@article {maity_meta-analysis_2022,
	title = {Meta-analysis of heterogeneous data: integrative sparse regression in high-dimensions},
	journal = {Journal of Machine Learning Research},
	volume = {23},
	number = {198},
	year = {2022},
	pages = {1{\textendash}50},
	abstract = {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.},
	issn = {1533-7928},
	url = {http://jmlr.org/papers/v23/21-0739.html},
	author = {Maity, Subha and Sun, Yuekai and Banerjee, Moulinath}
}
