Risk regularization through bidirectional dispersion
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Many alternative notions of "risk" (e.g., CVaR, entropic risk, DRO risk) have been proposed and studied, but these risks are all at least as sensitive as the mean to loss tails on the upside, and tend to ignore deviations on the downside. In this work, we study a complementary new risk class that penalizes loss deviations in a bidirectional manner, while having more flexibility in terms of tail sensitivity than is offered by classical mean-variance, without sacrificing computational or analytical tractability.
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