Skyline Computation with Noisy Comparisons
Bibliographic record
Abstract
Given a set of $n$ points in a $d$-dimensional space, we seek to compute the\nskyline, i.e., those points that are not strictly dominated by any other point,\nusing few comparisons between elements. We adopt the noisy comparison model\n[FRPU94] where comparisons fail with constant probability and confidence can be\nincreased through independent repetitions of a comparison. In this model\nmotivated by Crowdsourcing applications, Groz & Milo [GM15] show three bounds\non the query complexity for the skyline problem. We improve significantly on\nthat state of the art and provide two output-sensitive algorithms computing the\nskyline with respective query complexity $O(nd\\log (dk/\\delta))$ and $O(ndk\\log\n(k/\\delta))$ where $k$ is the size of the skyline and $\\delta$ the expected\nprobability that our algorithm fails to return the correct answer. These\nresults are tight for low dimensions.\n
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How this classification was reachedexpand
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 | 0.001 | 0.001 |
| Open science | 0.003 | 0.003 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".