← Back to all news
arXiv Machine Learning By Zaile Li, Yuchen Wan, L. Jeff Hong

Sequential Additivity in Distributionally Robust Ranking and Selection

Read the original on arXiv Machine Learning →

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.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

Related stories

arXiv Machine Learning
Jul 13

Multi-Metric Adaptive Experimental Design Under a Fixed Budget with Validation

arXiv:2506. 03062v2 Announce Type: replace Abstract: A/B tests in online experiments face statistical power challenges when testing multiple candidates simultaneously, while adaptive experimental designs (AED) alone fall short in inferring experiment statistics such as the average treatment effect, especially with many metrics (e.

By Qining Zhang, Tanner Fiez, Yi Liu, Wenyang Liu
safety
More like this →
arXiv Machine Learning
Jul 30

Early Verdicts, Better Budgets: Sequential Adaptive Rollout Allocation for Compute-Efficient RLVR

arXiv:2607. 26253v1 Announce Type: new Abstract: Reinforcement learning with verifiable rewards (RLVR) is bottlenecked by rollout generation, yet many sampled prompts produce saturated groups (all responses correct or all incorrect) whose zero reward variance yields no policy-gradient signal.

By Pixel Nomand, Elena Voss, Marcus Hale, Sofia Reyes
reinforcement-learning
More like this →
arXiv AI
Jul 23

Long-Term Sequential Decision Making under Risk

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.

By Irmaan (Mohammad), Mirzanejad, Nadjet Bourdache, Abdel-Illah Mouaddib
benchmarks
More like this →
arXiv Machine Learning
5d ago

Offline Policy Evaluation as a decision support tool for designing Adaptive Experiments

The paper explores how data from fixed A/B tests can guide the deployment of adaptive experiments using contextual bandits. By combining off‑policy evaluation with a controlled warm‑start simulation, the authors rank pre‑specified adaptive and non‑adaptive policies using doubly robust estimators. Experiments on synthetic trials and real benchmarks show that adaptive, context‑aware policies outperform fixed allocations when heterogeneity exists, but offer little advantage otherwise.

By Jo\~ao Victor Ferreira Alves, Eduardo Rocha Laurentino, Gustavo de Oliveira Kanno, Thiago Costa Rizuti da Rocha
benchmarks
More like this →