A Slice Sampler for Restricted Hierarchical Beta Process with Applications to Shared Subspace Learning

Sunil Kumar Gupta, Dinh Q. Phung, Svetha Venkatesh
Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence, PMLR R10:314-323, 2012.

Abstract

Hierarchical beta process has found interesting applications in recent years. In this paper we present a modified hierarchical beta process prior with applications to hierarchical modeling of multiple data sources. The novel use of the prior over a hierarchical factor model allows factors to be shared across different sources. We derive a slice sampler for this model, enabling tractable inference even when the likelihood and the prior over parameters are non-conjugate. This allows the application of the model in much wider contexts without restrictions. We present two different data generative models a linear GaussianGaussian model for real valued data and a linear Poisson-gamma model for count data. Encouraging transfer learning results are shown for two real world applications text modeling and content based image retrieval.

Cite this Paper

BibTeX
@InProceedings{pmlr-vR10-gupta12a, title = {A Slice Sampler for Restricted Hierarchical Beta Process with Applications to Shared Subspace Learning}, author = {Gupta, Sunil Kumar and Phung, Dinh Q. and Venkatesh, Svetha}, booktitle = {Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence}, pages = {314--323}, year = {2012}, editor = {de Freitas, Nando and Murphy, Kevin}, volume = {R10}, series = {Proceedings of Machine Learning Research}, month = {14--18 Aug}, publisher = {PMLR}, pdf = {https://raw.githubusercontent.com/mlresearch/r10/main/assets/gupta12a/gupta12a.pdf}, url = {https://proceedings.mlr.press/r10/gupta12a.html}, abstract = {Hierarchical beta process has found interesting applications in recent years. In this paper we present a modified hierarchical beta process prior with applications to hierarchical modeling of multiple data sources. The novel use of the prior over a hierarchical factor model allows factors to be shared across different sources. We derive a slice sampler for this model, enabling tractable inference even when the likelihood and the prior over parameters are non-conjugate. This allows the application of the model in much wider contexts without restrictions. We present two different data generative models a linear GaussianGaussian model for real valued data and a linear Poisson-gamma model for count data. Encouraging transfer learning results are shown for two real world applications text modeling and content based image retrieval.}, note = {Reissued by PMLR on 04 October 2026.} }
Endnote
%0 Conference Paper %T A Slice Sampler for Restricted Hierarchical Beta Process with Applications to Shared Subspace Learning %A Sunil Kumar Gupta %A Dinh Q. Phung %A Svetha Venkatesh %B Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence %C Proceedings of Machine Learning Research %D 2012 %E Nando de Freitas %E Kevin Murphy %F pmlr-vR10-gupta12a %I PMLR %P 314--323 %U https://proceedings.mlr.press/r10/gupta12a.html %V R10 %X Hierarchical beta process has found interesting applications in recent years. In this paper we present a modified hierarchical beta process prior with applications to hierarchical modeling of multiple data sources. The novel use of the prior over a hierarchical factor model allows factors to be shared across different sources. We derive a slice sampler for this model, enabling tractable inference even when the likelihood and the prior over parameters are non-conjugate. This allows the application of the model in much wider contexts without restrictions. We present two different data generative models a linear GaussianGaussian model for real valued data and a linear Poisson-gamma model for count data. Encouraging transfer learning results are shown for two real world applications text modeling and content based image retrieval. %Z Reissued by PMLR on 04 October 2026.
APA
Gupta, S.K., Phung, D.Q. & Venkatesh, S.. (2012). A Slice Sampler for Restricted Hierarchical Beta Process with Applications to Shared Subspace Learning. Proceedings of the 28th Conference on Uncertainty in Artificial Intelligence, in Proceedings of Machine Learning Research R10:314-323 Available from https://proceedings.mlr.press/r10/gupta12a.html. Reissued by PMLR on 04 October 2026.

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