Publicaties
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Lifted relax, compensate and then recover: From approximate to exact lifted probabilistic inference KU Leuven
We propose an approach to lifted approximate inference for first-order probabilistic models, such as Markov logic networks. It is based on performing exact lifted inference in a simplified first-order model, which is found by relaxing first-order constraints, and then compensating for the relaxation. These simplified models can be incrementally improved by carefully recovering constraints that have been relaxed, also at the first-order level. ...
The southeast Brazilian rifted continental margin is not a single, continuous upwarp : variations in morphology and denudation patterns along the continental drainage divide Universiteit Gent
Rifted continental margins (RCM) are large-scale features of Earth's surface that show substantial morphological variations. Classical escarpment features are the subject of many studies in these settings while other mor-phologies that characterize this tectonic environment receive less attention. The case of the Brazilian South Atlantic margin, a continental-scale topographically pronounced terrain covering >1000 km of the western South ...
Lifted Probabilistic Inference by Variable Elimination (Gelifte probabilistische inferentie door variabele eliminatie) KU Leuven
Representing, learning, and reasoning about knowledge are central to artificial intelligence (AI). A long standing goal of AI is unifying logic and probability, to benefit from the strengths of both formalisms. Probability theory allows us to represent and reason in uncertain domains, while first-order logic allows us to represent and reason about structured, relational domains. Many real-world problems exhibit both uncertainty and structure, ...
Lifted Inference and Learning in Statistical Relational Models (Eerste-orde inferentie en leren in statistische relationele modellen) KU Leuven
Statistical relational models combine aspects of first-order logic and probabilistic graphical models, enabling them to model complex logical and probabilistic interactions between large numbers of objects. This level of expressivity comes at the cost of increased complexity of inference, motivating a new line of research in lifted probabilistic inference. By exploiting symmetries of the relational structure in the model, and reasoning about ...
Levensverzekeringen en giften Universiteit Gent
Lifted Model Checking for Relational MDPs KU Leuven
Model checking has been developed for verifying the behaviour of systems with stochastic and non-deterministic behavior. It is used to provide guarantees about such systems. While most model checking methods focus on propositional models, various probabilistic planning and reinforcement frameworks deal with relational domains, for instance, STRIPS planning and relational Markov Decision Processes. Using propositional model checking in relational ...
Lifted variable elimination: Decoupling the operators from the constraint language KU Leuven
Lifted probabilistic inference algorithms exploit regularities in the structure of graphical models to perform inference more efficiently. More specifically, they identify groups of interchangeable variables and perform inference once for each group, as opposed to once for each variable. The groups are defined by means of constraints, so the flexibility of the grouping is determined by the expressivity of the constraint language. Existing ...
Towards high-level probabilistic reasoning with lifted inference KU Leuven
High-level representations of uncertainty, such as probabilistic logics and programs, have been around for decades. Lifted inference was initially motivated by the need to make reasoning algorithms high-level as well. While the lifted inference community focused on machine learning applications, the high-level reasoning goal has received less attention recently. We revisit the idea and look at the capabilities of the latest techniques in lifted ...