Distributionally Robust Survival Models under Subpopulation Shift and Outlier Contamination
Read the original on arXiv AI →The paper introduces a distributionally robust framework for survival analysis that simultaneously tackles latent subpopulation shift and outlier contamination. It employs an outer minimization to refine the nominal distribution by down-weighting contaminated samples and an inner maximization to target the most challenging subpopulation, directly handling non-decomposable survival losses such as the Cox partial log-likelihood. An alternating gradient-based algorithm, guided by KKT conditions, is developed, and experiments on simulated data and two benchmarks show improved worst-group performance and stable training under contamination.
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