Learning Relationships between Data Obtained Independently

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Alexandra Carpentier, Teresa Schlueter ;
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, PMLR 51:658-666, 2016.

Abstract

The aim of this paper is to provide a new method for learning the relationships between data that have been obtained independently. Unlike existing methods like matching, the proposed technique does not require any contextual information, provided that the dependency between the variables of interest is monotone. It can therefore be easily combined with matching in order to exploit the advantages of both methods. This technique can be described as a mix between quantile matching, and deconvolution. We provide for it a theoretical and an empirical validation.

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