| 2025 | UAI | Statistical Significance of Feature Importance Rankings. | Jeremy Goldwasser, Giles Hooker |
| 2025 | UAI | Targeted Learning for Variable Importance. | Xiaohan Wang, Yunzhe Zhou, Giles Hooker |
| 2021 | KDD | S-LIME: Stabilized-LIME for Model Explanation. | Zhengze Zhou, Giles Hooker, Fei Wang |
| 2020 | AISTATS | Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models. | Benjamin J. Lengerich, Sarah Tan, Chun-Hao Chang, Giles Hooker, Rich Caruana |
| 2018 | AIES | Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation. | Sarah Tan, Rich Caruana, Giles Hooker, Yin Lou |
| 2017 | ICPRAM | Random Projections with Control Variates. | Keegan Kang, Giles Hooker |
| 2017 | ICPRAM | Control Variates as a Variance Reduction Technique for Random Projections. | Keegan Kang, Giles Hooker |
| 2016 | CISS | Improving the recovery of principal components with semi-deterministic random projections. | Keegan Kang, Giles Hooker |
| 2013 | KDD | Accurate intelligible models with pairwise interactions. | Yin Lou, Rich Caruana, Johannes Gehrke, Giles Hooker |
| 2004 | KDD | Diagnosing extrapolation: tree-based density estimation. | Giles Hooker |
| 2004 | KDD | Discovering additive structure in black box functions. | Giles Hooker |