Giles Hooker - RankMe

59 papers A* 4A 3C 2Misc 1Journal 45Unranked 4

Year	Rank	Type	Title / Venue / Authors
2026	J	`jnl`	Statistical Inference for Explainable Boosting Machines. CoRR Haimo Fang, Kevin Tan, Jonathan Pipping, Giles Hooker
2025	J	`jnl`	Statistical Inference for Gradient Boosting Regression. CoRR Haimo Fang, Kevin Tan, Giles Hooker
2025	A	`conf`	Statistical Significance of Feature Importance Rankings. UAI Jeremy Goldwasser, Giles Hooker
2025	J	`jnl`	Targeted Learning for Data Fairness. CoRR Alexander Asemota, Giles Hooker
2025	A	`conf`	Targeted Learning for Variable Importance. UAI Xiaohan Wang, Yunzhe Zhou, Giles Hooker
2025	J	`jnl`	Unifying Image Counterfactuals and Feature Attributions with Latent-Space Adversarial Attacks. CoRR Jeremy Goldwasser, Giles Hooker
2024	J	`jnl`	A generic approach for reproducible model distillation. Mach. Learn. Yunzhe Zhou, Peiru Xu, Giles Hooker
2024	J	`jnl`	Approximation trees: statistical reproducibility in model distillation. Data Min. Knowl. Discov. Yichen Zhou, Zhengze Zhou, Giles Hooker
2024	J	`jnl`	Differentiable Programming for Differential Equations: A Review. CoRR Facundo Sapienza, Jordi Bolibar, Frank Schäfer, Brian Groenke, Avik Pal, Victor Boussange, Patrick Heimbach, Giles Hooker, Fernando Pérez, Per-Olof Persson, Christopher Rackauckas
2024	J	`jnl`	Longitudinal Counterfactuals: Constraints and Opportunities. CoRR Alexander Asemota, Giles Hooker
2024	J	`jnl`	Provably Stable Feature Rankings with SHAP and LIME. CoRR Jeremy Goldwasser, Giles Hooker
2024	—	`conf`	Stabilizing Estimates of Shapley Values with Control Variates. xAI (2) Jeremy Goldwasser, Giles Hooker
2024	J	`jnl`	Targeted Learning for Variable Importance. CoRR Xiaohan Wang, Yunzhe Zhou, Giles Hooker
2024	—	`conf`	Using Longitudinal Data for Plausible Counterfactual Explanations. HI-AI@KDD Alexander Asemota, Giles Hooker
2024	J	`jnl`	Why You Should Not Trust Interpretations in Machine Learning: Adversarial Attacks on Partial Dependence Plots. CoRR Xi Xin, Giles Hooker, Fei Huang
2023	J	`jnl`	Considerations when learning additive explanations for black-box models. Mach. Learn. Sarah Tan, Giles Hooker, Paul Koch, Albert Gordo, Rich Caruana
2023	J	`jnl`	Stabilizing Estimates of Shapley Values with Control Variates. CoRR Jeremy Goldwasser, Giles Hooker
2022	J	`jnl`	A Generic Approach for Statistical Stability in Model Distillation. CoRR Yunzhe Zhou, Peiru Xu, Giles Hooker
2022	J	`jnl`	Boulevard: Regularized Stochastic Gradient Boosted Trees and Their Limiting Distribution. J. Mach. Learn. Res. Yichen Zhou, Giles Hooker
2022	J	`jnl`	Decision tree boosted varying coefficient models. Data Min. Knowl. Discov. Yichen Zhou, Giles Hooker
2022	J	`jnl`	Selecting the derivative of a functional covariate in scalar-on-function regression. Stat. Comput. Giles Hooker, Han Lin Shang
2022	J	`jnl`	The Infinitesimal Jackknife and Combinations of Models. CoRR Indrayudh Ghosal, Yunzhe Zhou, Giles Hooker
2021	J	`jnl`	Boosting Random Forests to Reduce Bias; One-Step Boosted Forest and Its Variance Estimate. J. Comput. Graph. Stat. Indrayudh Ghosal, Giles Hooker
2021	J	`jnl`	Bridging Breiman's Brook: From Algorithmic Modeling to Statistical Learning. CoRR Lucas Mentch, Giles Hooker
2021	A*	`conf`	S-LIME: Stabilized-LIME for Model Explanation. KDD Zhengze Zhou, Giles Hooker, Fei Wang
2021	J	`jnl`	S-LIME: Stabilized-LIME for Model Explanation. CoRR Zhengze Zhou, Giles Hooker, Fei Wang
2021	J	`jnl`	Unbiased Measurement of Feature Importance in Tree-Based Methods. ACM Trans. Knowl. Discov. Data Zhengze Zhou, Giles Hooker
2021	J	`jnl`	Unrestricted permutation forces extrapolation: variable importance requires at least one more model, or there is no free variable importance. Stat. Comput. Giles Hooker, Lucas Mentch, Siyu Zhou
2021	J	`jnl`	V-statistics and Variance Estimation. J. Mach. Learn. Res. Zhengze Zhou, Lucas Mentch, Giles Hooker
