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A0169
Title: Transfer learning under high-dimensional generalized linear models Authors:  Yang Feng - NYU (United States) [presenting]
Abstract: The transfer learning problem is studied under high-dimensional generalized linear models (GLMs), which aim to improve the fit of target data by borrowing information from useful source data. Given which sources to transfer, we propose a transfer learning algorithm on GLM and derive its $\ell_1/\ell_2$-estimation error bounds as well as a bound for a prediction error measure. The theoretical analysis shows that under certain conditions, when the target and source are sufficiently close to each other, these bounds could be improved over those of the classical penalized estimator using only target data. When we ignore which sources to transfer, an algorithm-free transferable source detection approach is introduced to detect informative sources. The detection consistency is proved under the high-dimensional GLM transfer learning setting. Extensive simulations and a real-data experiment verify the effectiveness of our algorithms. We summarize R codes for GLM transfer learning algorithms in a new R package glmtrans, which is available on CRAN.