On Identifying Good Options under Combinatorially Structured Feedback in Finite Noisy Environments
Bibliographic record
Abstract
We consider the problem of identifying a good option out of finite set of options under combinatorially structured, noisy feedback about the quality of the options in a sequential process: In each round, a subset of the options, from an available set of subsets, can be selected to receive noisy information about the quality of the options in the chosen subset. The goal is to identify the highest quality option, or a group of options of the highest quality, with a small error probability, while using the smallest number of measurements. The problem generalizes best-arm identification problems. By extending previous work, we design new algorithms that are shown to be able to exploit the combinatorial structure of the problem in a nontrivial fashion, while being unimprovable in special cases. The algorithms call a set multicovering oracle, hence their performance and efficiency is strongly tied to whether the associated set multicovering problem can be efficiently solved.
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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 | 0.014 | 0.062 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.004 | 0.004 |
| Insufficient payload (model declined to judge) | 0.003 | 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".