2 citations · 5 across the 4 of their papers we have counts for
4 papers
On the Robustness of Removal-Based Feature Attributions
Chris Lin, Ian Covert, Su-In Lee
To explain predictions made by complex machine learning models, many feature attribution methods have been developed that assign importance scores to input features. Some recent wo…
Contrastive Corpus Attribution for Explaining Representations
Chris Lin, Hugh Chen, Chanwoo Kim +1
Despite the widespread use of unsupervised models, very few methods are designed to explain them. Most explanation methods explain a scalar model output. However, unsupervised mode…
Ergodic Limits, Relaxations, and Geometric Properties of Random Walk Node Embeddings
Christy Lin, Daniel Sussman, Prakash Ishwar
Random walk based node embedding algorithms learn vector representations of nodes by optimizing an objective function of node embedding vectors and skip-bigram statistics computed…
Node Embedding via Word Embedding for Network Community Discovery
Weicong Ding, Christy Lin, Prakash Ishwar
Neural node embeddings have recently emerged as a powerful representation for supervised learning tasks involving graph-structured data. We leverage this recent advance to develop…