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 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".