RECENT ADVANCES IN GLOBALLY ROBUST INFERENCE METHODS
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
summary In this paper we discuss how recent advances in robustness theory can be used to construct globally robust inference methods, that is: procedures that remain stable and informative over a range of data distributions that includes the cases of asymmetric outliers, heavy tails, and other departures from the classical unperturbed model. In order to construct these inference methods we need robust estimators that are asymptotically normally distributed over contamination neighbourhoods and that simultaneously have good (small) asymptotic biases. First, we discuss the derivation of asymptotic approximations to the distribution of robust estimators that are valid over whole gross error contamination neighbourhoods. Next, we consider how to use the maximum bias of an estimator in the construction of globally robust confidence intervals and tests of hypotheses. Finally, we describe recently proposed efficient algorithmsto compute highly robust estimators. These algorithms allow the computation of robust estimators with good robustness and efficiency properties in a much widerrange of problems than was previously possible.
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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 | 0.001 | 0.003 |
| 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) | 0.001 | 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