  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-$2$ polynomials of Ising models that may
be of independent interest.
