研究人员提出了一种新颖的鲁棒分布外随机优化框架，旨在即使在历史数据与目标分布不完全匹配的情况下也能做出有效决策。该方法从相关数据分布中学习一个不确定性集，并将其纳入最小-最大随机规划中，提供了严格的泛化保证。在新闻供应商和投资组合优化任务上的实验表明，该方法在未见过分布下的性能优越。此外，还提出了一种名为StoSOO的新算法，用于带有噪声评估的全局函数最大化，该算法在对函数的半度量没有先验知识的情况下运行，并实现了近乎最优的性能。 AI
影响 引入了不确定性和噪声评估下的优化新理论框架和算法，可能提高AI决策系统的鲁棒性。
排序理由 该集群包含两篇详细介绍新优化算法和框架的学术论文。
在 Hugging Face Daily Papers 阅读 →
AI 生成摘要 · Google Gemini · 来自 3 个来源。 我们如何撰写摘要 →
Data-driven decision-making under uncertainty typically presumes the collection of historical data from an unknown target probability distribution. However, one may have no access to any data from the target distribution prior to decision-making. To address this challenge, we pro…
arXiv:2604.24537v1 Announce Type: cross Abstract: We study the problem of global maximization of a function f given a finite number of evaluations perturbed by noise. We consider a very weak assumption on the function, namely that it is locally smooth (in some precise sense) with…
We study the problem of global maximization of a function f given a finite number of evaluations perturbed by noise. We consider a very weak assumption on the function, namely that it is locally smooth (in some precise sense) with respect to some semi-metric, around one of its gl…