Bibliographic record
Abstract
In Multiple Instance Learning, each training sample consists of a set of unlabelled instances. The set as a whole is labeled positive if at least one instance in the set is positive, or negative otherwise. Given such training samples, the goal is to learn either an explicit description of the common positive instance(s) or a bag classifier that can assign labels to bags. Previous research has focused on this standard definition of the problem where instances in a set are independent. This raises a question: if we remove the independence assumption, can we generalize the goal of finding a description of the common instance(s) to that of finding a description of the common pattern(s) among instances? Similarly, can we generate bag classifiers that discriminate based on common pattern(s) among instances instead of just common instance(s)? This question raises many other related questions that have not been yet fully explored in the context of this problem. In this paper we first present a survey of existing methods that work with the standard definition of the problem and then elaborate on the previous question in the hope that researchers will investigate this exciting research direction.
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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 | 0.019 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.035 |
| Open science | 0.007 | 0.005 |
| Research integrity | 0.009 | 0.012 |
| Insufficient payload (model declined to judge) | 0.030 | 0.009 |
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".