Author

Daeyoung Ham

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Preprint Oct 2026

Global Inference for High Dimensional Quantile Regression with Heterogeneity

Quantile regression has received considerable attention in high dimensional methodology due to its robustness and ability to accommodate heteroskedasticity. Despite this attention, current inferential methods for high dimensional quantile regression have significant limitations---only testing significance, testing at a...

Tate Jacobson, Daeyoung Ham, Ben Sherwood · 0 citations

Preprint Aug 2026

Black-Box Knowledge Transfer across Distinct Feature Sets

This work proposes a two-step neural network procedure, estimating the transferable component from abundant unlabeled feature pairs that bridge the two input spaces and the non-transferable component from limited labels, and derives prediction risk bounds that improve on those of a non-transfer alternative when the non...

Oh-Ran Kwon, Daeyoung Ham · 0 citations