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Doina Precup


Email: dprecup AT cs DOT mcgill DOT ca
Home Page:
Office: MC111N
Phone: +1-514-398-6443
Fax: +1-514-398-3883

Research Description

Dr. Precup's research interests include: artificial intelligence, machine learning, reinforcement learning, Markov Decision Processes, planning and scheduling, reasoning under uncertainty and applications of machine learning and artificial intelligence.

Most of Dr. Precup's research is in the field of machine learning, with a special focus on reinforcement learning. Reinforcement learning is an increasingly popular approach for learning from interaction with an unknown, complex environment. It can be applied successfully to a wide variety of tasks, from robotic control and the training of web-based agents to solving combinatorial optimization problems. Dr. Precup is working on new knowledge representation methods that facilitate learning and planning for reinforcement learning agents.

Dr. Precup is also more broadly interested in reasoning under uncertainty, and in applications of machine learning techniques to real-world problems.

Research Interests

Research Labs


Selected Publications (click link in front of each publication to see bibtex in ASCII format)

[1] Frank, J., Mannor, S., and Precup, D. Reinforcement learning in the presence of rare events. In McCallum, A., and Roweis, S., editors, Proceedings of the 25th Annual International Conference on Machine Learning (ICML 2008). Omnipress, 2008, pp. 336-343.
[2] Warrick, P. A., Hamilton, E. F., Precup, D., and Kearney, R. E. Detecting the temporal extent of the impulse response function from intra-partum cardiotocography for normal and hypoxic fetuses. In Engineering in Medicine and Biology Society, 2008. EMBS 2008. 30th Annual International Conference of the IEEE. IEEE, 2008, pp. 2797 - 2800.
[3] Izadi, M. T., and Precup, D. Point-based planning for predictive state representations. In Bergler, S., editor, Advances in Artificial Intelligence , 21st Conference of the Canadian Society for Computational Studies of Intelligence, Canadian AI 2008, Windsor, Canada, May 28-30, 2008, Proceedings. Springer, 2008, v. 5032 of Lecture Notes in Computer Science, Lecture Notes in Computer Science, pp. 126-137.
[4] Taylor, J., Precup, D., and Panangaden, P. Bounding performance loss in approximate MDP homomorphisms. In Koller, D., Schuurmans, D., Bengio, Y., and bouttou, L., editors, Advances in Neural Information Processing Systems 21 (Proceedings of NIPS'08). MIT Press, 2008, p. 8 pages (paper ID 09.
[5] Jaulmes, R., Pineau, J., and Precup, D. A formal framework for robot learning and control under model uncertainty. In Proceedings of IEEE International Conference on Robotics and Automation (ICRA), 2007, pp. 2104-2110.
[6] Jaulmes, R., Pineau, J., and Precup, D. Apprentissage actif dans les processus decisionnels de markov partiellement observables. l'algorithme medusa. Revue d'Intelligence Articielle, 2007, v. 27, n. 1, pp. 9-34.
[7] Warrick, P., Precup, D., Hamilton, E., and Kearney, R. Fetal heart rate deceleration detection using a discrete cosine transform implementation of singular spectrum analysis. Ad Hoc Networks Journal, 2007, v. 46, n. 2, pp. 196-201.
[8] Gendron-Bellemare, M., and Precup, D. Context-driven predictions. In Proceedings of IJCAI-07, 20th International Joint Conference on Artificial Intellience, 2007, pp. 250-255.
[9] Castro, P. S., and Precup, D. Using linear programming for bayesian explation in markov decision processes. In Proceedings of IJCAI-07, 20th International Joint Conference on Artificial Intelligence, 2007, pp. 2437-2442.
[10] Brooks, R., and Precup, T. A. D. Fast image alignment using anytime algorithms. In Proceedings of IJCAI-07, 20th International Joint Conference on Artificial Intellience, 2007.
[11] Gavalda, R., Keller, P. W., Pineau, J., and Precup, D. PAC-learning of markov models with hidden state. In Carbonell, J., and Siekmann, J., editors, Machine Learning: ECML 2006. 17th European Conference on Machine Learning Berlin, Germany, September 18-22, 2006 Proceedings. Springer, 2006, v. 4212/2006 of Lecture notes in Computer Science, Lecture notes in Computer Science, pp. 150-161. Acceptance rate 20%.
[12] Hundt, C., Panangaden, P., Pineau, J., and Precup, D. Representing systems with hidden state. In Proceedings of the Twenty-First National Conference on Artificial Intelligence (AAAI'06, Boston, Massachusetts). AAAI Press, 2006. Acceptance rate 30%.
[13] Ferns, N. F., Castro, P. S., Precup, D., and Panangaden, P. Methods for computing state similarity in Markov Decision Processes. 2006. Acceptance rate 20% for plenary talks.

Last Update:   2013/08/05 08:57:34.561 GMT-4