Many real-world applications produce networked data such as the world-wide web
(hypertext documents connected via hyperlinks), social networks (for example, people …

The automatic extraction of information from unstructured sources has opened up new
avenues for querying, organizing, and analyzing data by drawing upon the clean semantics …

Markov Logic Networks (MLNs) have emerged as a powerful framework that combines
statistical and logical reasoning; they have been applied to many data intensive problems …

Lifted inference algorithms exploit repeated structure in probabilistic models to answer
queries efficiently. Previous work such as de Salvo Braz et al.'s first-order variable …

There is a growing interest in building probabilistic models with high order potentials
(HOPs), or interactions, among discrete variables. Message passing inference in such …

Many visual recognition problems can be approached by counting instances. To determine
whether an event is present in a long internet video, one could count how many frames …

We study the question of how to make loss-aware predictions in image segmentation
settings where the evaluation function is the Intersection-over-Union (IoU) measure that is …

Exchangeability is a central notion in statistics and probability theory. The assumption that
an infinite sequence of data points is exchangeable is at the core of Bayesian statistics …

Boolean matrix factorization and Boolean matrix completion from noisy observations are
desirable unsupervised data-analysis methods due to their interpretability, but hard to …

We present a number of contributions to the LP relaxation approach to weighted constraint
satisfaction (= Gibbs energy minimization). We link this approach to many works from …