Probabilistic relational models are an efficient way to learn and represent the dynamics in realistic environments consisting of many objects. Autonomous intelligent agents that gr...
The task of aligning sequences arises in many applications. Classical dynamic programming approaches require the explicit state enumeration in the reward model. This is often impr...
Andreas Karwath, Kristian Kersting, Niels Landwehr
Collaborative filtering-based recommender systems, which automatically predict preferred products of a user using known preferences of other users, have become extremely popular ...
Many applications involve a set of prediction tasks that must be accomplished sequentially through user interaction. If the tasks are interdependent, the order in which they are p...
In this paper, we study a new research problem of causal discovery from streaming features. A unique characteristic of streaming features is that not all features can be available ...