Prasad Tadepalli
Prasad Tadepalli
Professor of Computer Science, Oregon State University
Verified email at - Homepage
Cited by
Cited by
Multi-task reinforcement learning: a hierarchical Bayesian approach
A Wilson, A Fern, S Ray, P Tadepalli
Proceedings of the 24th international conference on Machine learning, 1015-1022, 2007
Active learning with committees for text categorization
R Liere, P Tadepalli
AAAI/IAAI, 591-596, 1997
Relational reinforcement learning: An overview
P Tadepalli, R Givan, K Driessens
Proceedings of the ICML-2004 workshop on relational reinforcement learning, 1-9, 2004
Structured machine learning: the next ten years
TG Dietterich, P Domingos, L Getoor, S Muggleton, P Tadepalli
Machine Learning 73 (1), 3, 2008
Dynamic preferences in multi-criteria reinforcement learning
S Natarajan, P Tadepalli
Proceedings of the 22nd international conference on Machine learning, 601-608, 2005
Transfer in variable-reward hierarchical reinforcement learning
N Mehta, S Natarajan, P Tadepalli, A Fern
Machine Learning 73 (3), 289, 2008
Automatic discovery and transfer of MAXQ hierarchies
N Mehta, S Ray, P Tadepalli, T Dietterich
Proceedings of the 25th international conference on Machine learning, 648-655, 2008
Lower bounding Klondike solitaire with Monte-Carlo planning
R Bjarnason, A Fern, P Tadepalli
Nineteenth International Conference on Automated Planning and Scheduling, 2009
Model-based average reward reinforcement learning
P Tadepalli, DK Ok
Artificial intelligence 100 (1-2), 177-224, 1998
Maximizing the predictive value of production rules
SM Weiss, RS Galen, PV Tadepalli
Artificial Intelligence 45 (1-2), 47-71, 1990
A bayesian approach for policy learning from trajectory preference queries
A Wilson, A Fern, P Tadepalli
Advances in neural information processing systems, 1133-1141, 2012
Learning first-order probabilistic models with combining rules
S Natarajan, P Tadepalli, TG Dietterich, A Fern
Annals of Mathematics and Artificial Intelligence 54 (1-3), 223-256, 2008
Lazy ExplanationBased Learning: A Solution to the Intractable Theory Problem.
P Tadepalli
IJCAI, 694-700, 1989
Learning goal-decomposition rules using exercises
C Reddy, P Tadepalli
ICML, 278-286, 1997
A Decision-Theoretic Model of Assistance.
A Fern, S Natarajan, K Judah, P Tadepalli
IJCAI, 1879-1884, 2007
Imitation learning in relational domains: A functional-gradient boosting approach
S Natarajan, S Joshi, P Tadepalli, K Kersting, J Shavlik
IJCAI Proceedings-International Joint Conference on Artificial Intelligence …, 2011
HC-Search: A learning framework for search-based structured prediction
JR Doppa, A Fern, P Tadepalli
Journal of Artificial Intelligence Research 50, 369-407, 2014
Scaling up average reward reinforcement learning by approximating the domain models and the value function
P Tadepalli, DK Ok
ICML, 471-479, 1996
Using trajectory data to improve bayesian optimization for reinforcement learning
A Wilson, A Fern, P Tadepalli
The Journal of Machine Learning Research 15 (1), 253-282, 2014
Multi-agent inverse reinforcement learning
S Natarajan, G Kunapuli, K Judah, P Tadepalli, K Kersting, J Shavlik
2010 Ninth International Conference on Machine Learning and Applications …, 2010
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