distributionally robust optimization
Primal-Dual Coupled Variance Reduction for Faster Distributionally Robust Optimization
We consider the penalized distributionally robust optimization (DRO) problem with a closed, convex uncertainty set, a setting that encompasses learning using f -DRO and spectral/ L-risk minimization.
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Wasserstein Distributionally Robust Optimization Through the Lens of Structural Causal Models and Individual Fairness
To address this gap, we first formulate the DRO problem from causality and individual fairness perspectives.
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DistributionallyRobustOptimizationwithData Geometry
It has been well documented that high dimensional data approximately resides on low dimensional manifolds.
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A First-Order Algorithmic Framework for Distributionally Robust Logistic Regression
JIAJIN LI, SEN HUANG, Anthony Man-Cho So
Moreover,manyoftheDROproblems thatariseinthe learning context admits exact convex reformulations and hence can be tackled byoff-the-shelf solvers.
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