Dependent normalized random measures

Changyou Chen; Vinayak Rao; Wray Buntine; Yee Whye Teh

Dependent normalized random measures

Changyou Chen, Vinayak Rao, Wray Buntine, Yee Whye Teh

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

Abstract

In this paper we propose two constructions of dependent normalized random measures, a class of nonparametric priors over dependent probability measures. Our constructions, which we call mixed normalized random measures (MNRM) and thinned normalized random measures (TNRM), involve (respectively) weighting and thinning parts of a shared underlying Poisson process before combining them together. We show that both MNRM and TNRM are marginally normalized random measures, resulting in well understood theoretical properties. We develop marginal and slice samplers for both models, the latter necessary for inference in TNRM. In time-varying topic modeling experiments, both models exhibit superior performance over related dependent models such as the hierarchical Dirichlet process and the spatial normalized Gamma process.

Original language	English
Title of host publication	Proceedings of the 30th International Conference on Machine Learning (ICML 2013)
Subtitle of host publication	June 16 – June 21, 2013, Atlanta, Georgia, USA
Editors	Sanjoy Dasgupta, David McAllester
Publisher	International Machine Learning Society (IMLS)
Pages	2006-2014
Number of pages	9
Publication status	Published - 2013
Externally published	Yes
Event	International Conference on Machine Learning 2013 - Atlanta, United States of America Duration: 16 Jun 2013 → 21 Jun 2013 Conference number: 30th https://icml.cc/Conferences/2013/

Conference

Conference	International Conference on Machine Learning 2013
Abbreviated title	ICML 2013
Country	United States of America
City	Atlanta
Period	16/06/13 → 21/06/13
Internet address	https://icml.cc/Conferences/2013/

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Cite this

@inproceedings{d8ab3257639b4c0886fc70401b502341,

title = "Dependent normalized random measures",

abstract = "In this paper we propose two constructions of dependent normalized random measures, a class of nonparametric priors over dependent probability measures. Our constructions, which we call mixed normalized random measures (MNRM) and thinned normalized random measures (TNRM), involve (respectively) weighting and thinning parts of a shared underlying Poisson process before combining them together. We show that both MNRM and TNRM are marginally normalized random measures, resulting in well understood theoretical properties. We develop marginal and slice samplers for both models, the latter necessary for inference in TNRM. In time-varying topic modeling experiments, both models exhibit superior performance over related dependent models such as the hierarchical Dirichlet process and the spatial normalized Gamma process.",

author = "Changyou Chen and Vinayak Rao and Wray Buntine and Teh, {Yee Whye}",

year = "2013",

language = "English",

pages = "2006--2014",

editor = "Sanjoy Dasgupta and David McAllester",

booktitle = "Proceedings of the 30th International Conference on Machine Learning (ICML 2013)",

publisher = "International Machine Learning Society (IMLS)",

note = "International Conference on Machine Learning 2013, ICML 2013 ; Conference date: 16-06-2013 Through 21-06-2013",

url = "https://icml.cc/Conferences/2013/",

}

Chen, C, Rao, V, Buntine, W & Teh, YW 2013, Dependent normalized random measures. in S Dasgupta & D McAllester (eds), Proceedings of the 30th International Conference on Machine Learning (ICML 2013): June 16 – June 21, 2013, Atlanta, Georgia, USA. International Machine Learning Society (IMLS), pp. 2006-2014, International Conference on Machine Learning 2013, Atlanta, United States of America, 16/06/13.

Dependent normalized random measures. / Chen, Changyou; Rao, Vinayak; Buntine, Wray; Teh, Yee Whye.

Proceedings of the 30th International Conference on Machine Learning (ICML 2013): June 16 – June 21, 2013, Atlanta, Georgia, USA. ed. / Sanjoy Dasgupta; David McAllester. International Machine Learning Society (IMLS), 2013. p. 2006-2014.

Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review

TY - GEN

T1 - Dependent normalized random measures

AU - Chen, Changyou

AU - Rao, Vinayak

AU - Buntine, Wray

AU - Teh, Yee Whye

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PY - 2013

Y1 - 2013

N2 - In this paper we propose two constructions of dependent normalized random measures, a class of nonparametric priors over dependent probability measures. Our constructions, which we call mixed normalized random measures (MNRM) and thinned normalized random measures (TNRM), involve (respectively) weighting and thinning parts of a shared underlying Poisson process before combining them together. We show that both MNRM and TNRM are marginally normalized random measures, resulting in well understood theoretical properties. We develop marginal and slice samplers for both models, the latter necessary for inference in TNRM. In time-varying topic modeling experiments, both models exhibit superior performance over related dependent models such as the hierarchical Dirichlet process and the spatial normalized Gamma process.

AB - In this paper we propose two constructions of dependent normalized random measures, a class of nonparametric priors over dependent probability measures. Our constructions, which we call mixed normalized random measures (MNRM) and thinned normalized random measures (TNRM), involve (respectively) weighting and thinning parts of a shared underlying Poisson process before combining them together. We show that both MNRM and TNRM are marginally normalized random measures, resulting in well understood theoretical properties. We develop marginal and slice samplers for both models, the latter necessary for inference in TNRM. In time-varying topic modeling experiments, both models exhibit superior performance over related dependent models such as the hierarchical Dirichlet process and the spatial normalized Gamma process.

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BT - Proceedings of the 30th International Conference on Machine Learning (ICML 2013)

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