Bibliographic record
Abstract
Subsampling is a widely used and effective method to deal with the challenges brought by big data. Most subsampling procedures are designed based on the importance sampling framework, where samples with high importance measures are given corresponding sampling probabilities. However, in the highly noisy case, these samples may cause an unstable estimator which could lead to a misleading result. To tackle this issue, we propose a gradient-based Markov subsampling (GMS) algorithm to achieve robust estimation. The core idea is to construct a subset which allows us to conservatively correct a crude initial estimate towards the true signal. Specifically, GMS selects samples with small gradients via a probabilistic procedure, constructing a subset that is likely to exclude noisy samples and provide a safe improvement over the initial estimate. We show that the GMS estimator is statistically consistent at a rate which matches the optimal in the minimax sense. The promising performance of GMS is supported by simulation studies and real data examples.
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How this classification was reachedexpand
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 | 0.000 |
| 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 | 0.001 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".