3 papers
stat.ML2026
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…
cs.LG2025
Developing synthetic microdata through machine learning for firm-level business surveys
Jorge Cisneros, Timothy Wojan, Matthew Williams +7
Public-use microdata samples (PUMS) from the United States (US) Census Bureau on individuals have been available for decades. However, large increases in computing power and the gr…
stat.ML2025
Bayesian Pseudo Posterior Mechanism for Differentially Private Machine Learning
Robert Chew, Matthew R. Williams, Elan A. Segarra +3
Differential privacy (DP) is becoming increasingly important for deployed machine learning applications because it provides strong guarantees for protecting the privacy of individu…