| Policy-gradient algorithms for partially observable Markov decision processes D Aberdeen The Australian National University, 2003 | 123 | 2003 |
| The learning behind gmail priority inbox D Aberdeen, O Pacovsky, A Slater LCCC: NIPS 2010 Workshop on Learning on Cores, Clusters and Clouds, 97, 2010 | 116 | 2010 |
| Decision-Theoretic Military Operations Planning. D Aberdeen, S Thiébaux, L Zhang ICAPS, 402-412, 2004 | 116 | 2004 |
| Natural actor-critic for road traffic optimisation S Richter, D Aberdeen, J Yu Advances in neural information processing systems 19, 2006 | 112 | 2006 |
| Scaling internal-state policy-gradient methods for POMDPs D Aberdeen, J Baxter MACHINE LEARNING-INTERNATIONAL WORKSHOP THEN CONFERENCE-, 3-10, 2002 | 111 | 2002 |
| A (revised) survey of approximate methods for solving partially observable Markov decision processes D Aberdeen Technical report, National ICT Australia, 2003 | 103 | 2003 |
| The factored policy-gradient planner O Buffet, D Aberdeen Artificial Intelligence 173 (5-6), 722-747, 2009 | 72 | 2009 |
| Prottle: A probabilistic temporal planner I Little, D Aberdeen, S Thiébaux AAAI Press, 2005 | 71 | 2005 |
| A survey of approximate methods for solving partially observable Markov decision process D Aberdeen Report National ICT Australia, 2003 | 53 | 2003 |
| Fast online policy gradient learning with SMD gain vector adaptation J Yu, D Aberdeen, N Schraudolph Advances in neural information processing systems 18, 2005 | 45 | 2005 |
| Emmerald: a fast matrix–matrix multiply using Intel's SSE instructions D Aberdeen, J Baxter Concurrency and Computation: Practice and Experience 13 (2), 103-119, 2001 | 44 | 2001 |
| 92¢/mflops/s, ultra-large-scale neural-network training on a piii cluster D Aberdeen, J Baxter, R Edwards SC'00: Proceedings of the 2000 ACM/IEEE Conference on Supercomputing, 44-44, 2000 | 39 | 2000 |
| Conditional random fields for multi-agent reinforcement learning X Zhang, D Aberdeen, SVN Vishwanathan Proceedings of the 24th international conference on Machine learning, 1143-1150, 2007 | 31 | 2007 |
| Robust planning with (l) rtdp O Buffet, D Aberdeen INTERNATIONAL JOINT CONFERENCE ON ARTIFICIAL INTELLIGENCE 19, 1214, 2005 | 31 | 2005 |
| Presentation of messages in multi-sectioned views KJ Coleman, AW Moedinger, DJ Lauterbach, JB Cornwell, PM McDonald, ... US Patent 8,793,591, 2014 | 27 | 2014 |
| FF+ FPG: Guiding a Policy-Gradient Planner. O Buffet, D Aberdeen ICAPS, 42-48, 2007 | 27 | 2007 |
| Policy-gradients for PSRs and POMDPs D Aberdeen, O Buffet, O Thomas Artificial intelligence and statistics, 3-10, 2007 | 27 | 2007 |
| Sorted inbox with important message identification based on global and user models D Aberdeen, DS De Kloet, T Pietraszek, A Chen US Patent 8,700,545, 2014 | 24 | 2014 |
| Policy-gradient methods for planning D Aberdeen Advances in Neural Information Processing Systems 18, 2005 | 21 | 2005 |
| The factored policy gradient planner (ipc-06 version) O Buffet, D Aberdeen ICAPS 2006, 69, 2006 | 20 | 2006 |
Olivier Buffetresearcher, INRIA, Nancy在 loria.fr 的电子邮件经过验证
