11 citations · 23 across the 16 of their papers we have counts for
6 papers · 2 filters
Augmented Transfer Regression Learning with Semi-non-parametric Nuisance Models
Molei Liu, Yi Zhang, Katherine P Liao +1
In contemporary statistical learning, covariate shift correction plays an important role in transfer learning when distribution of the testing data is shifted from the training dat…
Double/Debiased Machine Learning for Logistic Partially Linear Model
Molei Liu, Yi Zhang, Doudou Zhou
We propose double/debiased machine learning approaches to infer (at the parametric rate) the parametric component of a logistic partially linear model with the binary response foll…
A Note on Debiased/Double Machine Learning Logistic Partially Linear Model
Molei Liu
It is of particular interests in many application fields to draw doubly robust inference of a logistic partially linear model with the predictor specified as combination of a targe…
Fast and Powerful Conditional Randomization Testing via Distillation
Molei Liu, Eugene Katsevich, Lucas Janson +1
We consider the problem of conditional independence testing: given a response Y and covariates (X,Z), we test the null hypothesis that Y is independent of X given Z. The conditiona…
Integrative High Dimensional Multiple Testing with Heterogeneity under Data Sharing Constraints
Molei Liu, Yin Xia, Kelly Cho +1
Identifying informative predictors in a high dimensional regression model is a critical step for association analysis and predictive modeling. Signal detection in the high dimensio…
Prior Adaptive Semi-supervised Learning with Application to EHR Phenotyping
Yichi Zhang, Molei Liu, Matey Neykov +1
Electronic Health Records (EHR) data, a rich source for biomedical research, have been successfully used to gain novel insight into a wide range of diseases. Despite its potential,…