Research map / Optimization / Nonconvex and smooth optimization
Revisiting Large-Scale Non-convex Distributionally Robust Optimization
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Revisits large-scale non-convex distributionally robust optimization (DRO) with smooth loss functions, building on prior analysis by Jin et al.
Problem
Training machine learning models with distributionally robust optimization on non-convex smooth losses at large scale, where worst-case-distribution optimization is expensive.
Approach
Revisits the non-convex DRO problem studied by Jin et al. and proposes improved large-scale methods; the abstract is truncated, so the specific technique is not stated.
Experimental setup
Builds on: Jin et al. non-convex DRO analysis
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