Outlier-Robust Learning of Ising Models Under Dobrushin's Condition
Machine Learning
2021-02-04 v1 Data Structures and Algorithms
Probability
Statistics Theory
Machine Learning
Statistics Theory
Abstract
We study the problem of learning Ising models satisfying Dobrushin's condition in the outlier-robust setting where a constant fraction of the samples are adversarially corrupted. Our main result is to provide the first computationally efficient robust learning algorithm for this problem with near-optimal error guarantees. Our algorithm can be seen as a special case of an algorithm for robustly learning a distribution from a general exponential family. To prove its correctness for Ising models, we establish new anti-concentration results for degree- polynomials of Ising models that may be of independent interest.
Cite
@article{arxiv.2102.02171,
title = {Outlier-Robust Learning of Ising Models Under Dobrushin's Condition},
author = {Ilias Diakonikolas and Daniel M. Kane and Alistair Stewart and Yuxin Sun},
journal= {arXiv preprint arXiv:2102.02171},
year = {2021}
}