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Lifted probabilistic inference
 In
, 2012
"... Abstract. 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 solved using probabilistic graphical models. Often, however, we are facing inference problems with symmetries and r ..."
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Cited by 18 (5 self)
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complete) overview and invitation to the emerging field of lifted probabilistic inference, inference techniques that exploit these symmetries in graphical models in order to speed up inference, ultimately orders of magnitude. 1
Lifted probabilistic inference with counting formulas
 Proceedings of the TwentyThird AAAI Conference on Artificial Intelligence (AAAI2008
, 2008
"... Lifted inference algorithms exploit repeated structure in probabilistic models to answer queries efficiently. Previous work such as de Salvo Braz et al.’s firstorder variable elimination (FOVE) has focused on the sharing of potentials across interchangeable random variables. In this paper, we also ..."
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Cited by 72 (11 self)
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Lifted inference algorithms exploit repeated structure in probabilistic models to answer queries efficiently. Previous work such as de Salvo Braz et al.’s firstorder variable elimination (FOVE) has focused on the sharing of potentials across interchangeable random variables. In this paper, we also
Constraint processing in lifted probabilistic inference
 In Proceedings of 25th Conference on Uncertainty in Artificial Intelligence (UAI09), 2009. Jacek Kisyński and
"... Firstorder probabilistic models combine representational power of firstorder logic with graphical models. There is an ongoing effort to design lifted inference algorithms for firstorder probabilistic models. We analyze lifted inference from the perspective of constraint processing and, through th ..."
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Cited by 18 (0 self)
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Firstorder probabilistic models combine representational power of firstorder logic with graphical models. There is an ongoing effort to design lifted inference algorithms for firstorder probabilistic models. We analyze lifted inference from the perspective of constraint processing and, through
Lifted Probabilistic Inference: An MCMC Perspective
"... The general consensus seems to be that lifted inference is concerned with exploiting model symmetries and grouping indistinguishable objects at inference time. Since firstorder probabilistic formalisms are essentially template languages providing a more compact representation of a corresponding gro ..."
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Cited by 4 (1 self)
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The general consensus seems to be that lifted inference is concerned with exploiting model symmetries and grouping indistinguishable objects at inference time. Since firstorder probabilistic formalisms are essentially template languages providing a more compact representation of a corresponding
Exploiting Logical Structure in Lifted Probabilistic Inference
, 2010
"... Representations that combine firstorder logic and probability have been the focus of much recent research. Lifted inference algorithms for them avoid grounding out the domain, bringing benefits analogous to those of resolution theorem proving in firstorder logic. However, all lifted probabilistic ..."
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Cited by 8 (2 self)
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Representations that combine firstorder logic and probability have been the focus of much recent research. Lifted inference algorithms for them avoid grounding out the domain, bringing benefits analogous to those of resolution theorem proving in firstorder logic. However, all lifted probabilistic
Reasoning about Large Populations with Lifted Probabilistic Inference
"... We use a concrete problem in the context of planning meetings to show how lifted probabilistic inference can dramatically speed up reasoning. We also extend lifted inference to deal with cardinality potentials, and examine how to deal with background knowledge about a social network. Lifted inferenc ..."
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We use a concrete problem in the context of planning meetings to show how lifted probabilistic inference can dramatically speed up reasoning. We also extend lifted inference to deal with cardinality potentials, and examine how to deal with background knowledge about a social network. Lifted
Lifted Probabilistic Inference by FirstOrder Knowledge Compilation
 In Proceedings of the 22nd International Joint Conference on Artificial Intelligence
, 2011
"... Probabilistic logical languages provide powerful formalisms for knowledge representation and learning. Yet performing inference in these languages is extremely costly, especially if it is done at the propositional level. Lifted inference algorithms, which avoid repeated computation by treating i ..."
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Cited by 34 (11 self)
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Probabilistic logical languages provide powerful formalisms for knowledge representation and learning. Yet performing inference in these languages is extremely costly, especially if it is done at the propositional level. Lifted inference algorithms, which avoid repeated computation by treating
Aggregation and Constraint Processing in Lifted Probabilistic Inference
, 2010
"... Representations that mix graphical models and firstorder logic—called either firstorder or relational probabilistic models—were proposed nearly twenty years ago and many more have since emerged. In these models, random variables are parameterized by logical variables. One way to perform inference i ..."
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propositionalizing. An exact lifted inference procedure for firstorder probabilistic models was developed by Poole [2003] and later extended to a broader range of problems by de Salvo Braz et al. [2007]. The CFOVE algorithm by Milch et al. [2008] expanded the scope of lifted inference and is currently the state
Lifted Probabilistic Inference: A Guide for the Database Researcher
"... Modern knowledge bases such as Yago [14], DeepDive [19], and Google’s Knowledge Vault [6] are constructed from large corpora of text by using some form of supervised information extraction. The extracted data usually starts as a large probabilistic database, then its accuracy is improved by adding d ..."
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Modern knowledge bases such as Yago [14], DeepDive [19], and Google’s Knowledge Vault [6] are constructed from large corpora of text by using some form of supervised information extraction. The extracted data usually starts as a large probabilistic database, then its accuracy is improved by adding
Lifted Relax, Compensate and then Recover: From Approximate to Exact Lifted Probabilistic Inference
"... We propose an approach to lifted approximate inference for firstorder probabilistic models, such as Markov logic networks. It is based on performing exact lifted inference in a simplified firstorder model, which is found by relaxing firstorder constraints, and then compensating for the relaxation ..."
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We propose an approach to lifted approximate inference for firstorder probabilistic models, such as Markov logic networks. It is based on performing exact lifted inference in a simplified firstorder model, which is found by relaxing firstorder constraints, and then compensating
Results 1  10
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