The effects of random variations during the manufacturing process on devices can be simulated as a variation of transistor parameters. Device degradation, due to temperature or vo...
Udo Sobe, Karl-Heinz Rooch, Andreas Ripp, Michael ...
We present a manifold learning approach to dimensionality
reduction that explicitly models the manifold as a mapping
from low to high dimensional space. The manifold is
represen...
—One key adaptation mechanism often deployed in networking and computing systems is dynamic load balancing. The goal from employing dynamic load balancers is to ensure that the o...
Mina Guirguis, Azer Bestavros, Ibrahim Matta, Yuti...
We propose a novel method of dimensionality reduction for supervised learning. Given a regression or classification problem in which we wish to predict a variable Y from an expla...
Kenji Fukumizu, Francis R. Bach, Michael I. Jordan
We study a sequential variance reduction technique for Monte Carlo estimation of functionals in Markov Chains. The method is based on designing sequential control variates using s...