<ul><li>Researchers propose a two-stage method for transferring knowledge from a source domain to a target domain in high-dimensional regression models with feature mismatch.</li><li>Existing transfer learning methods assume that the source and target domains have the same feature space, which limits their practical applicability.</li><li>The proposed method involves learning the relationship between missing and observed features in the source domain and solving a joint penalized regression optimization problem in the target domain.</li><li>The researchers provide an upper bound on the parameter estimation risk and prediction risk, assuming sparse differences between the source and target domain parameters.</li></ul>

Heterogeneous transfer learning for high dimensional regression with feature mismatch

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