On‐line partitioning of the sample space in the regional adaptive algorithm
Bibliographic record
Abstract
Abstract The regional adaptive (RAPT) algorithm is particularly useful in sampling from multimodal distributions. We propose an adaptive partitioning of the sample space, to be used in conjunction with the RAPT sampler and its variants. The adaptive partitioning consists in defining a hyperplane that is orthogonal to the line joining averaged coordinates in two separate regions and that goes through a point such that both averaged coordinates are equally Mahalanobis‐distant from this point. This yields an adaptive process that is robust to the choice of initial partition, stabilizes rapidly and is implemented at a marginal computational cost. The ergodicity of the sampler is verified through the simultaneous uniform ergodicity and diminishing adaptation conditions. The approach is compared to the RAPT algorithm with fixed regions and to the RAPT with online recursion (RAPTOR) through various examples, including a real data application. In short, our main contribution is the development of an alternative version of RAPTOR that seems to have no obvious downside and runs 15–35% faster in the examples considered.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".