On coding for data analytics: New information distances
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
Distance plays a vital role in many applications of data analytics. In this paper, the concept of distance between any two data objects X and Y is addressed from the perspective of Shannon information theory. Consider a coding paradigm where X and Y are encoded into a sequence of coded bits specifying a codeword (or method) which would in turn convert Y into X, and X into Y such that both the distortion between X and X and the distortion between Y and Y are less than or equal to a prescribed threshold D. Given a class C of coding schemes within the coding paradigm, the information distance R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</sub> (X, Y, D) between X and Y at the distortion level D is defined as the smallest number of coded bits afforded by coding schemes from C. For two important classes C, R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</sub> (X, Y, D) is shown to be indeed a pseudo distance in some sense; it is further characterized or bounded. When C is the class of so-called separately precoded broadcast codes, it is shown that for any stationary, totally ergodic sources X and Y, RC (X, Y, D) is equal to the maximum of the Wyner-Ziv coding rate of X with Y as side information and the Wyner-Ziv coding rate of Y with X as side information. In the general case where C consists of all codes within the coding paradigm, upper and lower bounds to R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</sub> (X, Y, D) are established, and are further shown to be tight when X and Y are jointly Gaussian. The distance R <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">C</sub> (X, Y, D) generalizes the notion of information distance defined within the framework of Kolmogorov complexity.
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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 | 0.000 | 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 it