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- W2896534181 abstract "A common goal in statistics and machine learning is to learn models that can perform well against distributional shifts, such as latent heterogeneous subpopulations, unknown covariate shifts, or unmodeled temporal effects. We develop and analyze a distributionally robust stochastic optimization (DRO) framework that learns a model providing good performance against perturbations to the data-generating distribution. We give a convex formulation for the problem, providing several convergence guarantees. We prove finite-sample minimax upper and lower bounds, showing that distributional robustness sometimes comes at a cost in convergence rates. We give limit theorems for the learned parameters, where we fully specify the limiting distribution so that confidence intervals can be computed. On real tasks including generalizing to unknown subpopulations, fine-grained recognition, and providing good tail performance, the distributionally robust approach often exhibits improved performance." @default.
- W2896534181 created "2018-10-26" @default.
- W2896534181 creator A5008595840 @default.
- W2896534181 creator A5061331093 @default.
- W2896534181 date "2018-10-20" @default.
- W2896534181 modified "2023-09-27" @default.
- W2896534181 title "Learning Models with Uniform Performance via Distributionally Robust Optimization" @default.
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