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Prasad Tadepalli: [Publications] [Author Rank by year] [Co-authors] [Prefers] [Cites] [Cited by]

Publications of Author

  1. Ray Liere, Prasad Tadepalli
    Active Learning with Committees for Text Categorization. [Citation Graph (0, 0)][DBLP]
    AAAI/IAAI, 1997, pp:591-596 [Conf]
  2. Ray Liere, Prasad Tadepalli
    Active Learning with Committees. [Citation Graph (0, 0)][DBLP]
    AAAI/IAAI, 1997, pp:838- [Conf]
  3. DoKyeong Ok, Prasad Tadepalli
    Auto-Exploratory Average Reward Reinforcement Learning. [Citation Graph (0, 0)][DBLP]
    AAAI/IAAI, Vol. 1, 1996, pp:881-887 [Conf]
  4. Charles Parker, Alan Fern, Prasad Tadepalli
    Gradient Boosting for Sequence Alignment. [Citation Graph (0, 0)][DBLP]
    AAAI, 2006, pp:- [Conf]
  5. Chandra Reddy, Prasad Tadepalli
    Learning Goal-Decomposition Rules Using Exercises. [Citation Graph (0, 0)][DBLP]
    AAAI/IAAI, 1997, pp:843- [Conf]
  6. Prasad Tadepalli
    A Theory of Unsupervised Speedup Learning. [Citation Graph (0, 0)][DBLP]
    AAAI, 1992, pp:229-234 [Conf]
  7. Sholom M. Weiss, Robert S. Galen, Prasad Tadepalli
    Optimizing the Predictive Value of Diagnostic Decision Rules. [Citation Graph (0, 0)][DBLP]
    AAAI, 1987, pp:521-527 [Conf]
  8. Thomas R. Amoth, Paul Cull, Prasad Tadepalli
    Exact Learning of Tree Patterns from Queries and Counterexamples. [Citation Graph (0, 0)][DBLP]
    COLT, 1998, pp:175-186 [Conf]
  9. Thomas R. Amoth, Paul Cull, Prasad Tadepalli
    Exact Learning of Unordered Tree Patterns from Queries. [Citation Graph (0, 0)][DBLP]
    COLT, 1999, pp:323-332 [Conf]
  10. Scott Proper, Prasad Tadepalli
    Scaling Model-Based Average-Reward Reinforcement Learning for Product Delivery. [Citation Graph (0, 0)][DBLP]
    ECML, 2006, pp:735-742 [Conf]
  11. Michael Chisholm, Prasad Tadepalli
    Learning Decision Rules by Randomized Iterative Local Search. [Citation Graph (0, 0)][DBLP]
    ICML, 2002, pp:75-82 [Conf]
  12. Sridhar Mahadevan, Prasad Tadepalli
    On the Tractability of Learning from Incomplete Theories. [Citation Graph (0, 0)][DBLP]
    ML, 1988, pp:235-241 [Conf]
  13. Sriraam Natarajan, Prasad Tadepalli
    Dynamic preferences in multi-criteria reinforcement learning. [Citation Graph (0, 0)][DBLP]
    ICML, 2005, pp:601-608 [Conf]
  14. Balas K. Natarajan, Prasad Tadepalli
    Two New Frameworks for Learning. [Citation Graph (0, 0)][DBLP]
    ML, 1988, pp:402-415 [Conf]
  15. Sriraam Natarajan, Prasad Tadepalli, Eric Altendorf, Thomas G. Dietterich, Alan Fern, Angelo C. Restificar
    Learning first-order probabilistic models with combining rules. [Citation Graph (0, 0)][DBLP]
    ICML, 2005, pp:609-616 [Conf]
  16. Chandra Reddy, Prasad Tadepalli
    Learning Goal-Decomposition Rules using Exercises. [Citation Graph (0, 0)][DBLP]
    ICML, 1997, pp:278-286 [Conf]
  17. Chandra Reddy, Prasad Tadepalli
    Learning First-Order Acyclic Horn Programs from Entailment. [Citation Graph (0, 0)][DBLP]
    ICML, 1998, pp:472-480 [Conf]
  18. Chandra Reddy, Prasad Tadepalli, Silvana Roncagliolo
    Theory-guided Empirical Speedup Learning of Goal Decomposition Rules. [Citation Graph (0, 0)][DBLP]
    ICML, 1996, pp:409-417 [Conf]
  19. Sandeep Seri, Prasad Tadepalli
    Model-based Hierarchical Average-reward Reinforcement Learning. [Citation Graph (0, 0)][DBLP]
    ICML, 2002, pp:562-569 [Conf]
  20. Prasad Tadepalli
    Planning Approximate Plans for Use in the Real World. [Citation Graph (0, 0)][DBLP]
    ML, 1989, pp:224-228 [Conf]
  21. Prasad Tadepalli
    Learning with Incrutable Theories. [Citation Graph (0, 0)][DBLP]
    ML, 1991, pp:544-548 [Conf]
  22. Prasad Tadepalli
    Learning from Queries and Examples with Tree-structured Bias. [Citation Graph (0, 0)][DBLP]
