The SCEAS System
Navigation Menu

Search the dblp DataBase

Title:
Author:

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]


Search in 0.009secs, Finished in 0.307secs
NOTICE1
System may not be available sometimes or not working properly, since it is still in development with continuous upgrades
NOTICE2
The rankings that are presented on this page should NOT be considered as formal since the citation info is incomplete in DBLP
 
System created by asidirop@csd.auth.gr [http://users.auth.gr/~asidirop/] © 2002
for Data Engineering Laboratory, Department of Informatics, Aristotle University © 2002