Interactive Encoding and Decoding for One Way Learning: Near Lossless Recovery With Side Information at the Decoder
Bibliographic record
Abstract
A source coding paradigm called interactive encoding and decoding (IED) is considered for a source network where a finite alphabet sourceXis to be encoded, and another finite alphabet sourceYcorrelated withXis available only to the decoder as a helper. The optimal performance achievable asymptotically (OPAA) by IED is investigated, where the performance is measured as the average number of bits per symbol exchanged by the encoder and decoder until the decoder learnsXwith high probability. First, it is shown that for any stationary(X,Y), the OPAA by IED is given by the conditional entropy rateH(X|Y) ofXgivenY. This is in contrast with noninteractive Slepian-Wolf (SW) coding, where the OPAA is shown in general to be strictly greater thanH(X|Y) when(X,Y) is not ergodic. Second, for a memoryless source pair (X, Y), it is shown that IED approachesH(X|Y) faster than SW coding does. Finally, it is demonstrated that one can convert any classical universal data compression algorithm with side information to a universal IED algorithm for the class¿of all stationary ergodic source pairs. In contrast, universal SW coding algorithms for the class¿do not exist.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".