提出更优的不确定性决策压缩界，无需更强假设。
Improved Compression Bounds for Scenario Decision Making
- 基于采样场景构建决策，通过压缩大小优化风险控制
- 新界比现有方法更紧，样本数、容忍风险下表现更优
- 适合需要严格风险保障的决策系统设计
情景决策为不确定环境中的决策提供了一种灵活方法，并能对决策失败风险提供概率保证。该方法通过抽取不确定性样本（称为‘情景’）来做出决策。概率保证以一个边界形式呈现，即在给定最大可容忍风险下，采样到导致高风险决策的情景集合的概率上限。该边界依赖于所采情景数量、最大容忍风险以及问题内在属性——‘压缩大小’。已有研究在不同假设下提出了多种此类边界。本文提出的新边界在不增加问题假设强度的前提下，优于现有结果。
原文摘要 · Abstract (English)
Scenario decision making offers a flexible way of making decision in an uncertain environment while obtaining probabilistic guarantees on the risk of failure of the decision. The idea of this approach is to draw samples of the uncertainty and make a decision based on the samples, called "scenarios". The probabilistic guarantees take the form of a bound on the probability of sampling a set of scenarios that will lead to a decision whose risk of failure is above a given maximum tolerance. This bound can be expressed as a function of the number of sampled scenarios, the maximum tolerated risk, and some intrinsic property of the problem called the "compression size". Several such bounds have been proposed in the literature under various assumptions on the problem. We propose new bounds that improve upon the existing ones without requiring stronger assumptions on the problem.
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