Indexing Methods for Discrete Mode Pursuing Sampling
Bibliographic record
Abstract
Discrete Mode Pursuing Sampling (D-MPS) is a method used for optimization of expensive black-box functions with discrete variables. The type of discrete space where all possible combinations of discrete values of all variables are valid design points is called a full grid of points or a “regular grid” in this paper. A regular structure for sampling data is a requirement when D-MPS is applied. This paper presents two new indexing methods, i.e. “n-D” and “distance”, to transform non-regular discrete data sets into regular data sets. The n-D indexing method sorts data by all the axes, regardless of the function values. The distance indexing method sorts data dynamically by its distance from the current mode. A number of optimization problems are used to test the performance of the two methods in comparison with a 1-D indexing method which simply sorts data by a single axis. Both n-D and distance methods give much better performance than the 1-D. It is concluded that the distance indexing method can be recommended for most applications. While this paper uses D-MPS as the test bed, the indexing methods are applicable to other discrete optimization methods.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".