Inference and Learning in Large Factor Graphs with Adaptive Proposal Distributions

CMPSCI Technical Report, UM-CS-2009-008, University of Massachusetts, December 2008
Inference and Learning in Large Factor Graphs with Adaptive Proposal Distributions
Khashayar Rohanimanesh, Michael Wick, Andrew McCallum

Large templated factor graphs with complex structure that changes during inference have been shown to provide state-of-the-art experimental results in tasks such as identity uncertainty and information integration. However, inference and learning in these models is notoriously difficult.

This paper formalizes, analyzes and proves convergence for the SampleRank algorithm, which learns extremely efficiently by calculating approximate parameter estimation gradients from each proposed MCMC jump. Next we present a parameterized, adaptive proposal distribution, which greatly increases the number of accepted jumps.

We combine these methods in experiments on a real-world information extraction problem and demonstrate that the adaptive proposal distribution requires 27% fewer jumps than a more traditional proposer.

Another publication from the same category: Machine Learning and Data Science

WWW '17 Perth Australia April 2017

Drawing Sound Conclusions from Noisy Judgments

David Goldberg, Andrew Trotman, Xiao Wang, Wei Min, Zongru Wan

The quality of a search engine is typically evaluated using hand-labeled data sets, where the labels indicate the relevance of documents to queries. Often the number of labels needed is too large to be created by the best annotators, and so less accurate labels (e.g. from crowdsourcing) must be used. This introduces errors in the labels, and thus errors in standard precision metrics (such as P@k and DCG); the lower the quality of the judge, the more errorful the labels, consequently the more inaccurate the metric. We introduce equations and algorithms that can adjust the metrics to the values they would have had if there were no annotation errors.

This is especially important when two search engines are compared by comparing their metrics. We give examples where one engine appeared to be statistically significantly better than the other, but the effect disappeared after the metrics were corrected for annotation error. In other words the evidence supporting a statistical difference was illusory, and caused by a failure to account for annotation error.