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

02/03/2021
by   Ilias Diakonikolas, et al.
5

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.

READ FULL TEXT

Please sign up or login with your details

Forgot password? Click here to reset