Penalized solutions to functional regression problems. - Jaroslaw Harezlak; Brent A. Coull; Nan M. Laird; Shannon R. Magari; David Christiani (Comput. Stat. Data Anal., 2007)

Robust Hypothesis Test for Nonlinear Effect with Gaussian Processes. - Jeremiah Z. Liu; Brent A. Coull (NIPS, 2017)

Adaptive and Calibrated Ensemble Learning with Dependent Tail-free Process. - Jeremiah Zhe Liu; John W. Paisley; Marianthi-Anna Kioumourtzoglou; Brent A. Coull (CoRR, 2018)

Accurate Uncertainty Estimation and Decomposition in Ensemble Learning. - Jeremiah Z. Liu; John W. Paisley; Marianthi-Anna Kioumourtzoglou; Brent A. Coull (NeurIPS, 2019)

Bayesian wavelet-packet historical functional linear models. - Mark J. Meyer; Elizabeth J. Malloy; Brent A. Coull (Stat. Comput., 2021)

Bayesian Nonparametric Model Averaging Using Scalable Gaussian Process Representations. - John W. Paisley; Sebastian Rowland; Jeremiah Zhe Liu; Brent A. Coull; Marianthi-Anna Kioumourtzoglou (IEEE Big Data, 2022)

Towards a Unified Framework for Uncertainty-aware Nonlinear Variable Selection with Theoretical Guarantees. - Wenying Deng; Beau Coker; Rajarshi Mukherjee; Jeremiah Z. Liu; Brent A. Coull (NeurIPS, 2022)

Towards a Unified Framework for Uncertainty-aware Nonlinear Variable Selection with Theoretical Guarantees. - Wenying Deng; Beau Coker; Jeremiah Zhe Liu; Brent A. Coull (CoRR, 2022)

Covid-19 vaccination and menstrual cycle length in the Apple Women's Health Study. - Elizabeth A. Gibson; Huichu Li; Victoria Fruh; Malaika Gabra; Gowtham Asokan; Anne Marie Z. Jukic; Donna D. Baird; Christine L. Curry; Tyler Fischer-Colbrie; Jukka-Pekka Onnela; Michelle A. Williams; Russ Hauser; et al. (npj Digit. Medicine, 2022)

Menstrual cycle length variation by demographic characteristics from the Apple Women's Health Study. - Huichu Li; Elizabeth A. Gibson; Anne Marie Z. Jukic; Donna D. Baird; Allen J. Wilcox; Christine L. Curry; Tyler Fischer-Colbrie; Jukka-Pekka Onnela; Michelle A. Williams; Russ Hauser; Brent A. Coull; Shruthi Mahalingaiah (npj Digit. Medicine, 2023)

skipTrack: An R package for Identifying Skips in Self-Tracked Mobile Menstrual Cycle Data. - Luke Duttweiler; Shruthi Mahalingaiah; Brent A. Coull (J. Open Source Softw., 2024)