We present a general framework for defining priors on model structure and sampling from the posterior using the Metropolis-Hastings algorithm. The key ideas are that structure pri...
We consider the general problem of learning from labeled and unlabeled data, which is often called semi-supervised learning or transductive inference. A principled approach to sem...
Dengyong Zhou, Olivier Bousquet, Thomas Navin Lal,...
In this paper, we study weak {proximity drawings. All known algorithms that compute (weak) proximity drawings produce representations whose area increases exponentiallywith the nu...
We present and evaluate two ranking-and-selection procedures for use in steady-state simulation experiments when the goal is to find which among a finite number of alternative sys...
David Goldsman, William S. Marshall, Seong-Hee Kim...
Message passing overhead is often a substantial source of runtime overhead in object-oriented applications. To combat this performance problem, a number of techniques have been de...