DR-MOO adds distributional robustness to multi-objective optimization and gives single-loop MGDA algorithms reaching epsilon-Pareto-stationary points in O(epsilon^{-4}) samples for nonconvex problems.
Unifying distribution- ally robust optimization via optimal transport theory
2 Pith papers cite this work. Polarity classification is still indexing.
2
Pith papers citing it
years
2026 2verdicts
UNVERDICTED 2representative citing papers
Sinkhorn divergence defines ambiguity sets that make distributionally robust linear quadratic control over linear policies solvable via convex programming even with safety constraints.
citing papers explorer
-
Distributionally Robust Multi-Objective Optimization
DR-MOO adds distributional robustness to multi-objective optimization and gives single-loop MGDA algorithms reaching epsilon-Pareto-stationary points in O(epsilon^{-4}) samples for nonconvex problems.
-
Sinkhorn Ambiguity Sets for Distributionally Robust Control: Convexity, Weak Compactness, and Tractability
Sinkhorn divergence defines ambiguity sets that make distributionally robust linear quadratic control over linear policies solvable via convex programming even with safety constraints.