no code implementations • 10 Oct 2019 • Yang Liu, David P. Helmbold
This paper considers a variant of the classical online learning problem with expert predictions.
no code implementations • ICML 2018 • Peter L. Bartlett, David P. Helmbold, Philip M. Long
We provide polynomial bounds on the number of iterations for gradient descent to approximate the least squares matrix $\Phi$, in the case where the initial hypothesis $\Theta_1 = ... = \Theta_L = I$ has excess loss bounded by a small enough constant.
no code implementations • 17 Sep 2016 • Holakou Rahmanian, David P. Helmbold, S. V. N. Vishwanathan
We present applications of our framework to online learning of Huffman trees and permutations.
no code implementations • 14 Feb 2016 • David P. Helmbold, Philip M. Long
We analyze dropout in deep networks with rectified linear units and the quadratic loss.
no code implementations • 15 Dec 2014 • David P. Helmbold, Philip M. Long
Dropout is a simple but effective technique for learning in neural networks and other settings.