Scalable Approximate Bayesian Inference for Outlier Detection under Informative Sampling. - Terrance D. Savitsky (J. Mach. Learn. Res., 2016)

Distributed Bayesian Varying Coefficient Modeling Using a Gaussian Process Prior. - Rajarshi Guhaniyogi; Cheng Li; Terrance D. Savitsky; Sanvesh Srivastava (J. Mach. Learn. Res., 2022)

Bayesian Pseudo Posterior Mechanism under Asymptotic Differential Privacy. - Terrance D. Savitsky; Matthew R. Williams; Jingchen Hu (J. Mach. Learn. Res., 2022)

The growclusters Package for R. - Randall Powers; Wendy Martinez; Terrance D. Savitsky (CoRR, 2023)

Bayesian Data Synthesis and Disclosure Risk Quantification: An Application to the Consumer Expenditure Surveys. - Jingchen Hu; Terrance D. Savitsky (Trans. Data Priv., 2023)

Optimization for Calibration of Survey Weights under a Large Number of Conflicting Constraints. - Matthew R. Williams; Terrance D. Savitsky (J. Comput. Graph. Stat., 2024)

Bayesian Pseudo Posterior Mechanism for Differentially Private Machine Learning. - Robert F. Chew; Matthew R. Williams; Elan A. Segarra; Alexander Preiss; Amanda Konet; Terrance D. Savitsky (CoRR, 2025)