Generalization guides human exploration in vast decision spaces

[1]  Horatio M. Morgan,et al.  An Integrative Framework , 2019, Underdog Entrepreneurs.

[2]  Björn Meder,et al.  Connecting conceptual and spatial search via a model of generalization , 2018, bioRxiv.

[3]  E. Wagenmakers,et al.  Bayesian Latent-Normal Inference for the Rank Sum Test, the Signed Rank Test, and Spearman's $\rho$ , 2017, 1712.06941.

[4]  Andreas Krause,et al.  A tutorial on Gaussian process regression: Modelling, exploring, and exploiting functions , 2016, bioRxiv.

[5]  Kimberly L. Stachenfeld,et al.  The hippocampus as a predictive map , 2017, Nature Neuroscience.

[6]  E. Koechlin,et al.  The Importance of Falsification in Computational Cognitive Modeling , 2017, Trends in Cognitive Sciences.

[7]  N. Daw,et al.  Reinforcement Learning and Episodic Memory in Humans and Animals: An Integrative Framework , 2017, Annual review of psychology.

[8]  Samuel J. Gershman,et al.  Compositional inductive biases in function learning , 2016, Cognitive Psychology.

[9]  Stefano Palminteri,et al.  Confirmation bias in human reinforcement learning: Evidence from counterfactual feedback processing , 2016, PLoS Comput. Biol..

[10]  M. Speekenbrink,et al.  Putting bandits into context: How function learning supports decision making , 2016, bioRxiv.

[11]  Thomas L. Griffiths,et al.  Formalizing Neurath’s Ship: Approximate Algorithms for Online Causal Learning , 2016, Psychological review.

[12]  Timothy E. J. Behrens,et al.  Organizing conceptual knowledge in humans with a gridlike code , 2016, Science.

[13]  Joshua B. Tenenbaum,et al.  Building machines that learn and think like people , 2016, Behavioral and Brain Sciences.

[14]  Jan Hendrik Metzen,et al.  Minimum Regret Search for Single- and Multi-Task Optimization , 2016, ICML.

[15]  Demis Hassabis,et al.  Mastering the game of Go with deep neural networks and tree search , 2016, Nature.

[16]  Joshua B. Tenenbaum,et al.  Assessing the Perceived Predictability of Functions , 2015, CogSci.

[17]  M. Botvinick,et al.  Evidence integration in model-based tree search , 2015, Proceedings of the National Academy of Sciences.

[18]  Peter M. Todd,et al.  A Game of Hide and Seek: Expectations of Clumpy Resources Influence Hiding and Searching Patterns , 2015, PloS one.

[19]  Alkis Gotovos,et al.  Safe Exploration for Optimization with Gaussian Processes , 2015, ICML.

[20]  Vaibhav Srivastava,et al.  Correlated Multiarmed Bandit Problem: Bayesian Algorithms and Regret Analysis , 2015, ArXiv.

[21]  Maarten Speekenbrink,et al.  Human behavior in contextual multi-armed bandit problems , 2015, CogSci.

[22]  Maarten Speekenbrink,et al.  Uncertainty and Exploration in a Restless Bandit Problem , 2015, Top. Cogn. Sci..

[23]  Christopher G. Lucas,et al.  A rational model of function learning , 2015, Psychonomic bulletin & review.

[24]  Shane Legg,et al.  Human-level control through deep reinforcement learning , 2015, Nature.

[25]  Bernhard Schölkopf,et al.  Artificial intelligence: Learning to see and act , 2015, Nature.

[26]  Peter Dayan,et al.  Interplay of approximate planning strategies , 2015, Proceedings of the National Academy of Sciences.

[27]  Joshua T. Abbott,et al.  Random walks on semantic networks can resemble optimal foraging. , 2015, Psychological review.

[28]  Carl E. Rasmussen,et al.  Gaussian Processes for Data-Efficient Learning in Robotics and Control , 2015, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[29]  Jonathan D. Cohen,et al.  Humans use directed and random exploration to solve the explore-exploit dilemma. , 2014, Journal of experimental psychology. General.

