Discriminative Structure Learning of Arithmetic Circuits


Amirmohammad Rooshenas, Daniel Lowd ;
Proceedings of the 19th International Conference on Artificial Intelligence and Statistics, PMLR 51:1506-1514, 2016.


The biggest limitation of probabilistic graphical models is the complexity of inference, which is often intractable. An appealing alternative is to use tractable probabilistic models, such as arithmetic circuits (ACs) and sum-product networks (SPNs), in which marginal and conditional queries can be answered efficiently. In this paper, we present the first discriminative structure learning algorithm for ACs, DACLearn (Discriminative AC Learner). Like previous work on generative structure learning, DACLearn finds a log-linear model with conjunctive features, using the size of an equivalent AC representation as a learning bias. Unlike previous work, DACLearn optimizes conditional likelihood, resulting in a more accurate conditional distribution. DACLearn also learns much more compact ACs than generative methods, since it does not need to represent a consistent distribution over the evidence variables. To ensure efficiency, DACLearn uses novel initialization and search heuristics to drastically reduce the number of feature evaluations required to learn an accurate model. In experiments on 20 benchmark domains, we find that our DACLearn learns models that are more accurate and compact than other tractable generative and discriminative methods.

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