Nan Jiang

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Research Output

  • 11 Conference contribution
  • 6 Conference article

Information-theoretic considerations in batch reinforcement learning

Chen, J. & Jiang, N., Jan 1 2019, 36th International Conference on Machine Learning, ICML 2019. International Machine Learning Society (IMLS), p. 1792-1817 26 p. (36th International Conference on Machine Learning, ICML 2019; vol. 2019-June).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • Provably efficient RL with rich observations via latent state decoding

    Du, S. S., Krishnamurthy, A., Jiang, N., Agarwal, A., Dudík, M. & Langford, J., Jan 1 2019, 36th International Conference on Machine Learning, ICML 2019. International Machine Learning Society (IMLS), p. 2971-3002 32 p. (36th International Conference on Machine Learning, ICML 2019; vol. 2019-June).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • Completing state representations using spectral learning

    Jiang, N., Kulesza, A. & Singh, S., Jan 1 2018, In : Advances in Neural Information Processing Systems. 2018-December, p. 4328-4337 10 p.

    Research output: Contribution to journalConference article

  • Hierarchical imitation and reinforcement learning

    Le, H. M., Jiang, N., Agarwal, A., Dudík, M., Yue, Y. & Daumé, H., Jan 1 2018, 35th International Conference on Machine Learning, ICML 2018. Dy, J. & Krause, A. (eds.). International Machine Learning Society (IMLS), p. 4560-4573 14 p. (35th International Conference on Machine Learning, ICML 2018; vol. 7).

    Research output: Chapter in Book/Report/Conference proceedingConference contribution

  • On oracle-efficient PAC RL with rich observations

    Dann, C., Jiang, N., Krishnamurthy, A., Agarwal, A., Langford, J. & Schapire, R. E., Jan 1 2018, In : Advances in Neural Information Processing Systems. 2018-December, p. 1422-1432 11 p.

    Research output: Contribution to journalConference article