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ICML
2010
IEEE
15 years 20 days ago
Learning Programs: A Hierarchical Bayesian Approach
We are interested in learning programs for multiple related tasks given only a few training examples per task. Since the program for a single task is underdetermined by its data, ...
Percy Liang, Michael I. Jordan, Dan Klein
CORR
2011
Springer
174views Education» more  CORR 2011»
14 years 3 months ago
Parameter Learning of Logic Programs for Symbolic-Statistical Modeling
We propose a logical/mathematical framework for statistical parameter learning of parameterized logic programs, i.e. de nite clause programs containing probabilistic facts with a ...
Yoshitaka Kameya, Taisuke Sato
AAAI
2008
15 years 1 months ago
Revising Imprecise Probabilistic Beliefs in the Framework of Probabilistic Logic Programming
Probabilistic logic programming is a powerful technique to represent and reason with imprecise probabilistic knowledge. A probabilistic logic program (PLP) is a knowledge base whi...
Anbu Yue, Weiru Liu
LPNMR
2005
Springer
15 years 5 months ago
Revisiting the Semantics of Interval Probabilistic Logic Programs
Two approaches to logic programming with probabilities emerged over time: bayesian reasoning and probabilistic satisfiability (PSAT). The attractiveness of the former is in tying ...
Alex Dekhtyar, Michael I. Dekhtyar
JMLR
2012
13 years 2 months ago
Markov Logic Mixtures of Gaussian Processes: Towards Machines Reading Regression Data
We propose a novel mixtures of Gaussian processes model in which the gating function is interconnected with a probabilistic logical model, in our case Markov logic networks. In th...
Martin Schiegg, Marion Neumann, Kristian Kersting