An Instability in Variational Methods for Learning Topic Models

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• Andrea Montanari (Stanford University)
• Tuesday 16 January 2018, 09:00-09:45
• Seminar Room 1, Newton Institute.

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STSW01 - Theoretical and algorithmic underpinnings of Big Data

Topic models are extremely useful to extract latent degrees of freedom form large unlabeled datasets. Variational Bayes algorithms are the approach most commonly used by practitioners to learn topic models. Their appeal lies in the promise of reducing the problem of variational inference to an optimization problem. I will show that, even within an idealized Bayesian scenario, variational methods display an instability that can lead to misleading results. [Based on joint work with Behroz Ghorbani and Hamid Javadi]

This talk is part of the Isaac Newton Institute Seminar Series series.

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