16 citations · 67 across the 20 of their papers we have counts for
3 papers · 1 filter
Design-Based Supervised Learning with Noisy Human Labels
Robert Chew, Matthew R. Williams
Researchers increasingly use automated classifiers to label unstructured data for statistical analysis. Existing rectification methods can correct errors in these automated labels…
Design Effect Ratios for Bayesian Survey Models: A Diagnostic Framework for Identifying Survey-Sensitive Parameters
JoonHo Lee, Matthew R. Williams, Terrance D. Savitsky
Bayesian hierarchical models fitted to complex survey data often use weighted pseudo-posteriors, followed by a correction to match a design-based sandwich covariance. Correcting ev…
Privacy Amplification for Synthetic data using Range Restriction
Jingchen Hu, Matthew R. Williams, Terrance D. Savitsky
We introduce a new class of range restricted formal data privacy standards that condition on owner beliefs about sensitive data ranges. By incorporating this additional information…