Background

We encourage everyone interested in topic models to attend the workshop. For those less familiar with the field or for pointers to the variety of applications of topic models, we present the following references. This is not a complete list; please see the related work sections of these papers for more thorough treatment.

General

D. Blei, A. Ng, and M. Jordan. Latent Dirichlet allocation. Journal of Machine Learning Research, 3:993–1022, January 2003.
D. Blei and M. Jordan. Variational inference for Dirichlet process mixtures. Journal of Bayesian Analysis, 1:121–144, 2006.
M. Steyvers and T. Griffiths. Probabilistic Topic Models. In Latent Semantic Analysis: A Road to Meaning, T. Landauer, Mcnamara, S. Dennis, and W. Kintsch eds. Laurence Erlbaum, 2006.
Y. Teh, M. Jordan, M. Beal, and D. Blei. Hierarchical Dirichlet processes. Journal of the American Statistical Association, 101:1566-1581, 2006.
J. Zhu, A. Ahmed and E. P. Xing. MedLDA: Maximum Margin Supervised Topic Models for Regression and Classification. The 26th International Conference on Machine Learningy, 2009.

Inference and Evaluation

A. Asuncion, M. Welling, P. Smyth, and Y. Teh. On Smoothing and Inference for Topic Models. In Uncertainty in Artificial Intelligence, 2009.
W. Li, and A. McCallum. Pachinko Allocation: DAG-structured Mixture Models of Topic Correlations. In International Conference on Machine Learning, 2006.
I. Porteous, A. Ascuncion, D. Newman, A. Ihler, P. Smyth, and M. Welling. Fast Collapsed Gibbs Sampling For Latent Dirichlet Allocation. In Knowledge Discovery and Data Mining, 2008.
H. Wallach, I. Murray, R. Salakhutdinov and D. Mimno. Evaluation Methods for Topic Models. In International Conference on Machine Learning, 2009.
M. Welling, Y. Teh and B. Kappen. Hybrid Variational/Gibbs Inference in Topic Models. In Uncertainty in Artificial Intelligence, 2008.

Biology

E. Airoldi, D. Blei, S. Fienberg, and E. Xing. Mixed-membership stochastic blockmodels. Journal of Machine Learning Research, 9: 1981-2014, 2008.
P. Agius, Y. Ying, and C. Campbell. Bayesian Unsupervised Learning with Multiple Data Types. Statistical Applications in Genetic and Molecular Biology, 3(1):27, 2009.
P. Flaherty, G. Giaever, J. Kumm, Michael I. Jordan, Adam P. Arkin. A Latent Variable Model for Chemogenomic Profiling. Bioinformatics 2005 Aug 1;21(15):3286-93.
S. Shringarpure and E. P. Xing. mStruct: Inference of Population Structure in Light of Both Genetic Admixing and Allele Mutations. Genetics, Vol 182, issue 2, 2009.

Natural Language Processing

J. Boyd-Graber and D. Blei. Syntactic topic models. In Neural Information Processing Systems, 2009.
T. Griffiths, M. Steyvers, D. Blei, and J. Tenenbaum. Integrating topics and syntax. In Neural Information Processing Systems, 2005.
K. Toutanova and M. Johnson. A Bayesian LDA-based Model for Semi-Supervised Part-of-speech Tagging. In Neural Information Processing Systems, 2008.

Social Science

J. Chang, J. Boyd-Graber, and D. Blei. Connections between the lines: Augmenting social networks with text. Knowledge Discovery and Data Mining, 2009.
L. Dietz, S. Bickel, and T. Scheffer. Unsupervised Prediction of Citation Influences. In International Conference on Machine Learning, 2007.
D. Hall, D. Jurafsky, and C. Manning. Studying the History of Ideas Using Topic Models. In Emperical Methods in Natural Language Processing, 2008.

Temporal and Network Models

D. Blei and J. Lafferty. Dynamic topic models. In International Conference on Machine Learning, 2006.
J. Chang and D. Blei. Relational topic models for document networks. Artificial Intelligence and Statistics (in print), 2009.
E.P. Xing, W. Fu, and L. Song. A State-Space Mixed Membership Blockmodel for Dynamic Network Tomography. Annals of Applied Statistics, 2009.
H. Wallach. Topic Modeling: Beyond Bag-of-Words. In International Conference on Machine Learning, 2006.

Vision

J. Philbin, J. Sivic, and A. Zisserman. Geometric LDA: A Generative Model for Particular Object Discovery In British Machine Vision Conference, 2008.
L. Fei-Fei, R. Fergus and P. Perona. Learning generative visual models for 101 object categories. In Computer Vision and Image Understanding, 2007.
C. Wang, D. Blei and L. Fei-Fei. Simultaneous Image Classification and Annotation. In Computer Vision and Pattern Recognition, 2009.