Structure learning for relational logistic regression: an ensemble approach
By Nandini Ramanan, Gautam Kunapuli, Tushar Khot, Bahare Fatemi, Seyed Mehran Kazemi, David Poole, Kristian Kersting, Sriraam Natarajan. 2018
12 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition
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In Artificial Intelligence
- Sriraam Natarajan
- Kristian Kersting
- Ronald E. Parr
- Kaushik Roy
- Srijita Das
- Luc De Raedt
- David Poole
- Mathias Niepert
In Computer Vision and Pattern Recognition
Citations in the corpus
- Fitted Q-Learning for Relational Domains (2020, methodology): “While previously this idea had been employed in the context of relational classification (Natarajan et al. 2012; Khot et al. 2011; Ramanan et al. 2018), the use of RFGB for value function approximation, requires extending them to regression.”