1 citations · 1 across the 2 of their papers we have counts for
3 papers
stat.ML2026★ 1 cited
Spatially Robust Inference with Predicted and Missing at Random Labels
Stephen Salerno, Zhenke Wu, Tyler McCormick
When outcome data are expensive or onerous to collect, scientists increasingly substitute predictions from machine learning and AI models for unlabeled cases, a process which has c…
stat.ML2025
Do We Really Even Need Data? A Modern Look at Drawing Inference with Predicted Data
Stephen Salerno, Kentaro Hoffman, Awan Afiaz +3
As artificial intelligence and machine learning tools become more accessible, and scientists face new obstacles to data collection (e.g., rising costs, declining survey response ra…
stat.ME2024
ipd: An R Package for Conducting Inference on Predicted Data
Stephen Salerno, Jiacheng Miao, Awan Afiaz +5
Summary: ipd is an open-source R software package for the downstream modeling of an outcome and its associated features where a potentially sizable portion of the outcome data has…