2020	A	`conf`	Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models. AISTATS Benjamin J. Lengerich, Sarah Tan, Chun-Hao Chang, Giles Hooker, Rich Caruana
2020	J	`jnl`	Timing observations of diffusions. Stat. Comput. Aurya Javeed, Giles Hooker
2020	—	`conf`	Tree Space Prototypes: Another Look at Making Tree Ensembles Interpretable. FODS Sarah Tan, Matvey Soloviev, Giles Hooker, Martin T. Wells
2019	J	`jnl`	Asymptotic Normality and Variance Estimation For Supervised Ensembles. CoRR Zhengze Zhou, Lucas Mentch, Giles Hooker
2019	J	`jnl`	Please Stop Permuting Features: An Explanation and Alternatives. CoRR Giles Hooker, Lucas Mentch
2019	J	`jnl`	Purifying Interaction Effects with the Functional ANOVA: An Efficient Algorithm for Recovering Identifiable Additive Models. CoRR Benjamin J. Lengerich, Sarah Tan, Chun-Hao Chang, Giles Hooker, Rich Caruana
2019	J	`jnl`	Unbiased Measurement of Feature Importance in Tree-Based Methods. CoRR Zhengze Zhou, Giles Hooker
2018	J	`jnl`	Approximation Trees: Statistical Stability in Model Distillation. CoRR Yichen Zhou, Zhengze Zhou, Giles Hooker
2018	J	`jnl`	Asymptotic Properties for Methods Combining the Minimum Hellinger Distance Estimate and the Bayesian Nonparametric Density Estimate. Entropy Yuefeng Wu, Giles Hooker
2018	J	`jnl`	Boosting Random Forests to Reduce Bias; One-Step Boosted Forest and its Variance Estimate. CoRR Indrayudh Ghosal, Giles Hooker
2018	J	`jnl`	Bootstrap bias corrections for ensemble methods. Stat. Comput. Giles Hooker, Lucas Mentch
2018	C	`conf`	Distill-and-Compare: Auditing Black-Box Models Using Transparent Model Distillation. AIES Sarah Tan, Rich Caruana, Giles Hooker, Yin Lou
2018	J	`jnl`	Experimental Design for Partially Observed Markov Decision Processes. SIAM/ASA J. Uncertain. Quantification Leifur Thorbergsson, Giles Hooker
2018	J	`jnl`	Transparent Model Distillation. CoRR Sarah Tan, Rich Caruana, Giles Hooker, Albert Gordo
2017	—	`conf`	Control Variates as a Variance Reduction Technique for Random Projections. ICPRAM (Revised Selected Papers) Keegan Kang, Giles Hooker
2017	J	`jnl`	Detecting Bias in Black-Box Models Using Transparent Model Distillation. CoRR Sarah Tan, Rich Caruana, Giles Hooker, Yin Lou
2017	J	`jnl`	Functional principal component analysis of spatially correlated data. Stat. Comput. Chong Liu, Surajit Ray, Giles Hooker
2017	J	`jnl`	Machine Learning and the Future of Realism. CoRR Giles Hooker, Cliff Hooker
2017	C	`conf`	Random Projections with Control Variates. ICPRAM Keegan Kang, Giles Hooker
2016	Misc	`conf`	Improving the recovery of principal components with semi-deterministic random projections. CISS Keegan Kang, Giles Hooker
2016	J	`jnl`	Maximal autocorrelation functions in functional data analysis. Stat. Comput. Giles Hooker, Steven Roberts
2016	J	`jnl`	Quantifying Uncertainty in Random Forests via Confidence Intervals and Hypothesis Tests. J. Mach. Learn. Res. Lucas Mentch, Giles Hooker
2016	J	`jnl`	Tree Space Prototypes: Another Look at Making Tree Ensembles Interpretable. CoRR Hui Fen Tan, Giles Hooker, Martin T. Wells
2015	J	`jnl`	Control Theory and Experimental Design in Diffusion Processes. SIAM/ASA J. Uncertain. Quantification Giles Hooker, Kevin K. Lin, Bruce Rogers
2015	J	`jnl`	Restricted likelihood ratio tests for linearity in scalar-on-function regression. Stat. Comput. Mathew W. McLean, Giles Hooker, David Ruppert
2013	A*	`conf`	Accurate intelligible models with pairwise interactions. KDD Yin Lou, Rich Caruana, Johannes Gehrke, Giles Hooker
2012	J	`jnl`	Learned-loss boosting. Comput. Stat. Data Anal. Giles Hooker, James O. Ramsay
2012	J	`jnl`	Prediction-based regularization using data augmented regression. Stat. Comput. Giles Hooker, Saharon Rosset
2004	A*	`conf`	Diagnosing extrapolation: tree-based density estimation. KDD Giles Hooker
2004	A*	`conf`	Discovering additive structure in black box functions. KDD Giles Hooker