Distributionally Robust Chance-Constrained Optimization with Deep Kernel Ambiguity Set
Bibliographic record
Abstract
A distributionally robust chance-constrained programming (DRCCP) approach based on the deep kernel ambiguity set is proposed in this paper. The kernel ambiguity set possesses notable advantages over other existing ambiguity sets from the literature, and it is constructed by using the kernel mean embedding (KME) and the maximum mean discrepancy (MMD) between distributions. In the proposed method, the worst-case Conditional Value-at-Risk (CVaR) approximation is employed to approximate the distributionally robust joint chance constraint (DRJCC). Additionally, the performance of the presented method can be significantly enhanced by using the multi-layer deep arc-cosine kernel (MLACK), compared to the use of shallow kernels. The presented DRCCP approach is applied to a numeral example and a nonlinear process optimization problem to demonstrate its efficacy.
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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 | 0.001 | 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 | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.009 | 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".