Score operators of a qubit with applications. - Michael R. Frey; Amy L. Miller; Lucas K. Mentch; Jeffrey Graham (Quantum Inf. Process., 2010)

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

Multiphase segmentation for simultaneously homogeneous and textural images. - Duy Hoang Thai; Lucas Mentch (Appl. Math. Comput., 2018)

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

Precision VISSTA: Bring-Your-Own-Device (BYOD) mHealth Data for Precision Health. - Arlene E. Chung; Kimberly Glass; Jacob Leisey-Bartsch; Lucas Mentch; Nils Gehlenborg; David Gotz (AMIA, 2019)

Precision VISSTA: Machine Learning Prediction and Inference for Bring-Your-Own-Device (BYOD) mHealth Data. - Tim Coleman; Lucas Mentch; Kimberly Glass; David Gotz; Nils Gehlenborg; Arlene E. Chung (AMIA, 2019)

Earlier Isn't Always Better: Sub-aspect Analysis on Corpus and System Biases in Summarization. - Taehee Jung; Dongyeop Kang; Lucas Mentch; Eduard H. Hovy (EMNLP/IJCNLP (1), 2019)

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

Asymptotic Distributions and Rates of Convergence for Random Forests and other Resampled Ensemble Learners. - Wei Peng; Tim Coleman; Lucas Mentch (CoRR, 2019)

Locally Optimized Random Forests. - Tim Coleman; Kimberly Kaufeld; Mary Frances Dorn; Lucas Mentch (CoRR, 2019)

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

Smudge Noise for Quality Estimation of Fingerprints and its Validation. - Robin Richter; Carsten Gottschlich; Lucas Mentch; Duy Hoang Thai; Stephan F. Huckemann (IEEE Trans. Inf. Forensics Secur., 2019)

Posterior Calibrated Training on Sentence Classification Tasks. - Taehee Jung; Dongyeop Kang; Hua Cheng; Lucas Mentch; Thomas Schaaf (ACL, 2020)

Randomization as Regularization: A Degrees of Freedom Explanation for Random Forest Success. - Lucas Mentch; Siyu Zhou (J. Mach. Learn. Res., 2020)

Precision VISSTA Study: mHealth Physical Activity Patterns and Patient-Reported Outcomes in Patients with Inflammatory Bowel Diseases. - Ashley C. Griffin; Lucas Mentch; Feng-Chang Lin; Arlene E. Chung (AMIA, 2021)

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

Scalable and Efficient Hypothesis Testing with Random Forests. - Tim Coleman; Wei Peng; Lucas Mentch (J. Mach. Learn. Res., 2022)

Getting Better from Worse: Augmented Bagging and A Cautionary Tale of Variable Importance. - Lucas Mentch; Siyu Zhou (J. Mach. Learn. Res., 2022)

Trees, forests, chickens, and eggs: when and why to prune trees in a random forest. - Siyu Zhou; Lucas Mentch (Stat. Anal. Data Min., 2023)

Forward stability and model path selection. - Nicholas Kissel; Lucas Mentch (Stat. Comput., 2024)