CSE 574 - Artificial Intelligence II - Autumn 2011
Any of chapters 5-14 except 12 in
Intro to SRL
, Getoor and Taskar (eds.).
Log-linear description logics
, Niepert et al, IJCAI-11.
Probabilistic theorem proving
, Gogate and Domingos, UAI-11.
Coarse-to-fine inference and learning for first-order probabilistic models
, Kiddon and Domingos, AAAI-11.
Tuffy: Scaling up statistical inference in MLNs using an RDBMS
, Niu et al, VLDB-11.
Lifted probabilistic inference by first-order knowledge compilation
, Van den Broeck et al, IJCAI-11.
Lifted relational Kalman filtering
, Choi et al, IJCAI-11.
Lifted inference seen from the other side: The tractable features
, Jha et al, NIPS-10.
Approximate lifted belief propagation
, Singla et al, StarAI-10.
Constraint processing in lifted probabilistic inference
, Kisyinski and Poole, UAI-09.
First-order probabilistic inference
, David Poole, IJCAI-03.
General-Purpose MCMC Inference over Relational Structures
, Brian Milch and Stuart Russell, UAI-06.
Distributed Parallel Inference on Large Factor Graphs
, Gonzalez et al, UAI-09.
Cutting Plane MAP Inference for Markov Logic
, Sebastian Riedel, SRL-09.
A framework for incorporating general domain knowledge into latent Dirichlet allocation using first-order logic
, Andrzejewski et al, IJCAI-11.
Generative structure learning for MLNs based on graph of predicates
, Dinh et al, IJCAI-11.
Relational learning with one network
, Xiang and Neville, AIStats-11.
Learning causal models of relational domains
, Maier et al, AAAI-10.
Learning annotated hierarchies from relational data
, Roy et al, NIPS-07.
Modeling relational data using Bayesian clustered tensor factorization
, Sutskever et al, NIPS-09.
Learning Systems of Concepts with an Infinite Relational Model
, Kemp et al, AAAI-06.
Learning Markov Logic Networks Using Structural Motifs
, Stanley Kok and Pedro Domingos, ICML-10.
Discriminative Structure and Parameter Learning for Markov Logic Networks
, Tuyen Huynh and Raymond Mooney, ICML-08.
A language for relational decision theory
, Nath and Domingos, SRL-09. (Related:
Efficient belief propagation for utility maximization and repeated inference
Relational Reinforcement Learning
, Dzeroski, De Raedt and Driessens, ML 43.
Policy Transfer via Markov Logic Networks
, Lisa Torrey and Jude Shavlik, ILP-09.
Symbolic Dynamic Programming for First-order POMDPs
, Scott Sanner and Kristian Kersting, AAAI-10.
Recursive random fields
, Lowd and Domingos, IJCAI-07.
Hybrid Markov Logic Networks
, Jue Wang and Pedro Domingos, AAAI-08.
Markov Logic in Infinite Domains
, Parag Singla and Pedro Domingos, UAI-07.
A probabilistic-logical framework for ontology matching
, Niepert et al, AAAI-10.
Unsupervised semantic parsing
, Poon and Domingos, EMNLP-09.
Event modeling and recognition using MLNs
, Tran and Davis, ECCV-08.
Joint unsupervised coreference resolution with Markov logic
, Poon and Domingos, EMNLP-08.
Extracting semantic networks from text via relational clustering
, Kok and Domingos, ECML-08.
Automatically refining the Wikipedia infobox ontology
, Fei and Weld, WWW-08.
Joint inference in information extraction
, Poon and Domingos, AAAI-07.
Entity resolution with Markov logic
, Singla and Domingos, ICDM-06.
Machine Reading: A ``Killer App" for Statistical Relational AI
, Hoifung Poon and Pedro Domingos, StarAI-10.
Integrating Multiple Learning Components Through Markov Logic
, Thomas Dietterich and Xinlong Bao, AAAI-08.
Markov Logic Improves Protein β-Partners Prediction
, Marco Lippi and Paolo Frasconi, MLG-08.
Scaling Textual Inference to the Web
, Stefan Schoenmackers, Oren Etzioni and Daniel Weld, EMNLP-08.
Other NLP applications of MLNs by Sebastian Riedel et al (see
on Alchemy site).
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