High‐Dimensional Regression With Missing Data: An Asymptotic Study
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT We present an asymptotic analysis of high‐dimensional linear regression with missing data and propose a novel method to approximate leave‐one‐out cross validation, facilitating faster hyperparameter tuning. Our analysis extends beyond standard ridge regression to include adversarial training, introducing a robust formulation specifically designed to handle missing data. Building upon existing literature in reguralization, who addressed complete data settings, our framework establishes asymptotic properties of regression models with missing data. Notably, we are the first to explore cross‐validation for adversarial training in finite‐sample regimes where the loss functions is nondifferentiable. Our cross‐validation approximation demonstrates substantial computational advantages over traditional methods.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it