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Lihong Li: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Lihong Li, Michael L. Littman
    Lazy Approximation for Solving Continuous Finite-Horizon MDPs. [Citation Graph (0, 0)][DBLP]
    AAAI, 2005, pp:1175-1180 [Conf]
  2. Ilya Levner, Vadim Bulitko, Lihong Li, Greg Lee, Russell Greiner
    Towards Automated Creation of Image Interpretation Systems. [Citation Graph (0, 0)][DBLP]
    Australian Conference on Artificial Intelligence, 2003, pp:653-665 [Conf]
  3. Lihong Li, Vadim Bulitko, Russell Greiner
    Batch Reinforcement Learning with State Importance. [Citation Graph (0, 0)][DBLP]
    ECML, 2004, pp:566-568 [Conf]
  4. Alexander L. Strehl, Lihong Li, Eric Wiewiora, John Langford, Michael L. Littman
    PAC model-free reinforcement learning. [Citation Graph (0, 0)][DBLP]
    ICML, 2006, pp:881-888 [Conf]
  5. Vadim Bulitko, Lihong Li, Russell Greiner, Ilya Levner
    Lookahead Pathologies for Single Agent Search. [Citation Graph (0, 0)][DBLP]
    IJCAI, 2003, pp:1531-1533 [Conf]
  6. Thomas J. Walsh, Ali Nouri, Lihong Li, Michael L. Littman
    Planning and Learning in Environments with Delayed Feedback. [Citation Graph (0, 0)][DBLP]
    ECML, 2007, pp:442-453 [Conf]
  7. Ronald Parr, Christopher Painter-Wakefield, Lihong Li, Michael L. Littman
    Analyzing feature generation for value-function approximation. [Citation Graph (0, 0)][DBLP]
    ICML, 2007, pp:737-744 [Conf]
  8. Alexander L. Strehl, Lihong Li, Michael L. Littman
    Incremental Model-based Learners With Formal Learning-Time Guarantees. [Citation Graph (0, 0)][DBLP]
    UAI, 2006, pp:- [Conf]

  9. Feature Matrix Extraction and Classification of XML Pages. [Citation Graph (, )][DBLP]

  10. Online exploration in least-squares policy iteration. [Citation Graph (, )][DBLP]

  11. A worst-case comparison between temporal difference and residual gradient with linear function approximation. [Citation Graph (, )][DBLP]

  12. Knows what it knows: a framework for self-aware learning. [Citation Graph (, )][DBLP]

  13. An analysis of linear models, linear value-function approximation, and feature selection for reinforcement learning. [Citation Graph (, )][DBLP]

  14. The adaptive k-meteorologists problem and its application to structure learning and feature selection in reinforcement learning. [Citation Graph (, )][DBLP]

  15. Workshop summary: Results of the 2009 reinforcement learning competition. [Citation Graph (, )][DBLP]

  16. Sparse Online Learning via Truncated Gradient. [Citation Graph (, )][DBLP]

  17. CORL: A Continuous-state Offset-dynamics Reinforcement Learner. [Citation Graph (, )][DBLP]

  18. Maintaining Equilibria During Exploration in Sponsored Search Auctions. [Citation Graph (, )][DBLP]

  19. A contextual-bandit approach to personalized news article recommendation. [Citation Graph (, )][DBLP]

  20. A Framework of Face Tracking with Classification Using CAMShift-C and LBP. [Citation Graph (, )][DBLP]

  21. An Improved Scheme of SEP in Heterogeneous Wireless Sensor Networks. [Citation Graph (, )][DBLP]

  22. Energy-Efficiency Cooperative Communications with Node Selection for Wireless Sensor Networks. [Citation Graph (, )][DBLP]

  23. Learning and planning in environments with delayed feedback. [Citation Graph (, )][DBLP]

  24. Sparse Online Learning via Truncated Gradient [Citation Graph (, )][DBLP]

  25. An Optimal High Probability Algorithm for the Contextual Bandit Problem [Citation Graph (, )][DBLP]

  26. A Contextual-Bandit Approach to Personalized News Article Recommendation [Citation Graph (, )][DBLP]

  27. An Unbiased, Data-Driven, Offline Evaluation Method of Contextual Bandit Algorithms [Citation Graph (, )][DBLP]

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