| 2020 | CogSci | Machine Learning Optimizes Assessment: New Insights for the Development of Numerosity Estimation. | Sang Ho Lee, Dan Kim, John Opfer, Mark A. Pitt, Jay I. Myung |
| 2020 | CogSci | The Scaled Target Learning Model: A Novel Computational Model of the Balloon Analogue Risk Task. | Ran Zhou, Jay I. Myung, Mark A. Pitt |
| 2019 | CogSci | Modeling Delay Discounting using Gaussian Process with Active Learning. | Jorge Chang, Jiseob Kim, Byoung-Tak Zhang, Mark A. Pitt, Jay I. Myung |
| 2019 | CogSci | Active Learning for a Number-Line Task with Two Design Variables. | Sang Ho Lee, Dan Kim, John Opfer, Mark A. Pitt, Jay I. Myung |
| 2019 | CogSci | Optimizing the Design of an Experiment using the ADOpy Package: An Introduction and Tutorial. | Jay I. Myung, Mark A. Pitt, Jaeyeong Yang, Woo-Young Ahn |
| 2018 | CogSci | Assessing the Validity of Three Tasks of Risk-Taking Propensity: Behavioral Measure and Computational Modeling. | Ran Zhou, Jay I. Myung, Carol Mathews, Mark A. Pitt |
| 2015 | CogSci | Workshop on Optimizing Experimental Designs: Theory, Practice, and Applications. | Jay I. Myung, Mark A. Pitt, Maarten Speekenbrink |
| 2014 | CogSci | A Hierarchical Adaptive Approach to the Optimal Design of Experiments. | Woojae Kim, Mark A. Pitt, Zhong-Lin Lu, Mark Steyvers, Hairong Gu, Jay I. Myung |
| 2013 | CogSci | Adaptive Estimation of Psychometric Slope and Threshold with Differential Evolution. | Hairong Gu, Jay I. Myung, Mark A. Pitt, Zhong-Lin Lu |
| 2011 | CogSci | Tutorial on Model Comparison Methods. | Jay I. Myung, Mark A. Pitt |