Distributionally Robust Optimization via Ball Oracle Acceleration

Understand

We develop and analyze algorithms for distributionally robust optimization (DRO) of convex losses.

• In particular, we consider group-structured and bounded $f$-divergence uncertainty sets.
• Our approach relies on an accelerated method that queries a ball optimization oracle, i.e., a subroutine that minimizes the objective within a small ball around the query point.
• Our main contribution is efficient implementations of this oracle for DRO objectives.