Abstract--We give sublinear-time approximation algorithms for some optimization problems arising in machine learning, such as training linear classifiers and finding minimum enclos...
Kenneth L. Clarkson, Elad Hazan, David P. Woodruff
Optimally designing the location of training input points (active learning) and choosing the best model (model selection) are two important components of supervised learning and h...
We consider a two-hop wireless network where a transmitter communicates with a receiver via M relays with an amplify-and-forward (AF) protocol. Recent works have shown that sophis...
We present a new Gaussian Process inference algorithm, called Online Sparse Matrix Gaussian Processes (OSMGP), and demonstrate its merits with a few vision applications. The OSMGP ...
Academic literature has documented for a long time the existence of important price gains in the first trading day of initial public offerings (IPOs). Most of the empirical analys...