Structure Learning for Relational Logistic Regression: An Ensemble Approach
Published in International Conference on Principles of Knowledge Representation and Reasoning (KR'18), Tempe, AZ, 2018
Recommended citation: N. Ramanan, G. Kunapuli, T. Khot, B. Fatemi, S. M. Kazemi, D. Poole, K. Kersting and S. Natarajan. Structure Learning for Relational Logistic Regression: An Ensemble Approach. Proc. International Conference on Principles of Knowledge Representation and Reasoning (2018). http://gkunapuli.github.io/files/18rlrKR.pdf
We consider the problem of learning Relational Logistic Regression (RLR). Unlike standard logistic regression, the features of RLRs 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 and novel data sets demonstrates the superiority of our approach over other methods for learning RLR.