  This paper presents SVAM (Sequential Variance-Altered MLE), a unified
framework for learning generalized linear models under adversarial label
corruption in training data. SVAM extends to tasks such as least squares
regression, logistic regression, and gamma regression, whereas many existing
works on learning with label corruptions focus only on least squares
regression. SVAM is based on a novel variance reduction technique that may be
of independent interest and works by iteratively solving weighted MLEs over
variance-altered versions of the GLM objective. SVAM offers provable model
recovery guarantees superior to the state-of-the-art for robust regression even
when a constant fraction of training labels are adversarially corrupted. SVAM
also empirically outperforms several existing problem-specific techniques for
robust regression and classification. Code for SVAM is available at
https://github.com/purushottamkar/svam/
