Screening Collapse under Recursive Synthetic Data
Recursive synthetic-data training can degrade a fitted model, but its effects on the variable-screening stage that precedes model fitting are less understood. When each generation labels data for the next, a marginal screener ranks variables against the current pseudo-label target rather than the original response. Across generations, this target drift can erode or reverse the active--inactive score margin and cause screening to discard variables that clean-label screening would retain. We call this process \emph{screening collapse}. In a Gaussian linear model with minimum-norm recursive generators, we establish finite-sample screening bounds, characterize a late-generation information-lo...
| Authors | Jia Zhang, Xin Chen |
|---|---|
| Primary category | stat.ML |
| Published | 11 October 2026 |
| Last updated at source | 11 October 2026 |
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