A kernel-smoothed decorrelated score with cross-fitting enables valid inference for coefficients in high-dimensional classification using piecewise linear surrogate losses.
Q., Zeng, D., Laber, E
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Inference with non-differentiable surrogate loss in a general high-dimensional classification framework
A kernel-smoothed decorrelated score with cross-fitting enables valid inference for coefficients in high-dimensional classification using piecewise linear surrogate losses.