Smoothness Adaptive Hypothesis Transfer Learning

arXiv:2402.14966v1 Announce Type: cross
Abstract: Many existing two-phase kernel-based hypothesis transfer learning algorithms employ the same kernel regularization across phases and rely on the known smoothness of functions to obtain optimality. Therefore, they fail to adapt to the varying and unknown smoothness between the target/source and their offset in practice. In this paper, we address these problems by proposing Smoothness Adaptive Transfer Learning (SATL), a two-phase kernel ridge regression(KRR)-based algorithm. We first prove that employing the misspecified fixed bandwidth Gaussian …

abstract adapt algorithms arxiv cs.lg functions hypothesis kernel paper practice regularization stat.me stat.ml transfer transfer learning type