Year |
Citation |
Score |
2022 |
Matsuo Y, LeCun Y, Sahani M, Precup D, Silver D, Sugiyama M, Uchibe E, Morimoto J. Deep learning, reinforcement learning, and world models. Neural Networks : the Official Journal of the International Neural Network Society. 152: 267-275. PMID 35569196 DOI: 10.1016/j.neunet.2022.03.037 |
0.667 |
|
2020 |
Barreto A, Hou S, Borsa D, Silver D, Precup D. Fast reinforcement learning with generalized policy updates. Proceedings of the National Academy of Sciences of the United States of America. PMID 32817541 DOI: 10.1073/Pnas.1907370117 |
0.677 |
|
2020 |
Wu D, Wang B, Precup D, Boulet B. Multiple Kernel Learning-Based Transfer Regression for Electric Load Forecasting Ieee Transactions On Smart Grid. 11: 1183-1192. DOI: 10.1109/Tsg.2019.2933413 |
0.502 |
|
2019 |
Khetarpal K, Precup D. Learning Options with Interest Functions Proceedings of the Aaai Conference On Artificial Intelligence. 33: 9955-9956. DOI: 10.1609/aaai.v33i01.33019955 |
0.489 |
|
2019 |
Lupu A, Durand A, Precup D. Leveraging Observations in Bandits: Between Risks and Benefits Proceedings of the Aaai Conference On Artificial Intelligence. 33: 6112-6119. DOI: 10.1609/AAAI.V33I01.33016112 |
0.348 |
|
2019 |
Francois-Lavet V, Bengio Y, Precup D, Pineau J. Combined Reinforcement Learning via Abstract Representations Proceedings of the Aaai Conference On Artificial Intelligence. 33: 3582-3589. DOI: 10.1609/aaai.v33i01.33013582 |
0.423 |
|
2018 |
Bacon P, Precup D. Constructing Temporal Abstractions Autonomously in Reinforcement Learning Ai Magazine. 39: 39-50. DOI: 10.1609/Aimag.V39I1.2780 |
0.558 |
|
2015 |
Jafarpour N, Izadi M, Precup D, Buckeridge DL. Quantifying the determinants of outbreak detection performance through simulation and machine learning. Journal of Biomedical Informatics. 53: 180-7. PMID 25445482 DOI: 10.1016/J.Jbi.2014.10.009 |
0.341 |
|
2015 |
Mann TA, Mannor S, Precup D. Approximate value iteration with temporally extended actions Journal of Artificial Intelligence Research. 53: 375-438. DOI: 10.1613/Jair.4676 |
0.381 |
|
2015 |
Farahmand AM, Precup D, Barreto AMS, Ghavamzadeh M. Classification-based approximate policy iteration Ieee Transactions On Automatic Control. 60: 2989-2993. DOI: 10.1109/Tac.2015.2418411 |
0.384 |
|
2015 |
Bacon PL, Balle B, Precup D. Learning and planning with timing information in Markov decision processes Uncertainty in Artificial Intelligence - Proceedings of the 31st Conference, Uai 2015. 111-120. |
0.352 |
|
2014 |
Barreto AMS, Pineau J, Precup D. Policy iteration based on stochastic factorization Journal of Artificial Intelligence Research. 50: 763-803. DOI: 10.1613/Jair.4301 |
0.301 |
|
2014 |
McCarthy SM, Precup D. Theoretical results on the effect of 'shortcut' actions in MDPs Connection Science. 26: 179-193. DOI: 10.1080/09540091.2014.885304 |
0.306 |
|
2014 |
Bachman P, Alsharif O, Precup D. Learning with pseudo-ensembles Advances in Neural Information Processing Systems. 4: 3365-3373. |
0.478 |
|
2014 |
Sutton RS, Mahmood AR, Precup D, Van Hasselt H. A new Q(λ) with interim forward view and Monte Carlo equivalence 31st International Conference On Machine Learning, Icml 2014. 3: 1973-1988. |
0.357 |
|
2013 |
Frank J, Mannor S, Pineau J, Precup D. Time Series Analysis Using Geometric Template Matching. Ieee Transactions On Pattern Analysis and Machine Intelligence. 35: 740-54. PMID 22641699 DOI: 10.1109/Tpami.2012.121 |
0.332 |
|
2013 |
Gehring C, Precup D. Smart exploration in reinforcement learning using absolute temporal difference errors 12th International Conference On Autonomous Agents and Multiagent Systems 2013, Aamas 2013. 2: 1037-1043. |
0.389 |
|
2013 |
