Relational Learning and Reference Classes
By Michael Chiang
This talk is concerned with two settings for relational learning and their
connection to the 'reference class problem'. In the first setting one aims
to learn first-order generalisations of relational data by abstracting over
individuals. In the second, one explicitly represents and approximates
hidden properties of individuals. Under explicit assumptions about the
underlying generative process, we show why models which abstract over
individuals cannot achieve optimal prediction. We provide empirical evidence
demonstrating that models which capture hidden properties of individuals
achieve greater accuracy, and discuss the potential of such models for
achieving optimal prediction.
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