发表机构
LMU Munich; DFKI（慕尼黑大学; 德国人工智能研究中心）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
该研究提出基于Credal集的风险规避机器学习方法，可在分类、分布偏移及强化学习场景中避免灾难性决策，且预期性能损失小。
AI 中文摘要
在许多机器学习应用中，必须防范可能导致重大损失的最坏情况和预测。原则上，这可以通过训练风险规避预测模型来实现，该模型最小化诸如条件风险价值（CVaR）之类的损失函数，而非依赖于平均表现良好的模型。然而在实践中，这种风险规避方法的有效性会因学习者对真实损失分布以及由此产生的真实CVaR的不确定性而受到削弱。为实现可靠的风险规避，我们提出一种方法，其中这种认知不确定性用Credal集（即概率分布的集合）来表示。更具体地说，我们开发了一种高效且可靠的学习器，它以Credal集的形式生成预测，并将其与一种新的决策规则相结合，该规则将每个Credal集映射到用于CVaR最小化的单一预测分布。在分类、分布偏移下以及强化学习场景中，我们的方法能可靠地避免灾难性决策，同时在预期性能上的损失很小。
英文摘要
In many machine learning applications, it is necessary to guard against worst-case scenarios and predictions that could result in substantial losses. In principle, this can be achieved by training risk-averse predictive models that minimize loss functions such as conditional value-at-risk (CVaR), rather than relying on models that perform well on average. In practice, however, the effectiveness of this approach to risk aversion is undermined by the learner's uncertainty regarding the true loss distribution and, consequently, the true CVaR. To achieve reliable risk-aversion, we propose a method in which this (epistemic) uncertainty is represented in terms of credal sets, i.e., sets of probability distributions. More specifically, we develop an efficient yet reliable learner that produces predictions in the form of credal sets and combine it with a novel decision rule that maps each credal set to a single predictive distribution for CVaR minimization. Across classification, under distribution shift, and in reinforcement learning, our approach reliably avoids catastrophic decisions, while sacrificing little in expected performance.
