2012
NIPS
NeurIPS 2012
How They Vote: Issue-Adjusted Models of Legislative Behavior
Abstract
We develop a probabilistic model of legislative data that uses the text of the bills to uncover lawmakers' positions on specific political issues. Our model can be used to explore how a lawmaker's voting patterns deviate from what is expected and how that deviation depends on what is being voted on. We derive approximate posterior inference algorithms based on variational methods. Across 12 years of legislative data, we demonstrate both improvement in heldout predictive performance and the model's utility in interpreting an inherently multi-dimensional space.
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Interdisciplinary Bridge
— Artificial Intelligence and Interdisciplinary and Machine Learning
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Trend Setter
— Probabilistic Modeling
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Keyword Pioneer
— legislative behavior
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Cross-Pollinator
— Artificial Intelligence, Computer Science, Computer Vision, Data Science & Analytics, Deep Learning, Healthcare & Medicine, Interdisciplinary, Knowledge & Reasoning, Machine Learning, Mathematics & Optimization, Natural Language Processing, Reinforcement Learning, Robotics, Speech & Audio
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Topic Pioneer
— Variational Inference
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Hot Topic Early Bird
— text analysis
Authors
Topics
Artificial Intelligence > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Core Methods > Classification
Machine Learning > Core Methods > Clustering
Machine Learning > Optimization & Theory > Statistical Learning
Natural Language Processing > Applications > Text Classification
Interdisciplinary > Social > Social Media Analysis
Machine Learning > Bayesian & Probabilistic > Probabilistic Modeling
Machine Learning > Learning Types > Variational Inference