| 2018 | IPIN | A Bayesian Approach to Dealing with Device Heterogeneity in an Indoor Positioning System. | Kyle F. Davies, Ian G. Jones, Jonathan L. Shapiro |
| 2015 | FOGA | Convergence of Strategies in Simple Co-Adapting Games. | Richard Mealing, Jonathan L. Shapiro |
| 2013 | ICAISC | Opponent Modelling by Sequence Prediction and Lookahead in Two-Player Games. | Richard Mealing, Jonathan L. Shapiro |
| 2009 | FOGA | Stability of learning dynamics in two-agent, imperfect-information games. | John Michael Butterworth, Jonathan L. Shapiro |
| 2007 | GECCO | Addressing sampling errors and diversity loss in UMDA. | Jrgen Branke, Clemens Lode, Jonathan L. Shapiro |
| 2007 | GECCO | Parameter cross-validation and early-stopping in univariate marginal distribution algorithm. | Hao Wu, Jonathan L. Shapiro |
| 2006 | GECCO | Does overfitting affect performance in estimation of distribution algorithms. | Hao Wu, Jonathan L. Shapiro |
| 2006 | PPSN | Model Complexity vs. Performance in the Bayesian Optimization Algorithm. | Elon Santos Correa, Jonathan L. Shapiro |
| 2006 | PPSN | Diversity Loss in General Estimation of Distribution Algorithms. | Jonathan L. Shapiro |
| 2005 | ICANN | A Spiking Neural Sparse Distributed Memory Implementation for Learning and Predicting Temporal Sequences. | Joy Bose, Stephen B. Furber, Jonathan L. Shapiro |
| 2005 | IJCNN | An associative memory for the on-line recognition and prediction of temporal sequences. | Joy Bose, Steve B. Furber, Jonathan L. Shapiro |
| 2002 | FOGA | The Sensitivity of PBIL to Its Learning Rate, and How Detailed Balance Can Remove It. | Jonathan L. Shapiro |
| 1998 | PPSN | Does Data-Model Co-evolution Improve Generalization Performance of Evolving Learners? | Jonathan L. Shapiro |