Robust Training for Neural Networks

Designed a novel alternating-optimization based training algorithm for a single hidden layer NN with ReLU activation by breaking the training procedure into 3 stages -sparse recovery for choosing the sign of weights and separate regression procedures for the positive and negative weights. Proved the rates of convergence for this algorithm under mild assumptions on the weights & input.