In this paper we analyze the PAC learning abilities of several simple iterative algorithms for learning linear threshold functions, obtaining both positive and negative results. W...
Distributed representations of words are attractive since they provide a means for measuring word similarity. However, most approaches to learning distributed representations are ...
We consider on-line density estimation with the multivariate Gaussian distribution. In each of a sequence of trials, the learner must posit a mean µ and covariance Σ; the learner...
This paper applies affinity propagation (AP) to develop distributed solutions for routing over networks. AP is a message passing algorithm for unsupervised learning. This paper d...
Manohar Shamaiah, Sang Hyun Lee, Sriram Vishwanath...
Undirected graphs are often used to describe high dimensional distributions. Under sparsity conditions, the graph can be estimated using 1 penalization methods. However, current m...
Shuheng Zhou, John D. Lafferty, Larry A. Wasserman