Kim B, Farahmand AM, Pineau J, Precup D. Learning from limited demonstrations Advances in Neural Information Processing Systems. |
0.396 |
|
2012 |
Still S, Precup D. An information-theoretic approach to curiosity-driven reinforcement learning. Theory in Biosciences = Theorie in Den Biowissenschaften. 131: 139-48. PMID 22791268 DOI: 10.1007/S12064-011-0142-Z |
0.43 |
|
2012 |
Agmon N, Agrawal V, Aha DW, Aloimonos Y, Buckley D, Doshi P, Geib C, Grasso F, Green N, Johnston B, Kaliski B, Kiekintveld C, Law E, Lieberman H, Mengshoel OJ, ... ... Precup D, et al. Reports of the AAAI 2011 conference workshops Ai Magazine. 33: 57-70. DOI: 10.1609/Aimag.V33I1.2390 |
0.314 |
|
2012 |
Castro PS, Precup D. Automatic construction of temporally extended actions for MDPs using bisimulation metrics Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 7188: 140-152. DOI: 10.1007/978-3-642-29946-9_16 |
0.371 |
|
2012 |
Barreto AMS, Precup D, Pineau J. On-line reinforcement learning using incremental kernel-based stochastic factorization Advances in Neural Information Processing Systems. 2: 1484-1492. |
0.344 |
|
2012 |
Farahmand AM, Precup D. Value pursuit iteration Advances in Neural Information Processing Systems. 2: 1340-1348. |
0.317 |
|
2012 |
Warrick PA, Hamilton EF, Kearney RE, Precup D. A machine-learning approach to the detection of fetal hypoxia during labor and delivery Ai Magazine. 33: 79-90. |
0.316 |
|
2011 |
Sutton RS, Modayil J, Degris MDT, Pilarski PM, White A, Precup D. Horde: A scalable real-time architecture for learning knowledge from unsupervised sensorimotor interaction 10th International Conference On Autonomous Agents and Multiagent Systems 2011, Aamas 2011. 2: 713-720. |
0.58 |
|
2011 |
Barreto AMS, Precup D, Pineau J. Reinforcement learning using kernel-based stochastic factorization Advances in Neural Information Processing Systems 24: 25th Annual Conference On Neural Information Processing Systems 2011, Nips 2011. |
0.456 |
|
2011 |
Bachman P, Precup D. Learning compact representations of time-varying processes Proceedings of the National Conference On Artificial Intelligence. 2: 1748-1749. |
0.436 |
|
2010 |
Andrews S, Kry P, Precup D. Learning control policies for virtual grasping applications Ceur Workshop Proceedings. 588: 12-14. |
0.363 |
|
2010 |
Warrick PA, Hamilton EF, Kearney RE, Precup D. A machine learning approach to the detection of fetal hypoxia during labor and delivery Proceedings of the National Conference On Artificial Intelligence. 3: 1865-1870. |
0.316 |
|
2010 |
Dinculescu M, Precup D. Approximate predictive representations of partially observable systems Icml 2010 - Proceedings, 27th International Conference On Machine Learning. 895-902. |
0.376 |
|
2010 |
Comanici G, Precup D. Optimal policy switching algorithms for reinforcement learning Proceedings of the International Joint Conference On Autonomous Agents and Multiagent Systems, Aamas. 2: 709-714. |
0.405 |
|
2009 |
Sutton RS, Maei HR, Precup D, Bhatnagar S, Silver D, Szepesvári C, Wiewiora E. Fast gradient-descent methods for temporal-difference learning with linear function approximation Proceedings of the 26th International Conference On Machine Learning, Icml 2009. 993-1000. DOI: 10.1145/1553374.1553501 |
0.725 |
|
2009 |
Zhioua S, Precup D, Laviolette F, Desharnais J. Learning the difference between partially observable dynamical systems Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 5782: 664-677. DOI: 10.1007/978-3-642-04174-7_43 |
0.381 |
|
2009 |
Sutton RS, Maei HR, Precup D, Bhatnagar S, Silver D, Szepesvári C, Wiewiora E. Fast gradient-descent methods for temporal-difference learning with linear function approximation Proceedings of the 26th International Conference On Machine Learning, Icml 2009. 993-1000. |
