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]   [courses]   [people]

The list does not include all recent preprints from arXiv or bioRxiv. For a more complete list, see my Google scholar page

Reinforcement learning papers

• Anurag Ajay, Yilun Du, Abhi Gupta, Joshua Tenenbaum, Tommi Jaakkola, and Pulkit Agrawal.
  Is Conditional Generative Modeling all you need for Decision Making?.
  The 11th International Conference on Learning Representations (ICLR), 2023.
  [link]
• Xiang Fu, Ge Yang, Pulkit Agrawal, and Tommi Jaakkola.
  Learning Task Informed Abstractions.
  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), pp. 287. 2000.
  [ps]
• S. Singh, T. Jaakkola, and M. Jordan.
  Reinforcement learning with soft state aggregation.
  Advances in Neural Information Processing Systems (NIPS), 1994.
  [ps]
• T. Jaakkola, S. Singh, and M. Jordan.
  Reinforcement Learning Algorithm for Partially Observable Markov Decision Problems.
  Advances in Neural Information Processing Systems (NIPS), 1994.
  [ps]
• S. Singh, T. Jaakkola, and M. Jordan.
  Learning without state estimation in partially observable environments.
  Proceedings of the Eleventh Machine Learning Conference (ICML), 1994.
  [ps]
• T. Jaakkola, M. Jordan, and S. Singh.
  On the Convergence of Stochastic Iterative Dynamic Programming Algorithms.
  Neural Computation, 6(6), pp. 1185–1201. 1994.
  [ps]
• T. Jaakkola, M. Jordan, and S. Singh.
  Convergence of stochastic iterative Dynamic Programming algorithms.
  Advances in Neural Information Processing Systems (NIPS), 1993.
  [ps]