Semi-Supervised Inference with Large and High Dimensional Data: A Semi-Parametric Perspective

Lecture | January 18 | 12-2 p.m. | 5101 Berkeley Way West

 Abhishek Chakrabortty PhD

 Public Health, School of

In this talk, I will consider SS inference for a class of standard Z-estimation problems. I will discuss first the subtleties and associated challenges that necessitate a semi-parametric perspective. I will then demonstrate a family of SS Z-estimators that are robust and adaptive, thus ensuring that they are always as efficient as the supervised estimator and more efficient (optimal in some cases)
when the information from U actually relates to the parameter of interest. These properties are crucial for advocating ‘safe’ use of unlabeled data and are often unaddressed. Our framework provides a much needed unified understanding of these problems. Multiple EHR data applications are also presented to exhibit the
practical benefits of our estimator. In the later part of the talk, I consider SS inference in high dimensional settings, and demonstrate the remarkable benefits the unlabeled data provides in seamlessly obtaining a family of SS estimators with asymptotic linear expansions, without directly requiring any sparsity conditions or
debiasing needed in supervised settings. This, in particular, facilitates high dimensional inference under minimal assumptions.

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