SCENT, a stochastic proximal mirror descent on the dual variable with an exponential Bregman divergence, optimizes compositional entropic risk at O(1/sqrt(T)) in the convex setting and matches or beats baselines on large-scale losses.
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A Geometry-Aware Efficient Algorithm for Compositional Entropic Risk Minimization
SCENT, a stochastic proximal mirror descent on the dual variable with an exponential Bregman divergence, optimizes compositional entropic risk at O(1/sqrt(T)) in the convex setting and matches or beats baselines on large-scale losses.