June 11, 2024, 4:45 a.m. | Kayhan Behdin, Gabriel Loewinger, Kenneth T. Kishida, Giovanni Parmigiani, Rahul Mazumder

stat.ML updates on arXiv.org arxiv.org

arXiv:2212.08697v2 Announce Type: replace-cross
Abstract: We consider a problem in Multi-Task Learning (MTL) where multiple linear models are jointly trained on a collection of datasets ("tasks"). A key novelty of our framework is that it allows the sparsity pattern of regression coefficients and the values of non-zero coefficients to differ across tasks while still leveraging partially shared structure. Our methods encourage models to share information across tasks through separately encouraging 1) coefficient supports, and/or 2) nonzero coefficient values to be …

abstract arxiv collection computational datasets framework key linear multiple multi-task learning pattern perspectives problem regression replace sparsity statistical stat.me stat.ml tasks type values

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