Tailoring to the Tails: Risk Measures for Fine-Grained Tail Sensitivity

• URL: http://arxiv.org/abs/2208.03066v1
• Date: Fri, 5 Aug 2022 09:51:18 GMT
• Title: Tailoring to the Tails: Risk Measures for Fine-Grained Tail Sensitivity
• Authors: Christian Fr\"ohlich, Robert C. Williamson
• Abstract summary: Expected risk rearrangement (ERM) is at the core of machine learning systems. We propose a general approach to construct risk measures which exhibit a desired tail sensitivity.
• Score: 10.482805367361818
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Expected risk minimization (ERM) is at the core of machine learning systems. This means that the risk inherent in a loss distribution is summarized using a single number - its average. In this paper, we propose a general approach to construct risk measures which exhibit a desired tail sensitivity and may replace the expectation operator in ERM. Our method relies on the specification of a reference distribution with a desired tail behaviour, which is in a one-to-one correspondence to a coherent upper probability. Any risk measure, which is compatible with this upper probability, displays a tail sensitivity which is finely tuned to the reference distribution. As a concrete example, we focus on divergence risk measures based on f-divergence ambiguity sets, which are a widespread tool used to foster distributional robustness of machine learning systems. For instance, we show how ambiguity sets based on the Kullback-Leibler divergence are intricately tied to the class of subexponential random variables. We elaborate the connection of divergence risk measures and rearrangement invariant Banach norms.

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