An intelligent agent interacting with the real world will encounter individual people, courses,
test results, drugs prescriptions, chairs, boxes, etc., and needs to reason about properties of …

Lifted graphical models provide a language for expressing dependencies between different
types of entities, their attributes, and their diverse relations, as well as techniques for …

Many AI problems arising in a wide variety of fields such as machine learning, semantic
web, network communication, computer vision, and robotics can elegantly be encoded and …

Judging by the increasing impact of machine learning on large-scale data analysis in the
last decade, one can anticipate a substantial growth in diversity of the machine learning …

Lifted probabilistic inference algorithms exploit regularities in the structure of graphical
models to perform inference more efficiently. More specifically, they identify groups of …

Recent advances in the area of lifted inference, which exploits the structure inherent in
relational probabilistic models. Statistical relational AI (StaRAI) studies the integration of …

Abstract Gaussian Processes (GPs) provide a general and analytically tractable way of
modeling complex time-varying, nonparametric functions. The Automatic Bayesian …

Modeling problems containing a mixture of Boolean and numerical variables is a long-
standing interest of Artificial Intelligence. However, performing inference and learning in …

Tasks such as social network analysis, human behavior recognition, or modeling
biochemical reactions, can be solved elegantly by using the probabilistic inference …

Despite the recent successes of probabilistic programming languages (PPLs) in AI
applications, PPLs offer only limited support for random variables whose distributions …