greed is good if randomized: New inference for dependency parsing.
    In Conference on Empirical Methods in Natural Language Processing (EMNLP), 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.

  • R. Sherwood, T. Hashimoto, C. O'Donnell, S. Lewis, A. Barkal, J.P. van Hoff, V. Karun, T. Jaakkola, and D. Gifford.
    Discovery of directional and nondirectional pioneer transcription factors by modeling dnase profile magnitude and shape.
    Nature Biotechnology, 32(2):171--178, 2014.
    [pdf]

  • T. Hazan, S. Maji, J. Keshet, and T. Jaakkola.
    Learning efficient random maximum a-posteriori predictors with non-decomposable loss functions.
    In 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 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.

  • T. Hashimoto, T. Jaakkola, R. Sherwood, E. Mazzoni, H. Witchterle, and D. Gifford.
    Lineage based identification of cellular states and expression programs.
    In Proceedings of the 20th Annual International Conference on Intelligent Systems for Molecular Biology (ISMB), 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]