# Outlier-Robust Learning of Ising Models Under Dobrushin's Condition

@inproceedings{Diakonikolas2021OutlierRobustLO, title={Outlier-Robust Learning of Ising Models Under Dobrushin's Condition}, author={Ilias Diakonikolas and Daniel M. Kane and Alistair Stewart and Yuxin Sun}, booktitle={Annual Conference Computational Learning Theory}, year={2021} }

We study the problem of learning Ising models satisfying Dobrushin’s condition in the outlierrobust 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 nearoptimal 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…

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