6 accepted papers
Topic models often fail to capture low-prevalence, domain-critical themes—so-called minority topics—such as mental health themes in online comments. While some existing methods can incorporate domain knowledge such as expected topical content, methods allowing guidance may require overly detailed ex
We consider fairness in dimensionality reduction. Nonlinear dimensionality reduction yields low dimensional representations that let users visualize and explore high-dimensional data. However, traditional dimensionality reduction may yield biased visualizations overemphasizing relationships of socie…
Learning latent vector representations via embedding models has been shown promising in machine learning. However, most of the embedding models are still limited to a single type of observation data. We propose a Gaussian copula embedding model to learn latent vector representations of items in a he…
In graph data, each node often serves multiple functionalities. However, most graph embedding models assume that each node can only possess one representation. We address this issue by proposing a nonparametric graph embedding model. The model allows each node to learn multiple representations where…
Nonlinear dimensionality reduction of high-dimensional data is challenging as the low-dimensional embedding will necessarily contain distortions, and it can be hard to determine which distortions are the most important to avoid. When annotation of data into known relevant classes is available, it ca…
Nonlinear dimensionality reduction by manifold embedding has become a popular and powerful approach both for visualization and as preprocessing for predictive tasks, but more efficient optimization algorithms are still crucially needed. Majorization-Minimization (MM) is a promising approach that mo…