We propose a sequential randomized algorithm, which at each step concentrates on functions having both low risk and low variance with respect to the previous step prediction functi...
It has been shown that the problem of 1-penalized least-square regression commonly referred to as the Lasso or Basis Pursuit DeNoising leads to solutions that are sparse and there...
Abstract. Positioned at the confluence between human/machine and hardware/software integration and backed by a solid proof of concept realized through several scenarios encompassin...
Selection of an optimal estimator typically relies on either supervised training samples (pairs of measurements and their associated true values), or a prior probability model for...
In extraordinary situations, certain individuals may require access to information for which they are not normally authorized. For example, to facilitate rescue of people trapped ...
Cynthia E. Irvine, Timothy E. Levin, Paul C. Clark...