arXiv:2606. 11347v1 Announce Type: cross Abstract: We propose Annealed Entropic Allocation, an annealed weighted soft-min framework for sequential budget allocation in ranking and selection.
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arXiv:2607. 19914v1 Announce Type: new Abstract: We study finite-horizon MDP planning under \emph{root-based} (resolute) risk objectives that apply a rank-dependent functional to the distribution of total returns.
arXiv:2609.21899v1 Announce Type: new Abstract: Best-of-$n$ (BoN) sampling is a simple yet effective inference-time alignment method, but hard maximization provides only coarse control over the trade...
The paper studies distributionally robust ranking and selection (DRR&S), where the goal is to identify the best alternative under input uncertainty by considering multiple plausible input distributions. It introduces the concept of sequential additivity, showing that efficient sampling should focus on a small, additive set of critical scenarios rather than a multiplicative number. The authors prove an algorithm‑independent lower bound on sampling, design an additive allocation (AA) procedure that meets this bound and achieves exponentially decreasing error probability, and extend the approach to a general additive allocation (GAA) framework that incorporates traditional R&S sampling rules.
The paper derives the exact finite‑population variance of a weighted risk estimator for rare‑event forecasting in dependent sequences and solves for the optimal stratified allocation of a small subsample. It shows that the optimal allocation is equal across strata, independent of the imbalance ratio, and provides a parameter‑free efficiency prediction A(π,f). The authors validate these theoretical predictions on a real‑world dataset of U.S. equities, demonstrating that the predicted ordering of sampling designs matches empirical results.
arXiv:2607. 16194v1 Announce Type: new Abstract: In modern financial markets, decision-makers increasingly rely on quantitative methods to navigate complex trade-offs among multiple, often conflicting objectives.
arXiv:2606. 06555v1 Announce Type: cross Abstract: Noisy evolution strategies under fixed evaluation budgets face a depth-fidelity trade-off: spending evaluations to denoise intra-generation rankings reduces the number of distribution updates the optimizer can execute.