A meta-distribution-based robust optimization method learns RKHS uncertainty sets from relevant sources to guarantee out-of-distribution performance on unseen target distributions.
Learning models with uniform performance via distributionally robust optimization.The Annals of Statistics, 49(3):1378–1406
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Penalty-based first-order methods find ε-KKT points in bilevel minimax problems with Õ(ε^{-4}) deterministic and Õ(ε^{-9}) stochastic oracle complexity, improving prior bounds for constrained lower-level cases via Lagrangian duality.
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Robust Out-of-Distribution Stochastic Optimization
A meta-distribution-based robust optimization method learns RKHS uncertainty sets from relevant sources to guarantee out-of-distribution performance on unseen target distributions.
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Penalty-Based First-Order Methods for Bilevel Optimization with Minimax and Constrained Lower-Level Problems
Penalty-based first-order methods find ε-KKT points in bilevel minimax problems with Õ(ε^{-4}) deterministic and Õ(ε^{-9}) stochastic oracle complexity, improving prior bounds for constrained lower-level cases via Lagrangian duality.