CiteSeer: an automatic citation indexing system
Proceedings of the third ACM conference on Digital libraries
A threshold of ln n for approximating set cover
Journal of the ACM (JACM)
Approximation algorithms
Computers and Intractability: A Guide to the Theory of NP-Completeness
Computers and Intractability: A Guide to the Theory of NP-Completeness
Automating the Construction of Internet Portals with Machine Learning
Information Retrieval
Mini-buckets: A general scheme for bounded inference
Journal of the ACM (JACM)
CCS expressions, finite state processes, and three problems of equivalence
PODC '83 Proceedings of the second annual ACM symposium on Principles of distributed computing
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Introduction to Statistical Relational Learning (Adaptive Computation and Machine Learning)
Exploiting shared correlations in probabilistic databases
Proceedings of the VLDB Endowment
MPE and partial inversion in lifted probabilistic variable elimination
AAAI'06 proceedings of the 21st national conference on Artificial intelligence - Volume 2
Lifted probabilistic inference with counting formulas
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
Lifted first-order belief propagation
AAAI'08 Proceedings of the 23rd national conference on Artificial intelligence - Volume 2
First-order probabilistic inference
IJCAI'03 Proceedings of the 18th international joint conference on Artificial intelligence
Lifted first-order probabilistic inference
IJCAI'05 Proceedings of the 19th international joint conference on Artificial intelligence
SPOOK: a system for probabilistic object-oriented knowledge representation
UAI'99 Proceedings of the Fifteenth conference on Uncertainty in artificial intelligence
PrDB: managing and exploiting rich correlations in probabilistic databases
The VLDB Journal — The International Journal on Very Large Data Bases
Increasing representational power and scaling reasoning in probabilistic databases
Proceedings of the 13th International Conference on Database Theory
Computing query probability with incidence algebras
Proceedings of the twenty-ninth ACM SIGMOD-SIGACT-SIGART symposium on Principles of database systems
Speeding up inference in statistical relational learning by clustering similar query literals
ILP'09 Proceedings of the 19th international conference on Inductive logic programming
Learning statistical models from relational data
Proceedings of the 2011 ACM SIGMOD International Conference on Management of data
Decision-theoretic planning with generalized first-order decision diagrams
Artificial Intelligence
Efficient sequential clamping for lifted message passing
KI'11 Proceedings of the 34th Annual German conference on Advances in artificial intelligence
Multi-evidence lifted message passing, with application to PageRank and the Kalman filter
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Two
Lifted variable elimination: decoupling the operators from the constraint language
Journal of Artificial Intelligence Research
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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 first-order model is given as input to the system. Here, we describe lifted inference algorithms that determine symmetries and automatically lift the probabilistic model to speedup inference. In particular, we describe approximate lifted inference techniques that allow the user to trade off inference accuracy for computational efficiency by using a handful of tunable parameters, while keeping the error bounded. Our algorithms are closely related to the graph-theoretic concept of bisimulation. We report experiments on both synthetic and real data to show that in the presence of symmetries, run-times for inference can be improved significantly, with approximate lifted inference providing orders of magnitude speedup over ground inference.