TransFusion: Covariate-Shift Robust Transfer Learning for High-Dimensional Regression

arXiv:2404.01153v1 Announce Type: cross
Abstract: The main challenge that sets transfer learning apart from traditional supervised learning is the distribution shift, reflected as the shift between the source and target models and that between the marginal covariate distributions. In this work, we tackle model shifts in the presence of covariate shifts in the high-dimensional regression setting. Specifically, we propose a two-step method with a novel fused-regularizer that effectively leverages samples from source tasks to improve the learning performance on a …

abstract arxiv challenge cs.dc cs.lg distribution math.st regression robust shift stat.me stat.ml stat.th supervised learning transfer transfer learning type work