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Stochastic Planning and Lifted Inference
"... The paper argues that (1) stochastic planning should be used as a core problem domain for relational probabilistic models providing problems of interest that are challenging for current approaches and significant scope for extending their capabilities, (2) that symbolic dynamic programming solving s ..."
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such problems can be seen as a prime example of lifted inference in relational probabilistic problems, (3) that first order decision diagrams provide a useful tool to drive such lifted computations, and (4) that the resulting lifted inference is qualitatively different from what other approaches are providing
Lifted Inference Rules with Constraints
"... Lifted inference rules exploit symmetries for fast reasoning in statistical relational models. Computational complexity of these rules is highly dependent on the choice of the constraint language they operate on and therefore coming up with the right kind of representation is critical to the succes ..."
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Lifted inference rules exploit symmetries for fast reasoning in statistical relational models. Computational complexity of these rules is highly dependent on the choice of the constraint language they operate on and therefore coming up with the right kind of representation is critical
Bisimulationbased approximate lifted inference
"... There has been a great deal of recent interest in methods for performing lifted inference; however, most of this work assumes that the firstorder model is given as input to the system. Here, we describe lifted inference algorithms that determine symmetries and automatically lift the probabilistic m ..."
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There has been a great deal of recent interest in methods for performing lifted inference; however, most of this work assumes that the firstorder model is given as input to the system. Here, we describe lifted inference algorithms that determine symmetries and automatically lift the probabilistic
Lifted Inference via kLocality
"... Lifted inference approaches exploit symmetries of a graphical model. So far, only the automorphism group of the graphical model has been proposed to formalize the symmetries used. We show that this is only the GIcomplete tip of a hierarchy and that the amount of lifting depends on how local the inf ..."
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Lifted inference approaches exploit symmetries of a graphical model. So far, only the automorphism group of the graphical model has been proposed to formalize the symmetries used. We show that this is only the GIcomplete tip of a hierarchy and that the amount of lifting depends on how local
Lifted inference for relational continuous models
 In Proc. of the 26th Conference on Uncertainty in Artificial Intelligence (UAI10
, 2010
"... Relational Continuous Models (RCMs) represent joint probability densities over attributes of objects, when the attributes have continuous domains. With relational representations, they can model joint probability distributions over large numbers of variables compactly in a natural way. This paper pr ..."
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presents a new exact lifted inference algorithm for RCMs, thus it scales up to large models of real world applications. The algorithm applies to Relational Pairwise Models which are (relational) products of potentials of arity 2. Our algorithm is unique in two ways. First, it substantially improves
Lifted inference: normalizing loops by evaluation
"... Many loops in probabilistic inference map almost every individual in their domain to the same result. Running such loops symbolically takes time sublinear in the domain size. Using normalization by evaluation with firstclass delimited continuations, we lift inference procedures to reap this speedu ..."
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Many loops in probabilistic inference map almost every individual in their domain to the same result. Running such loops symbolically takes time sublinear in the domain size. Using normalization by evaluation with firstclass delimited continuations, we lift inference procedures to reap this speed
On the complexity and approximation of binary evidence in lifted inference
 In Advances in Neural Information Processing Systems 26 (NIPS
"... Lifted inference algorithms exploit symmetries in probabilistic models to speed up inference. They show impressive performance when calculating unconditional probabilities in relational models, but often resort to nonlifted inference when computing conditional probabilities. The reason is that cond ..."
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Cited by 7 (3 self)
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Lifted inference algorithms exploit symmetries in probabilistic models to speed up inference. They show impressive performance when calculating unconditional probabilities in relational models, but often resort to nonlifted inference when computing conditional probabilities. The reason
Approximate Lifted Inference with Probabilistic Databases
"... This paper proposes a new approach for approximate evaluation of #Phard queries with probabilistic databases. In our approach, every query is evaluated entirely in the database engine by evaluating a fixed number of query plans, each providing an upper bound on the true probability, then taking th ..."
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This paper proposes a new approach for approximate evaluation of #Phard queries with probabilistic databases. In our approach, every query is evaluated entirely in the database engine by evaluating a fixed number of query plans, each providing an upper bound on the true probability, then taking their minimum. We provide an algorithm that takes into account important schema information to enumerate only the minimal necessary plans among all possible plans. Importantly, this algorithm is a strict generalization of all known results of PTIME selfjoinfree conjunctive queries: A query is safe if and only if our algorithm returns one single plan. We also apply three relational query optimization techniques to evaluate all minimal safe plans very fast. We give a detailed experimental evaluation of our approach and, in the process, provide a new way of thinking about the value of probabilistic methods over nonprobabilistic methods for ranking query answers. 1.
Results 1  10
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