Conservative surrogate models for optimization with the active subspace method
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Bibliographic record
Abstract
Low-dimensional surrogate models are useful for reducing optimization costs. However, when using them to approximate constraints, additional conditions are needed to guarantee that the optimum will satisfy the constraints of the full-size model. One such way is to make the surrogate conservative. The surrogate model is constructed using a Gaussian process regression. To ensure conservativeness, two new approaches are proposed: the first one using a concentration inequality, and the second one using bootstrapping. Those two techniques are based on a stochastic argument and thus will only enforce conservativeness up to a user-defined probability threshold. The method is applied in the context of optimization using the active subspace method for dimensionality reduction. It addresses recorded issues about constraint violations when nonlinear constraints are replaced by low-dimensional surrogate models. The resulting algorithms are tested on a toy optimization problem in thermal design.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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 it