AISTATS 2019poster39 citations
Database Alignment with Gaussian Features
Osman E. Dai, Daniel Cullina, Negar Kiyavash
Abstract
We consider the problem of aligning a pair of databases with jointly Gaussian features. We consider two algorithms, complete database alignment via MAP estimation among all possible database alignments, and partial alignment via a thresholding approach of log likelihood ratios. We derive conditions on mutual information between feature pairs, identifying the regimes where the algorithms are guaranteed to perform reliably and those where they cannot be expected to succeed.
BibTeX
@InProceedings{pmlr-v89-dai19b,
title = {Database Alignment with Gaussian Features},
author = {Dai, Osman E. and Cullina, Daniel and Kiyavash, Negar},
booktitle = {Proceedings of the Twenty-Second International Conference on Artificial Intelligence and Statistics},
pages = {3225--3233},
year = {2019},
editor = {Chaudhuri, Kamalika and Sugiyama, Masashi},
volume = {89},
series = {Proceedings of Machine Learning Research},
month = {16--18 Apr},
publisher = {PMLR},
pdf = {http://proceedings.mlr.press/v89/dai19b/dai19b.pdf},
url = {https://proceedings.mlr.press/v89/dai19b.html},
abstract = {We consider the problem of aligning a pair of databases with jointly Gaussian features. We consider two algorithms, complete database alignment via MAP estimation among all possible database alignments, and partial alignment via a thresholding approach of log likelihood ratios. We derive conditions on mutual information between feature pairs, identifying the regimes where the algorithms are guaranteed to perform reliably and those where they cannot be expected to succeed.}
}