    ICML, 1993, pp:322-329 [Conf]
  23. Prasad Tadepalli, Thomas G. Dietterich
    Hierarchical Explanation-Based Reinforcement Learning. [Citation Graph (0, 0)][DBLP]
    ICML, 1997, pp:358-366 [Conf]
  24. Prasad Tadepalli, DoKyeong Ok
    Scaling Up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function. [Citation Graph (0, 0)][DBLP]
    ICML, 1996, pp:471-479 [Conf]
  25. Prasad Tadepalli
    Lazy ExplanationBased Learning: A Solution to the Intractable Theory Problem. [Citation Graph (0, 0)][DBLP]
    IJCAI, 1989, pp:694-700 [Conf]
  26. Prasad Tadepalli
    A Formalization of Explanation-Based Macro-operator Learning. [Citation Graph (0, 0)][DBLP]
    IJCAI, 1991, pp:616-622 [Conf]
  27. Alan Fern, Sriraam Natarajan, Kshitij Judah, Prasad Tadepalli
    A Decision-Theoretic Model of Assistance. [Citation Graph (0, 0)][DBLP]
    IJCAI, 2007, pp:1879-1884 [Conf]
  28. Chandra Reddy, Prasad Tadepalli
    Learning Horn Definitions with Equivalence and Membership Queries. [Citation Graph (0, 0)][DBLP]
    ILP, 1997, pp:243-255 [Conf]
  29. Chandra Reddy, Prasad Tadepalli
    Learning First-Order Acyclic Horn Programs from Entailment. [Citation Graph (0, 0)][DBLP]
    ILP, 1998, pp:23-37 [Conf]
  30. Sridhar Mahadevan, Tom M. Mitchell, Jack Mostow, Louis I. Steinberg, Prasad Tadepalli
    An Apprentice-Based Approach to Knowledge Acquisition. [Citation Graph (0, 0)][DBLP]
    Artif. Intell., 1993, v:64, n:1, pp:1-52 [Journal]
  31. Prasad Tadepalli, DoKyeong Ok
    Model-Based Average Reward Reinforcement Learning. [Citation Graph (0, 0)][DBLP]
    Artif. Intell., 1998, v:100, n:1-2, pp:177-223 [Journal]
  32. Sholom M. Weiss, Robert S. Galen, Prasad Tadepalli
    Maximizing the Predictive Value of Production Rules. [Citation Graph (0, 0)][DBLP]
    Artif. Intell., 1990, v:45, n:1-2, pp:47-71 [Journal]
  33. Prasad Tadepalli, Balas K. Natarajan
    A Formal Framework for Speedup Learning from Problems and Solutions [Citation Graph (0, 0)][DBLP]
    CoRR, 1996, v:0, n:, pp:- [Journal]
  34. Prasad Tadepalli, Balas K. Natarajan
    A Formal Framework for Speedup Learning from Problems and Solutions. [Citation Graph (0, 0)][DBLP]
    J. Artif. Intell. Res. (JAIR), 1996, v:4, n:, pp:445-475 [Journal]
  35. Thomas R. Amoth, Paul Cull, Prasad Tadepalli
    On Exact Learning of Unordered Tree Patterns. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 2001, v:44, n:3, pp:211-243 [Journal]
  36. Sridhar Mahadevan, Prasad Tadepalli
    Quantifying Prior Determination Knowledge Using the PAC Learning Model. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1994, v:17, n:1, pp:69-105 [Journal]
  37. Prasad Tadepalli, Stuart J. Russell
    Learning from Examples and Membership Queries with Structured Determinations. [Citation Graph (0, 0)][DBLP]
    Machine Learning, 1998, v:32, n:3, pp:245-295 [Journal]
  38. Chandra Reddy, Prasad Tadepalli
    Learning Horn Definitions: Theory and an Application to Planning. [Citation Graph (0, 0)][DBLP]
    New Generation Comput., 1999, v:17, n:1, pp:77-98 [Journal]
  39. Aaron Wilson, Alan Fern, Soumya Ray, Prasad Tadepalli
    Multi-task reinforcement learning: a hierarchical Bayesian approach. [Citation Graph (0, 0)][DBLP]
    ICML, 2007, pp:1015-1022 [Conf]
  40. Charles Parker, Alan Fern, Prasad Tadepalli
    Learning for efficient retrieval of structured data with noisy queries. [Citation Graph (0, 0)][DBLP]
    ICML, 2007, pp:729-736 [Conf]
  41. Sriraam Natarajan, Kshitij Judah, Prasad Tadepalli, Alan Fern
    A Decision-Theoretic Model of Assistance - Evaluation, Extensions and Open Problems. [Citation Graph (0, 0)][DBLP]
    Interaction Challenges for Intelligent Assistants, 2007, pp:90-97 [Conf]

