13 citations · 31 across the 6 of their papers we have counts for
6 papers
Classifying Argumentative Relations Using Logical Mechanisms and Argumentation Schemes
Yohan Jo, Seojin Bang, Chris Reed +1
While argument mining has achieved significant success in classifying argumentative relations between statements (support, attack, and neutral), we have a limited computational und…
Detecting Attackable Sentences in Arguments
Yohan Jo, Seojin Bang, Emaad Manzoor +2
Finding attackable sentences in an argument is the first step toward successful refutation in argumentation. We present a first large-scale analysis of sentence attackability in on…
Dropout Prediction over Weeks in MOOCs via Interpretable Multi-Layer Representation Learning
Byungsoo Jeon, Namyong Park, Seojin Bang
Massive Open Online Courses (MOOCs) have become popular platforms for online learning. While MOOCs enable students to study at their own pace, this flexibility makes it easy for st…
Explaining a black-box using Deep Variational Information Bottleneck Approach
Seojin Bang, Pengtao Xie, Heewook Lee +2
Interpretable machine learning has gained much attention recently. Briefness and comprehensiveness are necessary in order to provide a large amount of information concisely when ex…
A Mixture Model to Detect Edges in Sparse Co-expression Graphs
Haim Bar, Seojin Bang
In the early days of gene expression data, researchers have focused on gene-level analysis, and particularly on finding differentially expressed genes. This usually involved making…
Robust Multiple Kernel k-means Clustering using Min-Max Optimization
Seojin Bang, Yaoliang Yu, Wei Wu
Multiple kernel learning is a type of multiview learning that combines different data modalities by capturing view-specific patterns using kernels. Although supervised multiple ker…