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Learning Logical Relations

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If you have a question about this talk, please contact Emli-Mari Nel.

Given some (possibly incomplete) knowledge about features of given a set of objects, it is possible to construct probabilistic models which compress the data by learning an underlying structural organisation (such as a hierarchy). In addition to structural form, however, real-world datasets often exhibit logical constraints which, when discovered, can be exploited to better compress and predict the data. I will discuss the construction of models which capture such latent logical relations, and how this can be integrated into existing structure-discovering models.

This is early work and many things aren’t quite settled yet. Questions are welcome and I’d be grateful for ideas and feedback.

This talk is part of the Inference Group series.

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