## David Blei smile in Paris (seminar)

**N**icolas Chopin just reminded me of a seminar given by David Blei in Paris tomorrow (at 4pm, SMILE seminar, INRIA 23 avenue d’Italie, 5th floor, orange room) on ** Stochastic Variational Inference and Scalable Topic Models**, machine learning seminar that I will alas miss, being busy on giving mine at CMU. Here is the abstract:

```
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Probabilistic topic modeling provides a suite of tools for analyzing
large collections of electronic documents. With a collection as
input, topic modeling algorithms uncover its underlying themes and
decompose its documents according to those themes. We can use topic
models to explore the thematic structure of a large collection of
documents or to solve a variety of prediction problems about text.
Topic models are based on hierarchical mixed-membership models,
statistical models where each document expresses a set of components
(called topics) with individual per-document proportions. The
computational problem is to condition on a collection of observed
documents and estimate the posterior distribution of the topics and
per-document proportions. In modern data sets, this amounts to
posterior inference with billions of latent variables.
How can we cope with such data? In this talk I will describe
stochastic variational inference, a general algorithm for
approximating posterior distributions that are conditioned on massive
data sets. Stochastic inference is easily applied to a large class of
hierarchical models, including time-series models, factor models, and
Bayesian nonparametric models. I will demonstrate its application to
topic models fit with millions of articles. Stochastic inference
opens the door to scalable Bayesian computation for modern data
```

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