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

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

Reinforcement learning papers

  • A. Ajay, Y. Du, A. Gupta, J. Tenenbaum, T. Jaakkola, and P. Agrawal.
    Is conditional generative modeling all you need for decision making?
    In The 11th International Conference on Learning Representations (ICLR), 2023.
    [link]

  • X. Fu, G. Yang, P. Agrawal, and T. Jaakkola.
    Learning task informed abstractions.
    In International Conference on Machine Learning (ICML), 2021.
    [link]

  • 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.
    [ps]

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

  • 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.
    [ps]

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

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

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