Adjustable Robust Optimization Reformulations of Two-Stage Worst-Case Regret Minimization Problems
Why this work is in the frame
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Bibliographic record
Abstract
Although the stochastic optimization paradigm exploits probability theory to optimize the tradeoff between risk and returns, robust optimization has gained significant popularity by reducing computation requirements through the optimization of the worst-case scenario in a set. An appealing alternative to stochastic and robust optimization consists in optimizing decisions using the notion of regret. Although regret minimization models are generally perceived as leading to less conservative decisions than those produced by robust optimization, their numerical optimization is a real challenge in general. In “Adjustable Robust Optimization Reformulations of Two-Stage Worst-case Regret Minimization Problems,” M. Poursoltani and E. Delage show how to reduce a two-stage worst-case absolute/relative regret minimization problem to a two-stage robust optimization one. This opens the way for taking advantage of recent advanced approximate and exact solution schemes for these hard problems. Their experiments corroborate the high-quality performance of affine decision rules as a popular polynomial-time approximation scheme, from which, under mild conditions, one can even expect exact regret-averse decisions.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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