Uncertainty Aware SURE Transfer Learning for Classification Problems

Transfer learning is a powerful approach for improving classification performance when the target dataset contains limited observations. However, individual-level source data are often unavailable due to privacy or access constraints. We propose GSURE-Trans, an uncertainty-aware summary-level transfer learning framework for logistic classification that uses target individual-level data together with only source coefficient estimates and standard errors. By introducing and profiling out an auxiliary bridge parameter, GSURE-Trans induces a coordinatewise Huber penalty that accounts for both source uncertainty and source-target coefficient discrepancies. To further prevent negative transfer at study level, we develop a new criterion, generalized Stein unbiased risk estimate (GSURE), that adaptively selects the scale of source borrowing. Theoretically, we establish uniform consistency of GSURE and an oracle inequality for the selected transfer weight. Simulations and real data application show that GSURE-Trans improves prediction when sources are compatible and down-weights heterogeneous sources when transfer is harmful.