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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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Gerald Quon · Yee Whye Teh · Esther Chan · Michael Brudno · Tim Hughes · Quaid Morris -
2008 Poster: Dependent Dirichlet Process Spike Sorting »
Jan Gasthaus · Frank Wood · Dilan Gorur · Yee Whye Teh -
2008 Poster: An Efficient Sequential Monte Carlo Algorithm for Coalescent Clustering »
Dilan Gorur · Yee Whye Teh -
2007 Poster: Bayesian Agglomerative Clustering with Coalescents »
Yee Whye Teh · Hal Daumé III · Daniel Roy -
2007 Poster: Cooled and Relaxed Survey Propagation for MRFs »
Hai Leong Chieu · Wee Sun Lee · Yee Whye Teh -
2007 Session: Session 5: Probabilistic Representations and Learning »
Yee Whye Teh -
2007 Spotlight: Cooled and Relaxed Survey Propagation for MRFs »
Hai Leong Chieu · Wee Sun Lee · Yee Whye Teh -
2007 Oral: Bayesian Agglomerative Clustering with Coalescents »
Yee Whye Teh · Hal Daumé III · Daniel Roy -
2007 Spotlight: Distributed Inference for Latent Dirichlet Allocation »
David Newman · Arthur Asuncion · Padhraic Smyth · Max Welling -
2007 Poster: Infinite State Bayes-Nets for Structured Domains »
Max Welling · Ian Porteous · Evgeniy Bart -
2007 Poster: Distributed Inference for Latent Dirichlet Allocation »
David Newman · Arthur Asuncion · Padhraic Smyth · Max Welling -
2007 Spotlight: Infinite State Bayes-Nets for Structured Domains »
Max Welling · Ian Porteous · Evgeniy Bart -
2006 Poster: Structure Learning in Markov Random Fields »
Sridevi Parise · Max Welling -
2006 Poster: Accelerated Variational Dirichlet Process Mixtures »
Kenichi Kurihara · Max Welling · Nikos Vlassis -
2006 Spotlight: Accelerated Variational Dirichlet Process Mixtures »
Kenichi Kurihara · Max Welling · Nikos Vlassis -
2006 Poster: A Collapsed Variational Bayesian Inference Algorithm for Latent Dirichlet Allocation »
Yee Whye Teh · David Newman · Max Welling