Daeyoung Ham

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-transferable component is small or smooth.