Error analysis for approximate CVaR-optimal control with a maximum cost
Why this work is in the frame
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Bibliographic record
Abstract
Abstract We consider a risk-aware optimal control problem, where the objective is the conditional value-at-risk of a maximum of stagewise and terminal costs along a finite time-horizon. Previous techniques for this problem rely on dynamic programming (DP), which is notorious for scalability issues. Since approximate DP (ADP) in risk-neutral settings can alleviate such issues, we study an ADP method for the aforementioned risk-aware setting that relies on empirical sampling and function approximation in a reproducing kernel Hilbert space. Our contribution is the derivation of sup-norm approximation error bounds that are pointwise functions of the sampling process, using techniques from functional analysis, probability theory and a relaxed Lipschitz condition. The performance of the method is evaluated using a single-stage reservoir management problem, and the effect of different algorithm parameters on the error is illustrated.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.002 |
| 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