Doppa, J. R., Fern, A., and Tadepalli, P., HC-Search: A Learning Framework for Search-based Structured Prediction, Journal of Artificial Intelligence Research (JAIR), 2014. Accepted.
Doppa, J. R., Fern, A., and Tadepalli, P., Structured Prediction via Output Space Search , Journal of Machine Learning Research (JMLR), vol 15, 2014.
Wilson, A., Fern, A., and Tadepalli, P., Using Trajectory Data to Improve Bayesian Optimization for Reinforcement Learning , Journal of Machine Learning Research (JMLR), 15, 253--282, 2014.
Fern, A., Natarajan, S., Judah, K., and Tadepalli, P., A Decision-Theoretic Model of Assistance , Journal Of Artificial Intelligence Research (JAIR), 2014, Accepted.
Natarajan, S., Tadepalli, P., Fern, A., A Relational Hierarchical Model of Decision-Theoretic Assistance, {\em Knowledge and Information Systems(KAIS)}, 32(2), 329-349, 2012.
Mehta, N., Ray, S., Tadepalli, P., Dietterich, T. G., Automatic Discovery and Transfer of Task Hierarchies in Reinforcement Learning, AI Magazine 32(1): 35-50, 2011.
Natarajan, S., Tadepalli, P., Dietterich, T. and Fern, A., Learning First-Order Probabilistic Models with Combining Rules Annals of Mathematics and Artificial Intelligence , Special issue on Probabilistic Relational Learning, 2009.
Tadepalli, P. Learning to Solve Problems from Exercises Computational Intelligence , 24(4), 257--291, 2008.
Dietterich, T. G., Domingos, P., Getoor, L., Muggleton, S. and Tadepalli, P. Structured Machine Learning: The Next Ten Years , Machine Learning , August, 2008.
Mehta, N., Natarajan, S., Tadepalli, P., and Fern, A. Transfer in variable-reward hierarchical reinforcement learning, Machine Learning , June, 2008.
Bjarnason, R., Tadepalli, P. and Fern, A. Searching Solitaire in Real Time, International Computer Games Association Journal , 30 (3), 131-142, 2007
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.
Ghaeni, R., Fern, X., Huang, L., and Tadepalli, P. Event Nugget Detection with Forward-Backward Recurrent Neural Networks, In Proceedings of the Association of Computational Linguistics, 2016.
Obeidat, R., Fern, X., annd Tadepalli, P. Label Embedding for Transfer Learning In ICBO/BioCreative, 2016.
Goetschalckx, R., Fern, A., and Tadepalli, P., Multitask Coactive Learning , In Proceeedings of the International Joint Conference on Artificial Intelligence, 2015.
Hamidi, M., Tadepali, P., Goetschalckx, R., and Fern, A., Active Imitation Learning of Hierarchical Policies , In Proceeedings of the International Joint Conference on Artificial Intelligence, 2015.
Raghavan, A., Khardon, R., Tadepalli, P., and Fern, A., Memory-Efficient Symbolic Online Planning for Factored MDPs, In Proceedings of the International Conference on Uncertainty in Artificial Intelligence (UAI), 2015.
Cui, H., Khardon, R., Fern, A., and Tadepalli, P., Factored MCTS for Large Scale Stochastic Planning , In Proceedings of the AAAI Conference on Artificial Intelligence (AAAI), 2015.
Issakkimuthu, M., Fern, A., Khardon, R., Tadepalli, P., and Xue, S., Hindsight Optimization for Probabilistic Planning with Factored Actions , In Proceedings of the International Conference on Automated Planning and Scheduling (ICAPS), 2015.
Xie, J., Ma, C., Doppa, J. R., Mannem, P, Fern, X., Dietterich, T., and Tadepalli, P., Learning Greedy Policies for the Easy-First Framework , In Proceedings of National Conference on Artificial Intelligence (AAAI), 2015.
Ma, C., Doppa, J. R., Mannem, P, Fern, X., Dietterich, T., and Tadepalli, P., Prune-and-Score: Learning for Greedy Coreference Resolution , In Proceedings of International Conference on Empirical Methods in Natural Language Processing (EMNLP), 2014.
Orr, J. W., Tadepalli, P., Doppa, J. R., Fern, X., and Dietterich, T., Learning Scripts as Hidden Markov Models , In Proceeedings of the National Conference on Artificial Intelligence, 2014.
Judah, K., Fern, A., Tadepalli, P., and Goetschalckx, R., Imitation Learning with Demonstrations and Shaping Rewards , In Proceeedings of the National Conference on Artificial Intelligence, 2014.
Goetschalckx, R., Fern, A., and Tadepalli, P., Coactive Learning for Locally Optimal Problem Solving , In Proceeedings of the National Conference on Artificial Intelligence, 2014.
Doppa, J. R., Yu, J., Ma, C., Fern, A., and Tadepalli, P., HC-Search for Multi-label Prediction: An Empirical Study , Proceedings of National Conference on Artificial Intelligence, 2014.