  42. Bayesian Policy Search for Multi-Agent Role Discovery. [Citation Graph (, )][DBLP]


  43. Lower Bounding Klondike Solitaire with Monte-Carlo Planning. [Citation Graph (, )][DBLP]


  44. Solving multiagent assignment Markov decision processes. [Citation Graph (, )][DBLP]


  45. Bayesian role discovery for multi-agent reinforcement learning. [Citation Graph (, )][DBLP]


  46. Exploiting prior knowledge in Intelligent Assistants - Combining relational models with hierarchies. [Citation Graph (, )][DBLP]


  47. Automatic discovery and transfer of MAXQ hierarchies. [Citation Graph (, )][DBLP]


  48. Learning Parameters for Relational Probabilistic Models with Noisy-Or Combining Rule. [Citation Graph (, )][DBLP]


  49. Multiagent Transfer Learning via Assignment-Based Decomposition. [Citation Graph (, )][DBLP]


  50. A Relational Hierarchical Model for Decision-Theoretic Assistance. [Citation Graph (, )][DBLP]


  51. Logical Hierarchical Hidden Markov Models for Modeling User Activities. [Citation Graph (, )][DBLP]


  52. Transfer Learning via Relational Templates. [Citation Graph (, )][DBLP]


  53. Incorporating Domain Models into Bayesian Optimization for RL. [Citation Graph (, )][DBLP]


  54. Exploiting Causal Independence in Markov Logic Networks: Combining Undirected and Directed Models. [Citation Graph (, )][DBLP]


  55. Learning Algorithms for Link Prediction Based on Chance Constraints. [Citation Graph (, )][DBLP]


  56. Learning first-order probabilistic models with combining rules. [Citation Graph (, )][DBLP]


  57. Learning to Solve Problems from Exercises. [Citation Graph (, )][DBLP]


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