[30]  Vincenzo Crupi,et al.  State of the field: Measuring information and confirmation , 2014 .

[31]  Antoine Cully,et al.  Robots that can adapt like animals , 2014, Nature.

[32]  Ali Borji,et al.  Bayesian optimization explains human active search , 2013, NIPS.

[33]  Peter Dayan,et al.  Scalable and Efficient Bayes-Adaptive Reinforcement Learning Based on Monte-Carlo Tree Search , 2013, J. Artif. Intell. Res..

[34]  Alkis Gotovos,et al.  Active Learning for Level Set Estimation , 2022 .

[35]  Vaibhav Srivastava,et al.  Modeling Human Decision Making in Generalized Gaussian Multiarmed Bandits , 2013, Proceedings of the IEEE.

[36]  Shinsuke Shimojo,et al.  Neural Computations Underlying Arbitration between Model-Based and Model-free Learning , 2013, Neuron.

[37]  Thomas T. Hills,et al.  Cognitive search : evolution, algorithms, and the brain , 2012 .

[38]  Timothy E. J. Behrens,et al.  Neural Mechanisms of Foraging , 2012, Science.

[39]  Aurélien Garivier,et al.  On Bayesian Upper Confidence Bounds for Bandit Problems , 2012, AISTATS.

[40]  Thomas T. Hills,et al.  Optimal foraging in semantic memory. , 2012, Psychological review.

[41]  Nikolaos Scarmeas,et al.  The good, bad, and ugly? , 2012, Neurology.

[42]  Steven Reece,et al.  An introduction to Gaussian processes for the Kalman filter expert , 2010, 2010 13th International Conference on Information Fusion.

[43]  Andreas Krause,et al.  Information-Theoretic Regret Bounds for Gaussian Process Optimization in the Bandit Setting , 2009, IEEE Transactions on Information Theory.

[44]  Karl J. Friston,et al.  Bayesian model selection for group studies , 2009, NeuroImage.

[45]  M. Lee,et al.  A Bayesian analysis of human decision-making on bandit problems , 2009 .

[46]  Jeffrey N. Rouder,et al.  Bayesian t tests for accepting and rejecting the null hypothesis , 2009, Psychonomic bulletin & review.

[47]  P. Dayan,et al.  Reinforcement learning: The Good, The Bad and The Ugly , 2008, Current Opinion in Neurobiology.

[48]  P. Dayan,et al.  Cortical substrates for exploratory decisions in humans , 2006, Nature.

[49]  Christopher K. I. Williams,et al.  Gaussian Processes for Machine Learning (Adaptive Computation and Machine Learning) , 2005 .

[50]  Jonathan D. Nelson.  Finding useful questions: on Bayesian diagnosticity, probability, impact, and information gain. , 2005, Psychological review.

[51]  Carl E. Rasmussen,et al.  Gaussian Processes in Reinforcement Learning , 2003, NIPS.

[52]  Peter Auer,et al.  Using Confidence Bounds for Exploitation-Exploration Trade-offs , 2003, J. Mach. Learn. Res..

[53]  I. J. Myung,et al.  Toward an explanation of the power law artifact: Insights from response surface analysis , 2000, Memory & cognition.

[54]  Richard S. Sutton,et al.  Generalization in ReinforcementLearning : Successful Examples UsingSparse Coarse , 1996 .

[55]  Gerald Tesauro,et al.  Practical issues in temporal difference learning , 1992, Machine Learning.

[56]  R. Shepard,et al.  Toward a universal law of generalization for psychological science. , 1987, Science.

[57]  D. Lindley.  On a Measure of the Information Provided by an Experiment , 1956 .

[58]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[59]  Richard S. Sutton,et al.  Reinforcement Learning: An Introduction , 1998, IEEE Trans. Neural Networks.

[60]  J. Mockus,et al.  The Bayesian approach to global optimization , 1989 .