Abstract
We consider the problem of learning Relational Logistic Regression (RLR). Unlike standard logistic regression, the features of RLR are first-order formulae with associated weight vectors instead of scalar weights. We turn the problem of learning RLR to learning these vector-weighted formulae and develop a learning algorithm based on the recently successful functional-gradient boosting methods for probabilistic logic models. We derive the functional gradients and show how weights can be learned simultaneously in an efficient manner. Our empirical evaluation on standard data sets demonstrates the superiority of our approach over other methods for learning RLR.
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We use the term example to mean the grounded target literal. Hence \(y_i = 1\) denotes that the grounding \(\mathsf {Q}(\mathbf {X})=1\) i.e., the grounded target predicate is true. Following standard Bayesian networks terminology, we denote the parents \(\mathcal {A}(\mathsf {Q})\) to include the set of formulae \(\psi \) that influence the current predicate \(\mathsf {Q}\).
We use formulae and clauses interchangeably from hereon.
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Acknowledgements
SN & NR gratefully acknowledge AFOSR award FA9550-18-1-0462 and the support of relationalAI. We thank the anonymous reviewers for their insightful comments and in significantly improving the paper. Any opinions, findings, and conclusions, or recommendations expressed in this material are those of the authors and do not necessarily reflect the view of the AFOSR, or the US government.
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Ramanan, N., Kunapuli, G., Khot, T. et al. Structure learning for relational logistic regression: an ensemble approach. Data Min Knowl Disc 35, 2089–2111 (2021). https://doi.org/10.1007/s10618-021-00770-8
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DOI: https://doi.org/10.1007/s10618-021-00770-8