DC Field | Value | Language |
---|---|---|
dc.contributor.author | Ayed, Fadhel | ko |
dc.contributor.author | Lee, Juho | ko |
dc.contributor.author | Caron, François | ko |
dc.date.accessioned | 2020-07-22T02:55:19Z | - |
dc.date.available | 2020-07-22T02:55:19Z | - |
dc.date.created | 2020-07-20 | - |
dc.date.created | 2020-07-20 | - |
dc.date.issued | 2019-06-12 | - |
dc.identifier.citation | International Conference on Machine Learning(ICML 2019) | - |
dc.identifier.uri | http://hdl.handle.net/10203/275594 | - |
dc.description.abstract | Bayesian nonparametric approaches, in particular the Pitman-Yor process and the associated twoparameter Chinese Restaurant process, have been successfully used in applications where the data exhibit a power-law behavior. Examples include natural language processing, natural images or networks. There is also growing empirical evidence suggesting that some datasets exhibit a tworegime power-law behavior: one regime for small frequencies, and a second regime, with a different exponent, for high frequencies. In this paper, we introduce a class of completely random measures which are doubly regularly-varying. Contrary to the Pitman-Yor process, we show that when completely random measures in this class are normalized to obtain random probability measures and associated random partitions, such partitions exhibit a double power-law behavior. We present two general constructions and discuss in particular two models within this class: the beta prime process (Broderick et al. (2015, 2018) and a novel process called generalized BFRY process. We derive efficient Markov chain Monte Carlo algorithms to estimate the parameters of these models. Finally, we show that the proposed models provide a better fit than the Pitman-Yor process on various datasets. | - |
dc.language | English | - |
dc.publisher | International Conference on Machine Learning | - |
dc.title | Beyond the Chinese restaurant and Pitman-Yor processes: statistical models with double power-law behavior | - |
dc.type | Conference | - |
dc.type.rims | CONF | - |
dc.citation.publicationname | International Conference on Machine Learning(ICML 2019) | - |
dc.identifier.conferencecountry | US | - |
dc.identifier.conferencelocation | Long Beach Convention Center, Long Beach | - |
dc.contributor.localauthor | Lee, Juho | - |
dc.contributor.nonIdAuthor | Ayed, Fadhel | - |
dc.contributor.nonIdAuthor | Caron, François | - |
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