An Axiomatic Analysis of Distributionally Robust Optimization with $q$-Norm Ambiguity Sets for Probability Smoothing

TL;DR

This paper investigates the axiomatic properties of $q$-norm distributionally robust optimization estimators, highlighting their flexibility and relation to regularized empirical loss minimization.

Contribution

It establishes that $q$-DRO estimators satisfy key axioms like Positivity, Symmetry, and Order Preservation, offering a principled alternative to classical smoothing methods.

Findings

$q$-DRO satisfies Positivity and Symmetry for all $q \\in [1, \\infty]$.

For $q \\in (1, \\infty)$, $q$-DRO also satisfies Order Preservation.

The $q$-DRO formulation is equivalent to regularized empirical loss minimization.

Abstract

We analyze the axiomatic properties of a class of probability estimators derived from Distributionally Robust Optimization (DRO) with $q$-norm ambiguity sets ($q$-DRO), a principled approach to the zero-frequency problem. While classical estimators such as Laplace smoothing are characterized by strong linearity axioms like Ratio Preservation, we show that $q$-DRO provides a flexible alternative that satisfies other desirable properties. We first prove that for any $q \in [1, \infty]$, the $q$-DRO estimator satisfies the fundamental axioms of Positivity and Symmetry. For the case of $q \in (1, \infty)$, we then prove that it also satisfies Order Preservation. Our analysis of the optimality conditions also reveals that the $q$-DRO formulation is equivalent to the regularized empirical loss minimization.