8 citations · 9 across the 6 of their papers we have counts for
6 papers
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…
Interpretable Deep Representation Learning from Temporal Multi-view Data
Lin Qiu, Vernon M. Chinchilli, Lin Lin
In many scientific problems such as video surveillance, modern genomics, and finance, data are often collected from diverse measurements across time that exhibit time-dependent het…
A comparison of Deep Learning performances with other machine learning algorithms on credit scoring unbalanced data
Louis Marceau, Lingling Qiu, Nick Vandewiele +1
Training models on highly unbalanced data is admitted to be a challenging task for machine learning algorithms. Current studies on deep learning mainly focus on data sets with bala…