ReLU Regression with Massart Noise. (arXiv:2109.04623v2 [cs.LG] UPDATED)

We study the fundamental problem of ReLU regression, where the goal is to fit
Rectified Linear Units (ReLUs) to data. This supervised learning task is
efficiently solvable in the realizable setting, but is known to be
computationally hard with adversarial label noise. In this work, we focus on
ReLU regression in the Massart noise model, a natural and well-studied
semi-random noise model. In this model, the label of every point is generated
according to a function in the class, but …

arxiv noise regression relu