Learning Relationships between Data Obtained Independently

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.

Cite this Paper

BibTeX
@InProceedings{pmlr-v51-carpentier16b, title = {Learning Relationships between Data Obtained Independently}, author = {Carpentier, Alexandra and Schlueter, Teresa}, booktitle = {Proceedings of the 19th International Conference on Artificial Intelligence and Statistics}, pages = {658--666}, year = {2016}, editor = {Gretton, Arthur and Robert, Christian C.}, volume = {51}, series = {Proceedings of Machine Learning Research}, address = {Cadiz, Spain}, month = {09--11 May}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v51/carpentier16b.pdf}, url = {https://proceedings.mlr.press/v51/carpentier16b.html}, 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.} }
Endnote
%0 Conference Paper %T Learning Relationships between Data Obtained Independently %A Alexandra Carpentier %A Teresa Schlueter %B Proceedings of the 19th International Conference on Artificial Intelligence and Statistics %C Proceedings of Machine Learning Research %D 2016 %E Arthur Gretton %E Christian C. Robert %F pmlr-v51-carpentier16b %I PMLR %P 658--666 %U https://proceedings.mlr.press/v51/carpentier16b.html %V 51 %X 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.
RIS
TY - CPAPER TI - Learning Relationships between Data Obtained Independently AU - Alexandra Carpentier AU - Teresa Schlueter BT - Proceedings of the 19th International Conference on Artificial Intelligence and Statistics DA - 2016/05/02 ED - Arthur Gretton ED - Christian C. Robert ID - pmlr-v51-carpentier16b PB - PMLR DP - Proceedings of Machine Learning Research VL - 51 SP - 658 EP - 666 L1 - http://proceedings.mlr.press/v51/carpentier16b.pdf UR - https://proceedings.mlr.press/v51/carpentier16b.html AB - 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. ER -
APA
Carpentier, A. & Schlueter, T.. (2016). Learning Relationships between Data Obtained Independently. Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, in Proceedings of Machine Learning Research 51:658-666 Available from https://proceedings.mlr.press/v51/carpentier16b.html.

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