Protecting against evaluation overfitting in empirical reinforcement learning
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Shimon Whiteson | Peter Stone | Matthew E. Taylor | Brian Tanner | S. Whiteson | P. Stone | B. Tanner | Shimon Whiteson
[1] A. Mood,et al. The statistical sign test. , 1946, Journal of the American Statistical Association.
[2] R. Bellman. A Markovian Decision Process , 1957 .
[3] Adele E. Howe,et al. How evaluation guides AI research , 1988 .
[4] Fred S. Roberts,et al. Chapter 18 Limitations on conclusions using scales of measurement , 1994, Operations research and the public sector.
[5] Herbert A. Simon,et al. Artificial Intelligence: An Empirical Science , 1995, Artif. Intell..
[6] Richard S. Sutton,et al. Generalization in Reinforcement Learning: Successful Examples Using Sparse Coarse Coding , 1995, NIPS.
[7] John N. Hooker,et al. Testing heuristics: We have it all wrong , 1995, J. Heuristics.
[8] D. Saari,et al. The Copeland method , 1996 .
[9] San Cristóbal Mateo,et al. The Lack of A Priori Distinctions Between Learning Algorithms , 1996 .
[10] Emanuel Falkenauer,et al. On Method Overfitting , 1998, J. Heuristics.
[11] C. Gallistel. The Replacement of General-Purpose Learning Models with Adaptively Specialized Learning Modules , 2000 .
[12] Jonathan Baxter,et al. A Model of Inductive Bias Learning , 2000, J. Artif. Intell. Res..
[13] Tom Fawcett,et al. Robust Classification for Imprecise Environments , 2000, Machine Learning.
[14] Pat Langley,et al. Machine learning as an experimental science , 2004, Machine Learning.
[15] Steven Salzberg,et al. On Comparing Classifiers: Pitfalls to Avoid and a Recommended Approach , 1997, Data Mining and Knowledge Discovery.
[16] Geoffrey I. Webb. MultiBoosting: A Technique for Combining Boosting and Wagging , 2000, Machine Learning.
[17] David A. Cohn,et al. Improving generalization with active learning , 1994, Machine Learning.
[18] W. Smart,et al. Why (PO)MDPs Lose for Spatial Tasks and What to Do About It , 2005 .
[19] Andrew G. Barto,et al. Autonomous shaping: knowledge transfer in reinforcement learning , 2006, ICML.
[20] Jesse Hoey,et al. An analytic solution to discrete Bayesian reinforcement learning , 2006, ICML.
[21] Janez Demsar,et al. Statistical Comparisons of Classifiers over Multiple Data Sets , 2006, J. Mach. Learn. Res..
[22] Cordelia Schmid,et al. Dataset Issues in Object Recognition , 2006, Toward Category-Level Object Recognition.
[23] Alan Fern,et al. Multi-task reinforcement learning: a hierarchical Bayesian approach , 2007, ICML '07.
[24] Peter Stone,et al. Transfer Learning for Reinforcement Learning Domains: A Survey , 2009, J. Mach. Learn. Res..
[25] Brian Tanner,et al. RL-Glue: Language-Independent Software for Reinforcement-Learning Experiments , 2009, J. Mach. Learn. Res..
[26] Yehuda Koren,et al. The BellKor Solution to the Netflix Grand Prize , 2009 .
[27] Shimon Whiteson,et al. Neuroevolutionary reinforcement learning for generalized helicopter control , 2009, GECCO.
[28] Ronald E. Parr,et al. A Novel Benchmark Methodology and Data Repository for Real-life Reinforcement Learning , 2009 .
[29] Shimon Whiteson,et al. The Reinforcement Learning Competitions , 2010 .
[30] Shimon Whiteson,et al. Multi-task evolutionary shaping without pre-specified representations , 2010, GECCO '10.
[31] Nathalie Japkowicz,et al. Warning: statistical benchmarking is addictive. Kicking the habit in machine learning , 2010, J. Exp. Theor. Artif. Intell..
[32] Ahmed Syed Irshad,et al. Markov Decision Process , 2011 .