Non-Asymptotic Analysis of Relational Learning with One Network
Proceedings of the Seventeenth International Conference on Artificial Intelligence and Statistics, PMLR 33:320-327, 2014.
This theoretical paper is concerned with a rigorous non-asymptotic analysis of relational learning applied to a single network. Under suitable and intuitive conditions on features and clique dependencies over the network, we present the first probably approximately correct (PAC) bound for maximum likelihood estimation (MLE). To our best knowledge, this is the first sample complexity result of this problem. We propose a novel combinational approach to analyze complex dependencies of relational data, which is crucial to our non-asymptotic analysis. The consistency of MLE under our conditions is also proved as the consequence of our sample complexity bound. Finally, our combinational method for analyzing dependent data can be easily generalized to treat other generalized maximum likelihood estimators for relational learning.