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Oracle Variational Inference

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If you have a question about this talk, please contact Dr Jes Frellsen.

Variational inference is a powerful approach to performing Bayesian inference on large datasets with complex probabilistic models. But standard methods assume the data are fixed, which is a limitation when applying inference to modern data sources. In this talk, I will abandon the assumption of fixed data and develop oracle variational inference algorithms that work with a possibly unbounded number of data points. I then explain how to adaptively adjust the data sampling distribution to improve inference. Recent findings, using latent Dirichlet allocation on text corpora where the oracle distribution uses keyword searches to query documents, indicate that both types of oracle variational inference converge faster and to better local optima than stochastic variational inference.

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

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