author = {Nguembang Fadja, Arnaud  and Fabrizio Riguzzi},
  title = {Lifted Discriminative Learning of Probabilistic Logic Programs},
  journal = {Machine Learning},
  publisher = {Springer},
  copyright = {Springer},
  year = {2019},
  url = {},
  doi = {10.1007/s10994-018-5750-0},
  abstract = {
Probabilistic logic programming (PLP) provides a powerful tool for reason- ing with uncertain relational models. However, learning probabilistic logic programs is expensive due to the high cost of inference. Among the proposals to overcome this problem, one of the most promising is lifted inference. In this paper we consider PLP models that are amenable to lifted inference and present an algorithm for performing parameter and structure learning of these models from positive and negative exam- ples. We discuss parameter learning with EM and LBFGS and structure learning with LIFTCOVER, an algorithm similar to SLIPCOVER. The results of the comparison of LIFTCOVER with SLIPCOVER on 12 datasets show that it can achieve solutions of similar or better quality in a fraction of the time.
  keywords = { Statistical Relational Learning, Probabilistic Inductive Logic Program- ming, Probabilistic Logic Programming, Lifted Inference, Expectation Maximization
  scopus = {2-s2.0-85052570852},
  volume = {108},
  number = {7},
  pages = {1111--1135},
  note = {The original publication is available at

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