On <scp>Heavy‐Tail</scp> Phenomena in Some <scp>Large‐Deviations</scp> Problems
Bibliographic record
Abstract
Abstract We revisit the proof of the large‐deviations principle of Wiener chaoses partially given by Borell and then by Ledoux in its full form. We show that some heavy‐tail phenomena observed in large deviations can be explained by the same mechanism as for the Wiener chaoses, meaning that the deviations are created, in a sense, by translations. More precisely, we prove a general large‐deviations principle for a certain class of functionals , where is some metric space, under the n‐fold probability measure , where α ∈ (0, 2], for which the large deviations are due to translations. We retrieve, as an application, the large‐deviations principles known for the Wigner matrices without Gaussian tails, in works by Bordenave and Caputo on one hand, and the author on the other hand, of the empirical spectral measure, the largest eigenvalue, and traces of polynomials. We also apply our large‐deviations result to the last‐passage time, which yields a large‐deviations principle when the weights follow the law , with α ∈ (0, 1). © 2020 Wiley Periodicals LLC
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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".