Stochastic relational models (SRMs) [15] provide a rich family of choices for learning and predicting dyadic data between two sets of entities. The models generalize matrix factor...
Learning probabilistic graphical models from high-dimensional datasets is a computationally challenging task. In many interesting applications, the domain dimensionality is such a...
Empirical risk minimization offers well-known learning guarantees when training and test data come from the same domain. In the real world, though, we often wish to adapt a classi...
John Blitzer, Koby Crammer, Alex Kulesza, Fernando...
: Sequences of data-dependent tasks, each one traversing large data sets, exist in many applications (such as video, image and signal processing applications). Those tasks usually ...
— The rapid increase in IC design complexity and wide-spread use of intellectual-property (IP) blocks have made the so-called mixed-size placement a very important topic in recen...