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  "Title": "Model Averaging-Assisted Optimal Transfer Learning",
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  "Description": "Transfer learning, as a prevailing technique in computer\nsciences, aims to improve the performance of a target model by\nleveraging auxiliary information from heterogeneous source\ndata. We provide novel tools for multi-source transfer learning\nunder statistical models based on model averaging strategies,\nincluding linear regression models, partially linear models.\nUnlike existing transfer learning approaches, this method\nintegrates the auxiliary information through data-driven weight\nassignments to avoid negative transfer. This is the first\npackage for transfer learning based on the optimal model\naveraging frameworks, providing efficient implementations for\npractitioners in multi-source data modeling. The details are\ndescribed in Hu and Zhang (2023)\n<https://jmlr.org/papers/v24/23-0030.html>.",
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  "Author": "Xiaonan Hu [aut, cre]\n(<https://orcid.org/0000-0001-7614-2249>), Xinyu Zhang [aut]",
  "Maintainer": "Xiaonan Hu <xiaonanhu@cnu.edu.cn>",
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