Sparse Stochastic Inference for Latent Dirichlet allocation

David Blei (Princeton University); David Mimno (Princeton University); Matt Hoffman (Columbia University)

Sparse Stochastic Inference for Latent Dirichlet allocation

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arxiv 1206.6425 v1 pith:JYC3SFKZ submitted 2012-06-27 cs.LG stat.ML

David Mimno (Princeton University) , Matt Hoffman (Columbia University) , David Blei (Princeton University) This is my paper · ORCID

classification cs.LGstat.ML

keywords inferencealgorithmbayesianmodelssparsestochasticallocationanalyze

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We present a hybrid algorithm for Bayesian topic models that combines the efficiency of sparse Gibbs sampling with the scalability of online stochastic inference. We used our algorithm to analyze a corpus of 1.2 million books (33 billion words) with thousands of topics. Our approach reduces the bias of variational inference and generalizes to many Bayesian hidden-variable models.

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High dimensional statistical inference: theoretical development to data analytics
math.ST 2019-08 conditional novelty 1.0 of 10

This chapter surveys asymptotic, projection-based, and discrete-model approaches for high-dimensional mean testing, covariance inference, and count data analysis.

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