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
Dynamic preferences in multi-criteria reinforcement learning
S Natarajan, P Tadepalli
Proceedings of the 22nd international conference on Machine learning, 601-608, 2005
A bayesian approach for policy learning from trajectory preference queries
A Wilson, A Fern, P Tadepalli
Advances in neural information processing systems 25, 2012
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, 3-23, 2008
A decision-theoretic model of assistance
A Fern, S Natarajan, K Judah, P Tadepalli
Journal of Artificial Intelligence Research 50, 71-104, 2014
Transfer in variable-reward hierarchical reinforcement learning
N Mehta, S Natarajan, P Tadepalli, A Fern
Machine Learning 73, 289-312, 2008
Model-based average reward reinforcement learning
P Tadepalli, DK Ok
Artificial intelligence 100 (1-2), 177-224, 1998
Lower bounding Klondike solitaire with Monte-Carlo planning
R Bjarnason, A Fern, P Tadepalli
Proceedings of the International Conference on Automated Planning and …, 2009
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
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
Learning first-order probabilistic models with combining rules
S Natarajan, P Tadepalli, E Altendorf, TG Dietterich, A Fern, A Restificar
Proceedings of the 22nd international conference on Machine learning, 609-616, 2005
Interpreting recurrent and attention-based neural models: a case study on natural language inference
R Ghaeini, XZ Fern, P Tadepalli
arXiv preprint arXiv:1808.03894, 2018
Maximizing the predictive value of production rules
SM Weiss, RS Galen, PV Tadepalli
Artificial Intelligence 45 (1-2), 47-71, 1990
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
Lazy ExplanationBased Learning: A Solution to the Intractable Theory Problem.
P Tadepalli
IJCAI, 694-700, 1989
Event nugget detection with forward-backward recurrent neural networks
R Ghaeini, XZ Fern, L Huang, P Tadepalli
arXiv preprint arXiv:1802.05672, 2018
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
Learning goal-decomposition rules using exercises
C Reddy, P Tadepalli
ICML, 278-286, 1997
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