Uncertainty in Artificial Intelligence
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Annealing Paths for the Evaluation of Topic Models
James Foulds, Padhraic Smyth
Statistical topic models such as latent Dirich- let allocation have become enormously popu- lar in the past decade, with dozens of learning algorithms and extensions being proposed each year. As these models and algorithms continue to be developed, it becomes increasingly impor- tant to evaluate them relative to previous tech- niques. However, evaluating the predictive per- formance of a topic model is a computationally difficult task. Annealed importance sampling (AIS), a Monte Carlo technique which operates by annealing between two distributions, has pre- viously been successfully used for topic model evaluation (Wallach et al., 2009b). This tech- nique estimates the likelihood of a held-out doc- ument by simulating an annealing process from the prior to the posterior for the latent topic as- signments, and using this simulation as an im- portance sampling proposal distribution. In this paper we introduce new AIS annealing paths which instead anneal from one topic model to another, thereby estimating the relative perfor- mance of the models. This strategy can exhibit much lower empirical variance than previous ap- proaches, facilitating reliable per-document com- parisons of topic models. We then show how to use these paths to evaluate the predictive perfor- mance of topic model learning algorithms by effi- ciently estimating the likelihood at each iteration of the training procedure. The proposed method achieves better held-out likelihood estimates for this task than previous algorithms with, in some cases, an order of magnitude less computation.
Pages: 220-229
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PDF Link: /papers/14/p220-foulds.pdf
AUTHOR = "James Foulds and Padhraic Smyth",
TITLE = "Annealing Paths for the Evaluation of Topic Models",
BOOKTITLE = "Proceedings of the Thirtieth Conference Annual Conference on Uncertainty in Artificial Intelligence (UAI-14)",
ADDRESS = "Corvallis, Oregon",
YEAR = "2014",
PAGES = "220--229"

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