arXiv CS
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Scalable Cox Regression via Grouped Risk Sets and Sharper LogSumExp Rates
Motivated by the computational challenges of large-scale Cox regression, we study stochastic minimization of LogSumExp objectives over large sets. Mini-batch normalizer estimates generally yield biased gradients. We instead use a softplus surrogate that introduces one auxiliary scalar per normalizer
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