Hierarchical maximum entropy density estimation

Miroslav Dudik, David M. Blei, Robert E. Schapire

Research output: Contribution to conferencePaper

9 Scopus citations

Abstract

We study the problem of simultaneously estimating several densities where the datasets are organized into overlapping groups, such as a hierarchy. For this problem, we propose a maximum entropy formulation, which systematically incorporates the groups and allows us to share the strength of prediction across similar datasets. We derive general performance guarantees, and show how some previous approaches, such as hierarchical shrinkage and hierarchical priors, can be derived as special cases. We demonstrate the proposed technique on synthetic data and in a real-world application to modeling the geographic distributions of species hierarchically grouped in a taxonomy. Specifically, we model the geographic distributions of species in the Australian wet tropics and Northeast New South Wales. In these regions, small numbers of samples per species significantly hinder effective prediction. Substantial benefits are obtained by combining information across taxonomic groups.

Original languageEnglish (US)
Pages249-256
Number of pages8
DOIs
StatePublished - Aug 23 2007
Event24th International Conference on Machine Learning, ICML 2007 - Corvalis, OR, United States
Duration: Jun 20 2007Jun 24 2007

Other

Other24th International Conference on Machine Learning, ICML 2007
CountryUnited States
CityCorvalis, OR
Period6/20/076/24/07

All Science Journal Classification (ASJC) codes

  • Software
  • Human-Computer Interaction
  • Computer Vision and Pattern Recognition
  • Computer Networks and Communications

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    Dudik, M., Blei, D. M., & Schapire, R. E. (2007). Hierarchical maximum entropy density estimation. 249-256. Paper presented at 24th International Conference on Machine Learning, ICML 2007, Corvalis, OR, United States. https://doi.org/10.1145/1273496.1273528