多任务学习中的稀疏性模式异质性：统计与计算视角
Multi-Task Learning for Sparsity Pattern Heterogeneity: Statistical and Computational Perspectives
AI总结：
本文提出了一种新的多任务学习方法，通过允许不同任务间稀疏性模式和非零系数值的差异，提升变量选择和预测性能。
摘要：
我们考虑多任务学习（MTL）中的一个问题，其中多个线性模型在一组数据集（
英文摘要：
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 similar. This allows models to borrow strength during variable selection even when non-zero coefficient values differ across tasks. We propose a novel mixed-integer programming formulation for our estimator. We develop custom scalable algorithms based on block coordinate descent and combinatorial local search to obtain high-quality (approximate) solutions for our estimator. Additionally, we propose a novel exact optimization algorithm to obtain globally optimal solutions. We investigate the theoretical properties of our estimators. We formally show how our estimators leverage the shared support information across tasks to achieve better variable selection performance. We evaluate the performance of our methods in simulations and two biomedical applications. Our proposed approaches appear to outperform other sparse MTL methods in variable selection and prediction accuracy. We provide the sMTL package on CRAN.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
