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Collapsed Variational Inference for HDP
Yee Whye Teh · Kenichi Kurihara · Max Welling

Tue Dec 04 11:50 AM -- 12:00 PM (PST) @

A wide variety of Dirichlet-multinomial `topic' models have found interesting applications in recent years. While Gibbs sampling remains an important method of inference in such models, variational techniques have certain advantages such as easy assessment of convergence, easy optimization without the need to maintain detailed balance, a bound on the marginal likelihood, and side-stepping of issues with topic-identifiability. The most accurate variational technique thus far, namely collapsed variational LDA (CV-LDA)\cite{TehNewmanWelling06}, did not deal with model selection nor did it include inference for the hyper-parameters. We generalize their technique to the HDP to address these issues. The result is a collapsed variational inference technique that can add topics indefinitely, for instance through split and merge heuristics, while the algorithm will automatically remove clusters which are not supported by the data. Experiments show a very significant improvement in accuracy relative to CV-LDA.

Author Information

Yee Whye Teh (University of Oxford, DeepMind)

I am a Professor of Statistical Machine Learning at the Department of Statistics, University of Oxford and a Research Scientist at DeepMind. I am also an Alan Turing Institute Fellow and a European Research Council Consolidator Fellow. I obtained my Ph.D. at the University of Toronto (working with Geoffrey Hinton), and did postdoctoral work at the University of California at Berkeley (with Michael Jordan) and National University of Singapore (as Lee Kuan Yew Postdoctoral Fellow). I was a Lecturer then a Reader at the Gatsby Computational Neuroscience Unit, UCL, and a tutorial fellow at University College Oxford, prior to my current appointment. I am interested in the statistical and computational foundations of intelligence, and works on scalable machine learning, probabilistic models, Bayesian nonparametrics and deep learning. I was programme co-chair of ICML 2017 and AISTATS 2010.

Kenichi Kurihara (Google)
Max Welling (Microsoft Research AI4Science / University of Amsterdam)

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