Raghavan, A., Fern, A., Tadepalli, P., and Khardon, R., Symbolic Opportunistic Policy Iteration for Factored-Action MDPs , Proceedings of the International Conference on Neural Information Processing Systems (NIPS), 2499--2507, 2013.
Joshi, S., Khardon, R., Tadepalli, P., Raghavan, A., and Fern, A. Solving Relational MDPs with Exogenous Events and Additive Rewards , Euoropean Conference on Machine Learning, 178--193, 2013.
Natarajan, S., Odom, P., Joshi, S., Khot, T., Kersting, K., and Tadepalli, P., Accelerating Imitation Learning in Relational Domains via Transfer by Initialization , Proceedings of International Conference on Inductive Logic Programming, 2013.
Doppa, J. R., Fern, A., Tadepalli, P. HC-Search: Learning Heuristics and Cost Functions for Structured Prediction , Proceedings of National Conference on Artificial Intelligence, 2013.
Raghavan, A., Joshi, S., Fern, A., Tadepalli, P., Khardon, R. Planning in Factored Action Spaces with Symbolic Dynamic Programming , Proceedings of National Conference on Artificial Intelligence (AAAI), 2012.
Wilson, A., Fern, A. and Tadepalli, P., A Bayesian Approach to Policy Learning from Trajectory Preference Queries, Neural Information Processing Systems (NIPS), 2012.
Doppa, J. R., Fern, A. and Tadepalli, P., Output Space Search for Structured Prediction in Proceedings of International Conference on Machine Learning (ICML), 2012.
Doppa, J. R., Sorower, S. Nasresfahani, M., Irvine, J., Orr, W. Dietterich, T. G., Fern, X., and Tadepalli, P., Learning Rules from Incomplete Examples via Implicit Mention Models, Journal of Machine Learning Research (JMLR) Proceedings Track, volume 20, pp 197-212 (ACML-2011)
Sorower, S., Dietterich, T. G., Doppa, J., Orr, W., Fern, X. and Tadepalli, P. Inverting Grice's Maxims to Learn Rules from Natural Language Extractions in Proceedings of Advances in Neural Information Processing Systems, NIPS, 2011.
N. Mehta, P. Tadepalli, and A. Fern.
Autonomous Learning of Action Models for Planning in NIPS, 2011.
Natarajan, S., Joshi, S., Tadepalli, P., Kersting, K., and Shavlik, J.
Imitation Learning in Relational Domains: A Functional-Gradient Boosting Approach, in International Joint Conference in AI (IJCAI) , 2011.
Fern, A. and Tadepalli, P.,
A Computational Decision Theory for Interactive Assistants ,
in Neural Information Processing Systems , 2010.
Wilson, A., Fern, A. and Tadepalli, P.,
Incorporating Domain Models into Bayesian Optimization for Reinforcement Learning in European Conference on Machine Learning , 2010.
Wilson, A., Fern, A. and Tadepalli, P.,
Bayesian Policy Search for Multiagent Role Discovery
in National Conference on Artificial Intelligence , 2010.
Bjarnason, R., Fern, A. and Tadepalli, P., Lower Bounding Klondike Solitaire with Monte-Carlo Planning. in International Conference on Automated Planning and Scheduling (ICAPS) , 2009.
Proper, S., Tadepalli, P.,
Multiagent Transfer Learning via Assignment-based Decomposition
in Proceedings of the Interanational Conference on Machine Learning and
Applications, 2009.
Proper, S., Tadepalli, P.,
Solving Multiagent Assignment Markov Decision Processes
in Proceedings of the 8th International Conference on Autonomous Agents and Multiagent Systems p 681-688, 2009.
Proper, S., Tadepalli, P.,
Transfer Learning via Relational Templates ,
in Proceedings of the 19th International Conference on Inductive Logic Programming , 2009.
Bjarnason, R., Tadepalli, P., Fern, A. and Niedner, C., Simulation-based Optimization of Resource Placement and Emergency Response in Conference on Innovative Applications of Artificial Intelligence (IAAI) , 2009, p 47--53.
Mehta, N., Ray, S., Tadepalli, P., and Dietterich, T.
Automatic Discovery and Transfer of MAXQ Hierarchies
International Conference on Machine Learning , 648--655, 2008.
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.
Ray, S. and Tadepalli, P.
Model-based Reinforcement Learning in Encyclopedia of Machine Learning .
Fern, A. and Tadepalli, P.
A Computational Decision Theory for Interactive Assistants in the workshop on Interactive Decision Theory and Game Theory at the 24'th National Conference on Artificial Intelligence, Atlanta, GA.
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.
Book Chapters/Workshop papers
S. Joshi, R. Khardon, P. Tadepalli, A. Fern, A. Raghavan,
Relational Markov Decision Processes: Promise and Prospects , Workshop on Statistical Relational AI held at National
Conference on Artificial Inteligence, 2013.