86 citations · 307 across the 40 of their papers we have counts for
6 papers · 1 filter
Semi-Supervised Off Policy Reinforcement Learning
Aaron Sonabend-W, Nilanjana Laha, Ashwin N. Ananthakrishnan +2
Reinforcement learning (RL) has shown great success in estimating sequential treatment strategies which take into account patient heterogeneity. However, health-outcome information…
Efficient Estimation and Evaluation of Prediction Rules in Semi-Supervised Settings under Stratified Sampling
Jessica Gronsbell, Molei Liu, Lu Tian +1
In many contemporary applications, large amounts of unlabeled data are readily available while labeled examples are limited. There has been substantial interest in semi-supervised…
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…
Expert-Supervised Reinforcement Learning for Offline Policy Learning and Evaluation
Aaron Sonabend-W, Junwei Lu, Leo A. Celi +2
Offline Reinforcement Learning (RL) is a promising approach for learning optimal policies in environments where direct exploration is expensive or unfeasible. However, the adoption…
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,…