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Moment matching for latent variable models: from ICA to LDA and CCA

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If you have a question about this talk, please contact Zoubin Ghahramani .

Moment matching is a traditional alternative to maximum likelihood for parameter estimation in probabilistic models. For certain latent variable models, this has recently led to parameter estimation algorithms with theoretical guarantees. While independent component analysis (ICA) was the first semi-parametric model to be considered twenty years ago, this has been recently extended to latent Dirichlet Allocation (LDA), which is a parametric model for discrete data. In this talk I will present (a) a semi-parametric extension of LDA which, beyond making fewer modelling assumptions, leads to simpler estimation through moment matching techniques, and (b) an extension to multi-view models such as canonical correlation analysis (CCA). (Joint work with Anastasia Podosinnikova and Simon Lacoste-Julien).

This talk is part of the Machine Learning @ CUED series.

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Average on Saturdays: 7540.50909091 kWh McGrath Centre, St Catharine’s College & online Hybrid - Maxwell Centre & on Zoom

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