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i-Fusion: Individualized Fusion Learning

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Inferences from different data sources can often be fused together to yield more powerful findings than those from individual sources alone. We present a new fusion learning approach, called ‘i-Fusion’, for drawing efficient individualized inference by fusing the leanings from relevant data sources. i-Fusion is robust for handling heterogeneity arising from diverse data sources, and is ideally suited for goal-directed applications such as precision medicine. Specifically, i-Fusion summarizes individual inferences as confidence distributions (CDs), adaptively forms a clique of individuals that bear relevance to the target individual, and then suitably combines the CDs from those relevant individuals in the clique to draw inference for the target individual. In essence, i-Fusion strategically ‘borrows strength’ from relevant individuals to improve efficiency while retaining its inference validity. Computationally, i-Fusion is parallel in nature and scales up well in comparison with competing approaches. The performance of the approach is demonstrated by simulations and real data applications.

This is joint work with Jieli Shen and Minge Xie, Rutgers University.

This talk is part of the Isaac Newton Institute Seminar Series series.

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