We study sparse principal components analysis in the high-dimensional setting, where p (the number of variables) can be much larger than n (the number of observations). We prove o...
In this paper we propose a new approach for semi-supervised structured output learning. Our approach uses relaxed labeling on unlabeled data to deal with the combinatorial nature ...
Paramveer S. Dhillon, S. Sathiya Keerthi, Kedar Be...
This paper proposes a novel Bayesian approximation inference method for mixture modeling. Our key idea is to factorize marginal log-likelihood using a variational distribution ove...
Programming distributed data-intensive web and mobile applications is gratuitously hard. As the world is moving more and more towards the software as services model, we have to co...
Abstract. Knowledge discovery is a time-consuming and space intensive endeavor. By distributing such an endeavor, we can diminish both time and space. System INDEDpronounced indee...
Jennifer Seitzer, James P. Buckley, Yi Pan, Lee A....