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Award ID contains: 1916271

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  1. ; ; (, Journal of machine learning research)
    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. 
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    Accepted Manuscript Full Text Available
  2. ; ; ; ; (, International Conference on Learning Representations)
    Accepted Manuscript Full Text Available
  3. ; ; (, The Annals of Statistics)
    Full Text Available 
  4. ; (, Electronic Journal of Statistics)
    Full Text Available 
  5. ; ; ; (, Proceedings of the 36th International Conference on Machine Learning)
    Accepted Manuscript Full Text Available