Towards optimal error-estimating codes through the lens of Fisher information analysis
Bibliographic record
Abstract
Error estimating coding (EEC) has recently been established as an important tool to estimate bit error rates in the transmission of packets over wireless links, with a number of potential applications in wireless networks. In this paper, we present an in-depth study of error estimating codes through the lens of Fisher information analysis and find that the original EEC estimator fails to exploit the information contained in its code to the fullest extent. Motivated by this discovery, we design a new estimator for the original EEC algorithm, which significantly improves the estimation accuracy, and is empirically very close to the Cramer-Rao bound. Following this path, we generalize the EEC algorithm to a new family of algorithms called gEEC generalized EEC. These algorithms can be tuned to hold 25-35% more information with the same overhead, and hence deliver even better estimation accuracy---close to optimal, as evidenced by the Cramer-Rao bound. Our theoretical analysis and assertions are supported by extensive experimental evaluation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.002 | 0.005 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), 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".