Jan Van Haaren

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The structure of a Markov network is typically learned in one of two ways. The first approach is to treat this task as a global search problem. However, these algorithms are slow as they require running the expensive operation of weight (i.e., parameter) learning many times. The second approach involves learning a set of local models and then combining them(More)
Markov logic networks (MLNs) are a well-known statistical relational learning formalism that combines Markov networks with first-order logic. MLNs attach weights to formulas in first-order logic. Learning MLNs from data is a challenging task as it requires searching through the huge space of possible theories. Additionally, evaluating a theory’s likelihood(More)
Markov logic networks (MLNs) are a popular statistical relational learning formalism that combine Markov networks with first-order logic. Unfortunately, inference and maximum-likelihood learning with MLNs is highly intractable. For inference, this problem is addressed by lifted algorithms, which speed up inference by exploiting symmetries. State-of-the-art(More)
Software patterns are widely adopted to manage the rapidly increasing complexity of software. Despite their popularity, applying software patterns in a software model remains a time-consuming and errorprone manual task. In this paper, we argue that the relational nature of both software models and software patterns can be exploited to automate this(More)
This paper proposes a relational-learning based approach for discovering strategies in volleyball matches based on optical tracking data. In contrast to most existing methods, our approach permits discovering patterns that account for both spatial (that is, partial configurations of the players on the court) and temporal (that is, the order of events and(More)
Network medicine is an emerging paradigm for studying the co-occurrence between diseases. While diseases are often interlinked through complex patterns, most of the existing work in this area has focused on studying pairwise relationships between diseases. In this paper, we use a state-of-the-art Markov network learning method to learn interactions between(More)