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.

 CA, cassandrahopes@berkeley.edu, 5106438154