Statistical Learning
ReHLine: Regularized Composite ReLU-ReHU Loss Minimization with Linear Computation and Linear Convergence Ben Dai
Empirical risk minimization (ERM) is a crucial framework that offers a general approach to handling a broad range of machine learning tasks.
6a4262293ca91c5af2dfab24bd343b43-Supplemental-Conference.pdf
By combining robust regression and prior information, we develop an effective robust regression method that can resist adaptive adversarial attacks. Due to the widespread existence of noise and data corruption, it is necessary to recover the true regression parameters when a certain proportion of the response variables have been corrupted. Methods to overcome this problem often involve robust least-squaresregression.
6a4262293ca91c5af2dfab24bd343b43-Paper-Conference.pdf
By combining robust regression and prior information, we develop an effective robust regression method that can resist adaptive adversarial attacks. Due to the widespread existence of noise and data corruption, it is necessary to recover the true regression parameters when a certain proportion of the response variables have been corrupted. Methods to overcome this problem often involve robust least-squaresregression.
