Tadepalli, P. Cognitive Architectures have Limited Explanatory Power Commentary on Anderson and Lebiere's article on ``The Newell Test for a Theory of Mind,'' in Behavioral and Brain Sciences .
Amoth, T., Cull, P. and Tadepalli, P. On Exact Learning of Unordered Tree Patterns. Machine Learning , 44(3), 211--243, 2001.
Reddy, C. and Tadepalli, P. Learning Horn Definitions: Theory and an Application to Planning. New Generation Computing , 17, 77--98, 1999.
Tadepalli, P. and Ok, D. Model-based Average Reward Reinforcement Learning. Artificial Intelligence , 100, 177--224, 1998.
Tadepalli, P. and Natarajan, B. A Formal Framework for Speedup Learning from Problems and Solutions. , Journal of AI Research 4, 445-475, 1996.
Tadepalli, P. and Russell, S. Learning from Queries and Examples with Structured Determinations. Machine Learning , 32, 245--295, 1998.
Mahadevan, S. and Tadepalli, P., Quantifying Prior Determination Knowledge using PAC Learning Model. Machine Learning , 17, 69-105, 1994.
Mahadevan, S., Mitchell, T., Mostow, J., Stienberg, L. and Tadepalli, P. An Apprentice-based Approach to Knowledge Acquisition. Artificial Intelligence. 64, 1-52, 1993.
Weiss, S., Galen, R., and Tadepalli, P., Maximizing the Predictive Value of Production Rules. Artificial Intelligence. 45, 47-71, 1990.
Hall, P.R., Falkenhainer, B., Flann, N. S., Hampson, S., Reinke, R., Shrager, J., Sims, M., and Tadepalli, P. A review of the fourth international workshop on machine learning. Machine Learning. 2 (2), 1987.
Natarajan, S., Tadepalli, P., and Fern A. A Relational Hierarchical Model for Decision Theoretic Assistance (draft) International Conference on Inductive Logic Programming , 2007.
Parker, C., Fern, A., and Tadepalli, P. Learning for Efficient Retrieval of Structured Data with Noisy Queries International Conference on Machine Learning , 2007.
Wilson, A., Fern, A., Ray, S. and Tadepalli, P. Multi-Task Reinforcement Learning: A Hierarchical Bayesian Approach International Conference on Machine Learning , 2007.
Fern, A., Natarajan, S., Judah, K., and Tadepalli, P. A Decision-theoretic Model of Assistance , International Joint Conference on Artificial Intelligence , 2007.
Parker, C., Fern, A. and Tadepalli, P. Gradient Boosting for Sequence Alignment , National Conference on Artificial Intelligence , 2006.
Proper, S. and Tadepalli, P. Scaling Model-Based Average-Reward Reinforcement Learning for Product Delivery European Conference on Machine Learning , 2006.
Natarajan, S. and Tadepalli, P. Dynamic Preferences in Multi-Criteria Reinforcement Learning International Conference on Machine Learning , 2005.
Natarajan, S., Tadepalli, P., Altendorf, E., Deitterich, T., Fern, A., and Restificar, A. Learning First-Order Probabilistic Models with Combining Rules International Conference on Machine Learning , 2005.
Seri, S. and Tadepalli, P. Model-based Hierarchical Average-reward Reinforcement Learning , International Conference on Machine Learning , 2002.
Chisholm, M. and Tadepalli, P. Learning Decision Rules by Randomized Iterative Local Search , International Conference on Machine Learning , 2002.
Amoth, T., Cull, P. and Tadepalli, P. Exact Learning of Unordered Tree Patterns from Queries. Computational Learning Theory Conference , 1999.
Amoth, T., Cull, P. and Tadepalli, P. Exact Learning of Tree Patterns from Queries and Counterexamples. Computational Learning Theory Conference , 1998.
Reddy, C., Tadepalli, P. Learning First-Order Acyclic Horn Programs from Entailment. International Conference on Inductive Logic Programming , also appeared in International Conference on Machine Learning , 1998.
Reddy, C. and Tadepalli, P. Learning Horn Definitions using Equivalence and Membership Queries. International Conference on Inductive Logic Programming , 1997.
Tadepalli, P. and Dietterich, T. G. Hierarchical Explanation-Based Reinforcement Learning. Proceedings of International Machine Learning Conference, 1997.
Liere, R. and Tadepalli, P. Active Learning with Committees for Text Categorization . Proceedings of AAAI-97 , Providence, RI, 1997 591--596.
Reddy, C., Tadepalli, P. Learning Goal-Decomposition Rules using Exercises. Proceedings of International Conference on Machine Learning (ICML-1997) .
Tadepalli, P. and Ok, D. Scaling up Average Reward Reinforcement Learning by Approximating the Domain Models and the Value Function. Proceedings of International Machine Learning Conference , 1996.
Ok, D. and Tadepalli, P. Auto-exploratory Average Reward Reinforcement Learning. Proceedings of AAAI-96.
Reddy, C., Tadepalli, P. and Roncagliolo, S. Theory-guided Empirical Speedup Learning of Goal Decomposition Rules. Proceedings of International Machine Learning Conference , 1996.
Tadepalli, P., Learning from Queries and Examples with Tree-Structured Bias. Proceedings of International Machine Learning Conference , Amherst, MA, 1993.
Tadepalli, P., A Theory of Unsupervised Speedup Learning. Proceedings of National Conference on Artificial Intelligence , SanJose, CA, 1992.
Tadepalli, P. A Formalization of Explanation-Based Macro-operator Learning. International Joint Conference on Artificial Intelligence, Sydney, Australia, 1991.
Tadepalli, P. Lazy Explanation-Based Learning: A Solution to the Intractable Theory Problem. Proceedings of International Joint Conference on Artificial Intelligence, Detriot, MI, 1989.
Natarajan, B. and Tadepalli, P. Two New Frameworks for Learning. In Proceedings of the International Machine Learning Conference, Ann Arbor, MI, 1988.
Mahadevan, S. and Tadepalli, P. On the Tractability of Learning from Incomplete Theories. Proceedings of the International Machine Learning Conference, Ann Arbor, MI, 1988.
Weiss, S., Galen, R., and Tadepalli, P. Optimizing the Predictive Value of Diagnostic Decision Rules. Proceedings of the National Conference of AAAI-87, Seattle, 1987.
Tadepalli, P., Givan, R. and Driessens, K. Relational Reinforcement Learning: An Overview,
Roncagliolo, S. and Tadepalli, P. Function Approximation in Hierarchical Relational Reinforcement Learning
Liere, R. and Tadepalli, P. The Use of Active Learning in Text Categorization Working Notes of the AAAI 1996 Spring Symposium on Machine Learning in Information Access , Stanford, CA, 1996.
Tadepalli, P. and Ok, D. H-Learning: A Reinforcement Learning Method to Optimize Undiscounted Average Reward. Technical Report 94-30-1, Oregon State University, Department of Computer Science.