3 citations · 4 across the 8 of their papers we have counts for
8 papers
A New Targeted-Federated Learning Framework for Estimating Heterogeneity of Treatment Effects: A Robust Framework with Applications in Aging Cohorts
Rong Zhao, Jason Falvey, Xu Shi +2
Analyzing data from multiple sources offers valuable opportunities to improve the estimation efficiency of causal estimands. However, this analysis also poses many challenges due t…
Interrater agreement statistics under the two-rater dichotomous-response case with correlated decisions
Zizhong Tian, Vernon M. Chinchilli, Chan Shen +1
Measurement of the interrater agreement (IRA) is critical in various disciplines. To correct for potential confounding chance agreement in IRA, Cohen's kappa and many other methods…
Probabilistic Model Incorporating Auxiliary Covariates to Control FDR
Lin Qiu, Nils Murrugarra-Llerena, Vítor Silva +2
Controlling False Discovery Rate (FDR) while leveraging the side information of multiple hypothesis testing is an emerging research topic in modern data science. Existing methods r…
Variational Interpretable Learning from Multi-view Data
Lin Qiu, Lynn Lin, Vernon M. Chinchilli
The main idea of canonical correlation analysis (CCA) is to map different views onto a common latent space with maximum correlation. We propose a deep interpretable variational can…
NeurT-FDR: Controlling FDR by Incorporating Feature Hierarchy
Lin Qiu, Nils Murrugarra-Llerena, Vítor Silva +2
Controlling false discovery rate (FDR) while leveraging the side information of multiple hypothesis testing is an emerging research topic in modern data science. Existing methods r…
Probabilistic Canonical Correlation Analysis for Sparse Count Data
Lin Qiu, Vernon M. Chinchilli
Canonical correlation analysis (CCA) is a classical and important multivariate technique for exploring the relationship between two sets of continuous variables. CCA has applicatio…