Technical Note—Risk-Averse Regret Minimization in Multistage Stochastic Programs
Bibliographic record
Abstract
Regret minimization has gained popularity in a wide range of decision-making problems under uncertainty because of its capacity to identify more opportunistic solutions than worst-case value optimization. Unfortunately, the rigidity of current worst-case regret models and scarcity of tractable solution methods have been serious obstacles in multistage applications. In “Risk-Averse Regret Minimization in Multistage Stochastic Programs,” M. Poursoltani, E. Delage, and A. Georghiou consider a multistage stochastic programming setting with a discrete scenario tree. They introduce the notion of the Δ-regret model, which bridges between the ex ante and ex post regret minimization paradigms that are currently used in the regret minimization literature for single-stage problems. The notion of Δ-regret minimization is investigated for the first time both theoretically and numerically in order to better understand its behavior under a set of popular risk measures.
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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 | 0.005 | 0.015 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.005 |
| Insufficient payload (model declined to judge) | 0.008 | 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".