Reinforcement Learning for MAP Inference in Large Factor Graphs

CMPSCI Technical Report, UM-CS-2008-040, University of Massachusetts, December 2008
Reinforcement Learning for MAP Inference in Large Factor Graphs
Khashayar Rohanimanesh, Michael Wick, Sameer Singh, Andrew McCallum
Abstract

Large, relational factor graphs with structure defined by first-order logic or other languages give rise to notoriously difficult inference problems. Because unrolling the structure necessary to represent distributions over all hypotheses has exponential blow-up, solutions are often derived from MCMC.

However, because of limitations in the design and parameterization of the jump function, these sampling-based methods suffer from local minima—the system must transition through lower-scoring configurations before arriving at a better MAP solution.

This paper presents a new method of explicitly selecting fruitful downward jumps by leveraging reinforcement learning (RL) to model delayed reward with a log-linear function approximation of residual future score improvement.

Our method provides dramatic empirical success, producing new state-of-the-art results on a complex joint model of ontology alignment, with a 48% reduction in error over state-of-the-art in that domain.

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.

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