Web: http://arxiv.org/abs/2109.04623

Jan. 27, 2022, 2:11 a.m. | Ilias Diakonikolas, Jongho Park, Christos Tzamos

cs.LG updates on arXiv.org arxiv.org

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

More from arxiv.org / cs.LG updates on arXiv.org

Machine Learning Product Manager (Europe, Remote)

@ FreshBooks | Germany

Field Operations and Data Engineer, ADAS

@ Lucid Motors | Newark, CA

Machine Learning Engineer - Senior

@ Novetta | Reston, VA

Analytics Engineer

@ ThirdLove | Remote

Senior Machine Learning Infrastructure Engineer - Safety

@ Discord | San Francisco, CA or Remote

Internship, Data Scientist

@ Everstream Analytics | United States (Remote)