<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">Maity, Subha</style></author><author><style face="normal" font="default" size="100%">Sun, Yuekai</style></author><author><style face="normal" font="default" size="100%">Banerjee, Moulinath</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">Meta-analysis of heterogeneous data: integrative sparse regression in high-dimensions</style></title><secondary-title><style face="normal" font="default" size="100%">Journal of Machine Learning Research</style></secondary-title></titles><dates><year><style  face="normal" font="default" size="100%">2022</style></year></dates><urls><web-urls><url><style face="normal" font="default" size="100%">http://jmlr.org/papers/v23/21-0739.html</style></url></web-urls></urls><number><style face="normal" font="default" size="100%">198</style></number><volume><style face="normal" font="default" size="100%">23</style></volume><pages><style face="normal" font="default" size="100%">1–50</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">We consider the task of meta-analysis in high-dimensional settings in which the data sources are similar but non-identical. To borrow strength across such heterogeneous datasets, we introduce a global parameter that emphasizes interpretability and statistical efficiency in the presence of heterogeneity. We also propose a one-shot estimator of the global parameter that preserves the anonymity of the data sources and converges at a rate that depends on the size of the combined dataset. For high-dimensional linear model settings, we demonstrate the superiority of our identification restrictions in adapting to a previously seen data distribution as well as predicting for a new/unseen data distribution. Finally, we demonstrate the benefits of our approach on a large-scale drug treatment dataset involving several different cancer cell-lines.</style></abstract></record></records></xml>