Diagnosing extrapolation: tree-based density estimation. - Giles Hooker (KDD, 2004)

Discovering additive structure in black box functions. - Giles Hooker (KDD, 2004)

Learned-loss boosting. - Giles Hooker; James O. Ramsay (Comput. Stat. Data Anal., 2012)

Prediction-based regularization using data augmented regression. - Giles Hooker; Saharon Rosset (Stat. Comput., 2012)

Accurate intelligible models with pairwise interactions. - Yin Lou; Rich Caruana; Johannes Gehrke; Giles Hooker (KDD, 2013)

Control Theory and Experimental Design in Diffusion Processes. - Giles Hooker; Kevin K. Lin; Bruce Rogers (SIAM/ASA J. Uncertain. Quantification, 2015)

Restricted likelihood ratio tests for linearity in scalar-on-function regression. - Mathew W. McLean; Giles Hooker; David Ruppert (Stat. Comput., 2015)

Improving the recovery of principal components with semi-deterministic random projections. - Keegan Kang; Giles Hooker (CISS, 2016)

Tree Space Prototypes: Another Look at Making Tree Ensembles Interpretable. - Hui Fen Tan; Giles Hooker; Martin T. Wells (CoRR, 2016)

Quantifying Uncertainty in Random Forests via Confidence Intervals and Hypothesis Tests. - Lucas Mentch; Giles Hooker (J. Mach. Learn. Res., 2016)

Maximal autocorrelation functions in functional data analysis. - Giles Hooker; Steven Roberts (Stat. Comput., 2016)

Random Projections with Control Variates. - Keegan Kang; Giles Hooker (ICPRAM, 2017)

Control Variates as a Variance Reduction Technique for Random Projections. - Keegan Kang; Giles Hooker (ICPRAM (Revised Selected Papers), 2017)

Machine Learning and the Future of Realism. - Giles Hooker; Cliff Hooker (CoRR, 2017)

Detecting Bias in Black-Box Models Using Transparent Model Distillation. - Sarah Tan; Rich Caruana; Giles Hooker; Yin Lou (CoRR, 2017)

Functional principal component analysis of spatially correlated data. - Chong Liu; Surajit Ray; Giles Hooker (Stat. Comput., 2017)

Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation. - Sarah Tan; Rich Caruana; Giles Hooker; Yin Lou (AIES, 2018)

Transparent Model Distillation. - Sarah Tan; Rich Caruana; Giles Hooker; Albert Gordo (CoRR, 2018)

Approximation Trees: Statistical Stability in Model Distillation. - Yichen Zhou; Zhengze Zhou; Giles Hooker (CoRR, 2018)

Asymptotic Properties for Methods Combining the Minimum Hellinger Distance Estimate and the Bayesian Nonparametric Density Estimate. - Yuefeng Wu; Giles Hooker (Entropy, 2018)

Experimental Design for Partially Observed Markov Decision Processes. - Leifur Thorbergsson; Giles Hooker (SIAM/ASA J. Uncertain. Quantification, 2018)

Bootstrap bias corrections for ensemble methods. - Giles Hooker; Lucas Mentch (Stat. Comput., 2018)

Please Stop Permuting Features: An Explanation and Alternatives. - Giles Hooker; Lucas Mentch (CoRR, 2019)

Asymptotic Normality and Variance Estimation For Supervised Ensembles. - Zhengze Zhou; Lucas Mentch; Giles Hooker (CoRR, 2019)

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 (AISTATS, 2020)

Tree Space Prototypes: Another Look at Making Tree Ensembles Interpretable. - Sarah Tan; Matvey Soloviev; Giles Hooker; Martin T. Wells (FODS, 2020)

Timing observations of diffusions. - Aurya Javeed; Giles Hooker (Stat. Comput., 2020)

S-LIME: Stabilized-LIME for Model Explanation. - Zhengze Zhou; Giles Hooker; Fei Wang 001 (KDD, 2021)

Bridging Breiman's Brook: From Algorithmic Modeling to Statistical Learning. - Lucas Mentch; Giles Hooker (CoRR, 2021)

Boosting Random Forests to Reduce Bias; One-Step Boosted Forest and Its Variance Estimate. - Indrayudh Ghosal; Giles Hooker (J. Comput. Graph. Stat., 2021)

V-statistics and Variance Estimation. - Zhengze Zhou; Lucas Mentch; Giles Hooker (J. Mach. Learn. Res., 2021)

Unrestricted permutation forces extrapolation: variable importance requires at least one more model, or there is no free variable importance. - Giles Hooker; Lucas Mentch; Siyu Zhou (Stat. Comput., 2021)

Unbiased Measurement of Feature Importance in Tree-Based Methods. - Zhengze Zhou; Giles Hooker (ACM Trans. Knowl. Discov. Data, 2021)

The Infinitesimal Jackknife and Combinations of Models. - Indrayudh Ghosal; Yunzhe Zhou; Giles Hooker (CoRR, 2022)

A Generic Approach for Statistical Stability in Model Distillation. - Yunzhe Zhou; Peiru Xu; Giles Hooker (CoRR, 2022)

Decision tree boosted varying coefficient models. - Yichen Zhou; Giles Hooker (Data Min. Knowl. Discov., 2022)

Boulevard: Regularized Stochastic Gradient Boosted Trees and Their Limiting Distribution. - Yichen Zhou; Giles Hooker (J. Mach. Learn. Res., 2022)

Selecting the derivative of a functional covariate in scalar-on-function regression. - Giles Hooker; Han Lin Shang (Stat. Comput., 2022)

Considerations when learning additive explanations for black-box models. - Sarah Tan; Giles Hooker; Paul Koch; Albert Gordo; Rich Caruana (Mach. Learn., 2023)

Using Longitudinal Data for Plausible Counterfactual Explanations. - Alexander Asemota; Giles Hooker (HI-AI@KDD, 2024)

Stabilizing Estimates of Shapley Values with Control Variates. - Jeremy Goldwasser; Giles Hooker (xAI (2), 2024)

Provably Stable Feature Rankings with SHAP and LIME. - Jeremy Goldwasser; Giles Hooker (CoRR, 2024)

Longitudinal Counterfactuals: Constraints and Opportunities. - Alexander Asemota; Giles Hooker (CoRR, 2024)

Why You Should Not Trust Interpretations in Machine Learning: Adversarial Attacks on Partial Dependence Plots. - Xi Xin; Giles Hooker; Fei Huang (CoRR, 2024)

Differentiable Programming for Differential Equations: A Review. - Facundo Sapienza; Jordi Bolibar; Frank Schäfer; Brian Groenke; Avik Pal; Victor Boussange; Patrick Heimbach; Giles Hooker; Fernando Pérez; Per-Olof Persson; Christopher Rackauckas (CoRR, 2024)

Approximation trees: statistical reproducibility in model distillation. - Yichen Zhou; Zhengze Zhou; Giles Hooker (Data Min. Knowl. Discov., 2024)

A generic approach for reproducible model distillation. - Yunzhe Zhou; Peiru Xu; Giles Hooker (Mach. Learn., 2024)

Statistical Significance of Feature Importance Rankings. - Jeremy Goldwasser; Giles Hooker (UAI, 2025)

Targeted Learning for Variable Importance. - Xiaohan Wang; Yunzhe Zhou; Giles Hooker (UAI, 2025)

Targeted Learning for Data Fairness. - Alexander Asemota; Giles Hooker (CoRR, 2025)

Unifying Image Counterfactuals and Feature Attributions with Latent-Space Adversarial Attacks. - Jeremy Goldwasser; Giles Hooker (CoRR, 2025)