5 citations · 12 across the 3 of their papers we have counts for
3 papers
Prediction-Constrained Topic Models for Antidepressant Recommendation
Michael C. Hughes, Gabriel Hope, Leah Weiner +4
Supervisory signals can help topic models discover low-dimensional data representations that are more interpretable for clinical tasks. We propose a framework for training supervis…
Prediction-Constrained Training for Semi-Supervised Mixture and Topic Models
Michael C. Hughes, Leah Weiner, Gabriel Hope +4
Supervisory signals have the potential to make low-dimensional data representations, like those learned by mixture and topic models, more interpretable and useful. We propose a fra…
Model Selection Framework for Graph-based data
Rajmonda S. Caceres, Leah Weiner, Matthew C. Schmidt +2
Graphs are powerful abstractions for capturing complex relationships in diverse application settings. An active area of research focuses on theoretical models that define the gener…