Asymptotic Efficiency of Deterministic Estimators for Discrete\n Energy-Based Models: Ratio Matching and Pseudolikelihood
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Bibliographic record
Abstract
Standard maximum likelihood estimation cannot be applied to discrete\nenergy-based models in the general case because the computation of exact model\nprobabilities is intractable. Recent research has seen the proposal of several\nnew estimators designed specifically to overcome this intractability, but\nvirtually nothing is known about their theoretical properties. In this paper,\nwe present a generalized estimator that unifies many of the classical and\nrecently proposed estimators. We use results from the standard asymptotic\ntheory for M-estimators to derive a generic expression for the asymptotic\ncovariance matrix of our generalized estimator. We apply these results to study\nthe relative statistical efficiency of classical pseudolikelihood and the\nrecently-proposed ratio matching estimator.\n
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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 | 0.000 |
| 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 | 0.001 |
| 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