Lloyd, JR and Orbanz, P and Ghahramani, Z and Roy, D Random function priors for exchangeable arrays with applications to graphs and relational data. In: Neural Information Processing Systems, 2012-12-3 to 2012-12-10, South Lake Tahoe, Nevada. (Unpublished)Full text not available from this repository.
A fundamental problem in the analysis of structured relational data like graphs, networks, databases, and matrices is to extract a summary of the common structure underlying relations between individual entities. Relational data are typically encoded in the form of arrays; invariance to the ordering of rows and columns corresponds to exchangeable arrays. Results in probability theory due to Aldous, Hoover and Kallenberg show that exchangeable arrays can be represented in terms of a random measurable function which constitutes the natural model parameter in a Bayesian model. We obtain a ﬂexible yet simple Bayesian nonparametric model by placing a Gaussian process prior on the parameter function. Efﬁcient inference utilises elliptical slice sampling combined with a random sparse approximation to the Gaussian process. We demonstrate applications of the model to network data and clarify its relation to models in the literature, several of which emerge as special cases.
|Item Type:||Conference or Workshop Item (UNSPECIFIED)|
|Divisions:||Div F > Computational and Biological Learning|
|Depositing User:||Cron Job|
|Date Deposited:||09 Dec 2016 17:41|
|Last Modified:||23 Mar 2017 06:44|