Relational Learning
Last update: 07 Dec 2024 23:43First version: 27 April 2012
That is, learning models of mathematical relations and relational structures from data, not learning in a relational manner.
- See also:
- Data Mining
- Graphical Models
- Inferring Networks from Non-Network Data
- Machine Learning, Statistical Inference, and Induction
- Mathematical Logic
- Network Data Analysis
- Projectivity in Statistical Models
- Statistics of Structured Data
- Recommended, big picture:
- Lise Getoor and Ben Taskar (eds.), Introduction to Statistical Relational Learning [Official blurb, Lise's book site with more links]
- Recommended, close-ups:
- Qian Huang, Horace He, Abhay Singh, Ser-Nam Lim, Austin R. Benson, "Combining Label Propagation and Simple Models Out-performs Graph Neural Networks", arxiv:2010.13993
- Ben London, Bert Huang, Lise Getoor, "Graph-based Generalization Bounds for Learning Binary Relations", arxiv:1302.5348
- Ben London, Bert Huang, Ben Taskar, Lise Getoor, "Collective Stability in Structured Prediction: Generalization from One Example", ICML 2013 [My notes]
- Katerina Marazopoulou, Marc Maier and David Jensen, "Learning the Structure of Causal Models with Relational and Temporal Dependence", UAI 2015
- Rongjing Xiang and Jennifer Neville
- "Relational Learning with One Network: An Asymptotic Analysis", AI Stats 2011 [PDF reprint]
- "Understanding Propagation Error and Its Effect on Collective Classification" [PDF reprint via Prof. Neville]
- To read:
- Sheng Gao, Ludovic Denoyer, Patrick Gallinari, "Modeling Relational Data via Latent Factor Blockmodel", arxiv:1204.2581
- Peng He and Changshui Zhang, "Non-Asymptotic Analysis of Relational Learning with One Network", pp. 320--327 in AISTATS 2014
- Katsuhiko Ishiguro, Tomoharu Iwata, Naonori Ueda, Joshua Tenenbaum, "Dynamic Infinite Relational Model for Time-varying Relational Data Analysis" [NIPS 2010]
- Manfred Jaeger, Oliver Schulte, "Inference, Learning, and Population Size: Projectivity for SRL Models", arxiv:1807.00564
- Purushottam Kar, Bharath K Sriperumbudur, Prateek Jain, Harish C Karnick, "On the Generalization Ability of Online Learning Algorithms for Pairwise Loss Functions", arxiv:1305.2505
- Marc Maier, Katerina Marazopoulou, David Jensen, "Reasoning about Independence in Probabilistic Models of Relational Data", arxiv:1302.4381
- M. E. Müller, Relational Knowledge Discovery
- Anon Plangprasopchok, Kristina Lerman, Lise Getoor, "A Probabilistic Approach for Learning Folksonomies from Structured Data", arxiv:1011.3557
- Guillermo Puebla, Jeffrey S. Bowers, "Can Deep Convolutional Neural Networks Learn Same-Different Relations?", bioRxiv 2021.04.06.438551"
- Karl Rohe, Bin Yu, "Co-clustering for Directed Graphs; the Stochastic Co-Blockmodel and a Spectral Algorithm", arxiv:1204.2296
- Ryan A. Rossi, Luke K. McDowell, David W. Aha, Jennifer Neville, "Transforming Graph Representations for Statistical Relational Learning", arxiv:1204.0033
- Ryan A. Rossi, Jennifer Neville, "Representations and Ensemble Methods for Dynamic Relational Classification", arxiv:1111.5312
- Oliver Schulte, Hassan Khosravi, Flavia Moser, Martin Ester, "Learning Class-Level Bayes Nets for Relational Data", arxiv:0811.4458
- Ingo Thon, Niels Landwehr and Luc De Raedt, "Stochastic relational processes: Efficient inference and applications", Machine Learning 82 (2011): 239--272
- Guy Van den Broeck, Kristian Kersting, Sriraam Natarajan, David Poole (eds.), An Introduction to Lifted Probabilistic Inference
- Willem Waegeman, Tapio Pahikkala, Antti Airola, Tapio Salakoski, Michiel Stock, Bernard De Baets, "A kernel-based framework for learning graded relations from data", arxiv:1111.6473
- Yuyi Wang, Jan Ramon, Zheng-Chu Guo
- "Learning from networked examples in a k-partite graph", arxiv:1306.0393
- "Learning from networked examples", arxiv:1405.2600
- Zhao Xu, Volker Tresp, Kai Yu, Hans-Peter Kriegel, "Infinite Hidden Relational Models", UAI 2006, arxiv:1206.6864
- Shuheng Zhou, John Lafferty, Larry Wasserman, "Time Varying Undirected Graphs", arxiv:0802.2758
