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We propose a method for jointly learning multiple related matrices, and show that, by sharing information between the two matrices, such an approach allows us to improve predictive performances for items where one of the matrices contains very sparse, or no, information. While the above justificatio ...

Distributions over exchangeable matrices with infinitely many columns are useful in constructing nonparametric latent variable models. However, the distribution implied by such models over the number of features exhibited by each data point may be poorly-suited for many modeling tasks. In this paper ...