David Blei smile in Paris (seminar)

Nicolas Chopin just reminded me of a seminar given by David Blei in Paris tomorrow (at 4pm, SMILE seminarINRIA 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:

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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