3 citations · 4 across the 5 of their papers we have counts for
6 papers
Semi-Supervised Learning under General Causal Models
Archer Moore, Heejung Shim, Jingge Zhu +1
Semi-supervised learning (SSL) aims to train a machine learning model using both labelled and unlabelled data. While the unlabelled data have been used in various ways to improve t…
Detecting Jumps on a Tree: a Hierarchical Normalized Stable Process Model for Evolution of Discrete Distributions
Hanxi Sun, Heejung Shim, Vinayak Rao
This work focuses on clustering populations whose hierarchical dependency structure can be described by a tree. A particular example that is the focus of this work is a phylogeneti…
A Comparison of Bayesian Inference Techniques for Sparse Factor Analysis
Yong See Foo, Heejung Shim
Dimension reduction algorithms aim to discover latent variables which describe underlying structures in high-dimensional data. Methods such as factor analysis and principal compone…
Multi-scale Poisson process approaches for differential expression analysis of high-throughput sequencing data
Heejung Shim, Zhengrong Xing, Ester Pantaleo +3
Estimating and testing for differences in molecular phenotypes (e.g. gene expression, chromatin accessibility, transcription factor binding) across conditions is an important part…
BayesCAT: Bayesian Co-estimation of Alignment and Tree
Heejung Shim, Bret Larget
Traditionally, phylogeny and sequence alignment are estimated separately: first estimate a multiple sequence alignment and then infer a phylogeny based on the sequence alignment es…
Wavelet-based genetic association analysis of functional phenotypes arising from high-throughput sequencing assays
Heejung Shim, Matthew Stephens
Understanding how genetic variants influence cellular-level processes is an important step towards understanding how they influence important organismal-level traits, or "phenotype…