0.757 |
|
2009 |
Maei HR, Szepesvari C, Bhatnagar S, Precup D, Silver D, Sutton RS. Convergent temporal-difference learning with arbitrary smooth function approximation Advances in Neural Information Processing Systems 22 - Proceedings of the 2009 Conference. 1204-1212. |
0.753 |
|
2008 |
Brooks R, Arbel T, Precup D. Anytime similarity measures for faster alignment Computer Vision and Image Understanding. 110: 378-389. DOI: 10.1016/J.Cviu.2007.09.011 |
0.304 |
|
2008 |
Frank J, Mannor S, Precup D. Reinforcement learning in the presence of rare events Proceedings of the 25th International Conference On Machine Learning. 336-343. |
0.442 |
|
2006 |
Keller PW, Mannor S, Precup D. Automatic basis function construction for approximate dynamic programming and reinforcement learning Acm International Conference Proceeding Series. 148: 449-456. DOI: 10.1145/1143844.1143901 |
0.349 |
|
2006 |
Izadi MT, Precup D, Azar D. Belief selection in point-based planning algorithms for POMDPs Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 4013: 383-394. DOI: 10.1007/11766247_33 |
0.3 |
|
2006 |
Gavaldà R, Keller PW, Pineau J, Precup D. PAC-learning of Markov Models with Hidden State Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 4212: 150-161. |
0.427 |
|
2005 |
Muslea I, Dignum V, Corkill D, Jonker C, Dignum F, Coradeschi S, Saffiotti A, Fu D, Orkin J, Cheetham W, Goebel K, Bonissone P, Soh LK, Jones RM, Wray RE, ... ... Precup D, et al. The workshop program at the Nineteenth National Conference on Artificial Intelligence Ai Magazine. 26: 103-108. DOI: 10.1609/Aimag.V26I1.1806 |
0.373 |
|
2005 |
Precup D, Sutton RS, Paduraru C, Koop A, Singh S. Off-policy learning with options and recognizers Advances in Neural Information Processing Systems. 1097-1104. |
0.583 |
|
2004 |
Ratitch B, Mahadevan S, Precup D. Sparse distributed memories in reinforcement learning: Case studies Aaai Workshop - Technical Report. 85-90. |
0.329 |
|
2003 |
Ratitch B, Precup D. Using MDP characteristics to guide exploration in reinforcement learning Lecture Notes in Artificial Intelligence (Subseries of Lecture Notes in Computer Science). 2837: 313-324. |
0.411 |
|
2003 |
Rivest F, Precup D. Combining TD-learning with Cascade-correlation Networks Proceedings, Twentieth International Conference On Machine Learning. 2: 632-639. |
0.35 |
|
2002 |
LETIA IA, PRECUP D. ERRATUM: DEVELOPING COLLABORATIVE GOLOG AGENTS BY REINFORCEMENT LEARNING International Journal On Artificial Intelligence Tools. 11: 473-473. DOI: 10.1142/S021821300200099X |
0.412 |
|
2002 |
Ratitch B, Precup D. Characterizing Markov decision processes Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2430: 391-404. |
0.393 |
|
2002 |
Stolle M, Precup D. Learning options in reinforcement learning Lecture Notes in Computer Science (Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). 2371: 212-223. |
0.379 |
|
2001 |
Letia IA, Precup D. Developing collaborative Golog agents by reinforcement learning Proceedings of the International Conference On Tools With Artificial Intelligence. 195-202. DOI: 10.1142/S0218213002000873 |
0.397 |
|
1999 |
Sutton RS, Precup D, Singh S. Between MDPs and semi-MDPs: A framework for temporal abstraction in reinforcement learning Artificial Intelligence. 112: 181-211. DOI: 10.1016/S0004-3702(99)00052-1 |
0.668 |
|
1999 |
Sutton RS, Singh S, Precup D, Ravindran B. Improved switching among temporally abstract actions Advances in Neural Information Processing Systems. 1066-1072. |
0.399 |
|
1998 |
Precup D, Sutton RS. Multi-time models for temporally abstract planning Advances in Neural Information Processing Systems. 1050-1056. |
0.423 |
|
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