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Hierarchical Methods of Moments
Matteo Ruffini · Guillaume Rabusseau · Borja Balle

Mon Dec 04 06:30 PM -- 10:30 PM (PST) @ Pacific Ballroom #49

Spectral methods of moments provide a powerful tool for learning the parameters of latent variable models. Despite their theoretical appeal, the applicability of these methods to real data is still limited due to a lack of robustness to model misspecification. In this paper we present a hierarchical approach to methods of moments to circumvent such limitations. Our method is based on replacing the tensor decomposition step used in previous algorithms with approximate joint diagonalization. Experiments on topic modeling show that our method outperforms previous tensor decomposition methods in terms of speed and model quality.

Author Information

Matteo Ruffini (UPC)
Guillaume Rabusseau (Université de Montréal - Mila)
Borja Balle (DeepMind)

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