Jay I. Myung

Publication Activity (10 Years)

Years Active: 2005-2020
Publications (10 Years): 8

Top Topics

Active Learning

Dynamic Programming

Experimental Designs

Computational Model

Top Venues

Publications

Sang Ho Lee, Dan Kim, John Opfer, Mark A. Pitt, Jay I. Myung
Machine Learning Optimizes Assessment: New Insights for the Development of Numerosity Estimation. CogSci (2020)
Ran Zhou, Jay I. Myung, Mark A. Pitt
The Scaled Target Learning Model: A Novel Computational Model of the Balloon Analogue Risk Task. CogSci (2020)
Sang Ho Lee, Dan Kim, John Opfer, Mark A. Pitt, Jay I. Myung
Active Learning for a Number-Line Task with Two Design Variables. CogSci (2019)
Jay I. Myung, Mark A. Pitt, Jaeyeong Yang, Woo-Young Ahn
Optimizing the Design of an Experiment using the ADOpy Package: An Introduction and Tutorial. CogSci (2019)
Jorge Chang, Jiseob Kim, Byoung-Tak Zhang, Mark A. Pitt, Jay I. Myung
Modeling Delay Discounting using Gaussian Process with Active Learning. CogSci (2019)
Ran Zhou, Jay I. Myung, Carol Mathews, Mark A. Pitt
Assessing the Validity of Three Tasks of Risk-Taking Propensity: Behavioral Measure and Computational Modeling. CogSci (2018)
Woojae Kim, Mark A. Pitt, Zhong-Lin Lu, Jay I. Myung
Planning Beyond the Next Trial in Adaptive Experiments: A Dynamic Programming Approach. Cogn. Sci. 41 (8) (2017)
Jay I. Myung, Mark A. Pitt, Maarten Speekenbrink
Workshop on Optimizing Experimental Designs: Theory, Practice, and Applications. CogSci (2015)
Woojae Kim, Mark A. Pitt, Zhong-Lin Lu, Mark Steyvers, Jay I. Myung
A Hierarchical Adaptive Approach to Optimal Experimental Design. Neural Comput. 26 (11) (2014)
Woojae Kim, Mark A. Pitt, Zhong-Lin Lu, Mark Steyvers, Hairong Gu, Jay I. Myung
A Hierarchical Adaptive Approach to the Optimal Design of Experiments. CogSci (2014)
Hairong Gu, Jay I. Myung, Mark A. Pitt, Zhong-Lin Lu
Adaptive Estimation of Psychometric Slope and Threshold with Differential Evolution. CogSci (2013)
Daniel R. Cavagnaro, Richard Gonzalez, Jay I. Myung, Mark A. Pitt
Optimal Decision Stimuli for Risky Choice Experiments: An Adaptive Approach. Manag. Sci. 59 (2) (2013)
Jay I. Myung, Mark A. Pitt
Tutorial on Model Comparison Methods. CogSci (2011)
Daniel R. Cavagnaro, Jay I. Myung, Mark A. Pitt, Janne V. Kujala
Adaptive Design Optimization: A Mutual Information-Based Approach to Model Discrimination in Cognitive Science. Neural Comput. 22 (4) (2010)
Daniel R. Cavagnaro, Mark A. Pitt, Jay I. Myung
Adaptive Design Optimization in Experiments with People. NIPS (2009)
Mark A. Pitt, Jay I. Myung, Maximiliano Montenegro, James Pooley
Measuring Model Flexibility With Parameter Space Partitioning: An Introduction and Application Example. Cogn. Sci. 32 (8) (2008)
Jay I. Myung, Mark A. Pitt, Daniel J. Navarro
Model selection in cognitive science as an inverse problem. Computational Imaging (2005)