@article{NguRig19-ML-IJ, author = {Nguembang Fadja, Arnaud and Fabrizio Riguzzi}, title = {Lifted Discriminative Learning of Probabilistic Logic Programs}, journal = {Machine Learning}, publisher = {Springer}, copyright = {Springer}, year = {2019}, 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} }

@article{NguRigBerTru2021-BioDM-IJ, abstract = {With the increase in the size of genomic datasets describing variability in populations, extracting relevant information becomes increasingly useful as well as complex. Recently, computational methodologies such as Supervised Machine Learning and specifically Convolutional Neural Networks have been proposed to make inferences on demographic and adaptive processes using genomic data. Even though it was already shown to be powerful and efficient in different fields of investigation, Supervised Machine Learning has still to be explored as to unfold its enormous potential in evolutionary genomics.}, author = {Nguembang Fadja, Arnaud and Riguzzi, Fabrizio and Bertorelle, Giorgio and Trucchi, Emiliano}, doi = {10.1186/s13040-021-00280-9}, isbn = {1756-0381}, journal = {BioData Mining}, number = {1}, pages = {51}, title = {Identification of natural selection in genomic data with deep convolutional neural network}, volume = {14}, year = {2021} }

@article{NguRigLam21-ML-IJ, author = {Nguembang Fadja, Arnaud and Fabrizio Riguzzi and Evelina Lamma}, title = {Learning Hierarchical Probabilistic Logic Programs}, journal = {Machine Learning}, publisher = {Springer}, copyright = {Springer}, year = {2021}, doi = {10.1007/s10994-021-06016-4}, url = {https://link.springer.com/content/pdf/10.1007/s10994-021-06016-4.pdf}, abstract = { Probabilistic logic programming (PLP) combines logic programs and probabilities. Due to its expressiveness and simplicity, it has been considered as a powerful tool for learning and reasoning in relational domains characterized by uncertainty. Still, learning the parameter and the structure of general PLP is computationally expensive due to the inference cost. We have recently proposed a restriction of the general PLP language called hierarchical PLP (HPLP) in which clauses and predicates are hierarchically organized. HPLPs can be converted into arithmetic circuits or deep neural networks and inference is much cheaper than for general PLP. In this paper we present algorithms for learning both the parameters and the structure of HPLPs from data. We first present an algorithm, called parameter learning for hierarchical probabilistic logic programs (PHIL) which performs parameter estimation of HPLPs using gradient descent and expectation maximization. We also propose structure learning of hierarchical probabilistic logic programming (SLEAHP), that learns both the structure and the parameters of HPLPs from data. Experiments were performed comparing PHIL and SLEAHP with PLP and Markov Logic Networks state-of-the art systems for parameter and structure learning respectively. PHIL was compared with EMBLEM, ProbLog2 and Tuffy and SLEAHP with SLIPCOVER, PROBFOIL+, MLB-BC, MLN-BT and RDN-B. The experiments on five well known datasets show that our algorithms achieve similar and often better accuracies but in a shorter time. }, keywords = {Probabilistic Logic Programming, Distribution Semantics, Arithmetic Circuits, Gradient Descent, Back-propagation}, address = {Berlin, Germany}, scopus = {2-s2.0-85107994928}, volume = {110}, number = {7}, pages = {1637--1693}, isbn = {1573-0565} }

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