Randomized Optimum Models for Structured Prediction

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Randomized Optimum Models for Structured Prediction

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dc.contributor.author Tarlow, Daniel
dc.contributor.author Adams, Ryan Prescott
dc.contributor.author Zemel, Richard S.
dc.date.accessioned 2012-05-10T13:25:10Z
dc.date.issued 2012
dc.identifier.citation Tarlow, Daniel, Ryan P. Adams, and Richard S. Zimmel. Forthcoming. Randomized optimum models for structured prediction. In Proceedings of the Fifteenth Conference on Artificial Intelligence and Statistics: April 21-23, La Palma, Canary Islands, ed. Neil Lawrence and Mark Girolami, JMLR Workshop and Conference Proceedings 22:1221-1229. en_US
dc.identifier.issn 1938-7228
dc.identifier.uri http://nrs.harvard.edu/urn-3:HUL.InstRepos:8712189
dc.description.abstract One approach to modeling structured discrete data is to describe the probability of states via an energy function and Gibbs distribution. A recurring difficulty in these models is the computation of the partition function, which may require an intractable sum. However, in many such models, the mode can be found efficiently even when the partition function is unavailable. Recent work on Perturb-and-MAP (PM) models (Papandreou and Yuille, 2011) has exploited this discrepancy to approximate the Gibbs distribution for Markov random fields (MRFs). Here, we explore a broader class of models, called Randomized Optimum models (RandOMs), which include PM as a special case. This new class of models encompasses not only MRFs, but also other models that have intractable partition functions yet permit efficient mode-finding, such as those based on bipartite matchings, shortest paths, or connected components in a graph. We develop likelihood-based learning algorithms for RandOMs, which, empirical results indicate, can produce better models than PM. en_US
dc.description.sponsorship Engineering and Applied Sciences en_US
dc.language.iso en_US en_US
dc.publisher Microtome Publishing en_US
dc.relation.isversionof http://jmlr.csail.mit.edu/proceedings/papers/v22/tarlow12b/tarlow12b.pdf en_US
dash.license OAP
dc.title Randomized Optimum Models for Structured Prediction en_US
dc.type Journal Article en_US
dc.description.version Accepted Manuscript en_US
dc.relation.journal Proceedings of the Fifteenth Conference on Artificial Intelligence and Statistics en_US
dash.depositing.author Adams, Ryan Prescott
dc.date.available 2012-05-10T13:25:10Z

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  • FAS Scholarly Articles [7106]
    Peer reviewed scholarly articles from the Faculty of Arts and Sciences of Harvard University

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