| Beyond equilibrium: Predicting human behavior in normal-form games JR Wright, K Leyton-Brown Twenty-Fourth AAAI Conference on Artificial Intelligence, 2010 | 184 | 2010 |
| Deep learning for predicting human strategic behavior JS Hartford, JR Wright, K Leyton-Brown Advances in neural information processing systems 29, 2016 | 78 | 2016 |
| Level-0 Meta-Models for Predicting Human Behavior in Games JR Wright, K Leyton-Brown Proceedings of the Fifteenth ACM Conference on Economics and Computation …, 2014 | 59 | 2014 |
| Incentivizing evaluation with peer prediction and limited access to ground truth XA Gao, JR Wright, K Leyton-Brown Artificial Intelligence 275, 618-638, 2019 | 48* | 2019 |
| Mechanical TA: Partially Automated High-Stakes Peer Grading JR Wright, C Thornton, K Leyton-Brown ACM Technical Symposium on Computer Science Education (ACM-SIGCSE), 2015 | 48 | 2015 |
| Behavioral game theoretic models: a Bayesian framework for parameter analysis. JR Wright, K Leyton-Brown AAMAS, 921-930, 2012 | 46 | 2012 |
| Predicting Human Behavior in Unrepeated, Simultaneous-Move Games JR Wright, K Leyton-Brown Games and Economic Behavior 106, 16-37, 2017 | 41 | 2017 |
| How can machine learning aid behavioral marketing research? L Hagen, K Uetake, N Yang, B Bollinger, AJB Chaney, D Dzyabura, ... Marketing Letters 31 (4), 361-370, 2020 | 26 | 2020 |
| Why do software developers use static analysis tools? a user-centered study of developer needs and motivations LNQ Do, J Wright, K Ali IEEE Transactions on Software Engineering, 2020 | 20 | 2020 |
| Learning when to stop searching DG Goldstein, RP McAfee, S Suri, JR Wright Management Science 66 (3), 1375-1394, 2020 | 17* | 2020 |
| Hindsight and sequential rationality of correlated play D Morrill, R D'Orazio, R Sarfati, M Lanctot, JR Wright, A Greenwald, ... arXiv preprint arXiv:2012.05874, 2020 | 12 | 2020 |
| Level-0 Models for Predicting Human Behavior in Games JR Wright, K Leyton-Brown Journal of Artificial Intelligence Research 64, 357-383, 2019 | 10 | 2019 |
| Efficient deviation types and learning for hindsight rationality in extensive-form games D Morrill, R D’Orazio, M Lanctot, JR Wright, M Bowling, AR Greenwald International Conference on Machine Learning, 7818-7828, 2021 | 7 | 2021 |
| A formal separation between strategic and nonstrategic behavior JR Wright, K Leyton-Brown Proceedings of the 21st ACM Conference on Economics and Computation, 535-536, 2020 | 4* | 2020 |
| Alternative Function Approximation Parameterizations for Solving Games: An Analysis of -Regression Counterfactual Regret Minimization R D'Orazio, D Morrill, JR Wright, M Bowling arXiv preprint arXiv:1912.02967, 2019 | 4 | 2019 |
| The spotlight: A general method for discovering systematic errors in deep learning models G d'Eon, J d'Eon, JR Wright, K Leyton-Brown arXiv preprint arXiv:2107.00758, 2021 | 3 | 2021 |
| Modeling human behavior in strategic settings JR Wright University of British Columbia, 2016 | 2 | 2016 |
| Evaluating, Understanding, and Improving Behavioral Game Theory Models For Predicting Human Behavior in Unrepeated Normal-Form Games JR Wright, K Leyton-Brown arXiv preprint arXiv:1306.0918, 2013 | 2 | 2013 |
| Game Theoretic Malware Detection R MacQueen, N Bombardieri, JR Wright, K Ali arXiv preprint arXiv:2012.00817, 2020 | 1 | 2020 |
| Bounds for approximate regret-matching algorithms R D'Orazio, D Morrill, JR Wright arXiv preprint arXiv:1910.01706, 2019 | 1 | 2019 |
Kevin Leyton-BrownProfessor, Computer Science, University of British Columbia; Canada CIFAR AI ChairVerified email at cs.ubc.ca
