David M. Blei

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David M. Blei is a person.

See: Latent Dirichlet Allocation, Probabilistic Topic Model.

References

2016

(Liang et al., 2016) ⇒ Dawen Liang, Jaan Altosaar, Laurent Charlin, and David M. Blei. (2016). “Factorization Meets the Item Embedding: Regularizing Matrix Factorization with Item Co-occurrence.” In: Proceedings of the 10th ACM Conference on Recommender Systems. ISBN:978-1-4503-4035-9 doi:10.1145/2959100.2959182

2013

(Ranganath et al., 2013) ⇒ Rajesh Ranganath, Chong Wang, David M. Blei, and Eric P. Xing (2013). "An Adaptive Learning Rate for Stochastic Variational Inference." In: International Conference on Machine Learning (ICML-2013).

2011

(Wang & Blei, 2011) ⇒ Chong Wang, and David M. Blei. (2011). “Collaborative Topic Modeling for Recommending Scientific Articles.” In: Proceedings of the 17th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. ISBN:978-1-4503-0813-7 doi:10.1145/2020408.2020480

2009

(Blei & Lafferty, 2009) ⇒ David M. Blei, and John D. Lafferty. (2009). “Topic Models.” In: A. Srivastava and M. Sahami, editors, Text Mining: Classification, Clustering, and Applications . Chapman & Hall/CRC Data Mining and Knowledge Discovery Series.

2008

(Blei, 2008) ⇒ David M. Blei. (2008). “Modeling Science." Presentation. April 17, 2008
(Airoldi et al., 2008) ⇒ Edoardo M. Airoldi, David M. Blei, Stephen E. Fienberg, and Eric P. Xing. (2008). “Mixed Membership Stochastic Blockmodels.” In: The Journal of Machine Learning Research, 9.

2007

(Blei & Lafferty, 2007) ⇒ David M. Blei, and John D. Lafferty. (2007). “A Correlated Topic Model of Science.” In: Annals of Applied Statistics, 1(1). doi:10.1214/07-AOAS114

2006

(Blei & Lafferty, 2006) ⇒ David M. Blei, and John D. Lafferty. (2006). “Dynamic Topic Models.” In: Proceedings of the 23rd International Conference on Machine Learning (ICML 2006). doi:10.1145/1143844.1143859
(Teh et al., 2006) ⇒ Yee Whye Teh, Michael I. Jordan, Matthew J. Beal, and David M. Blei. (2006). “Hierarchical Dirichlet Processes.” In: Journal of the American Statistical Association.

2005

(Blei & Jordan, 2005) ⇒ David M. Blei, and Michael I. Jordan (2005), "Variational Methods for Dirichlet Process Mixtures.” In: Bayesian Analysis, 1.

2004

(Blei & Jordan, 2004) ⇒ David M. Blei, and Michael I. Jordan. (2004). “Variational Methods for the Dirichlet Process.” In: Proceedings of the International Conference on Machine Learning (ICML 2004).
(Griffiths et al., 2004) ⇒ Thomas L. Griffiths, Mark Steyvers, David M. Blei, and Joshua B. Tenenbaum. (2004). “Integrating Topics and Syntax.” In: Advances in Neural Information Processing Systems 17 (NIPS 2004).

2003a

(Blei, Ng & Jordan, 2003) ⇒ David M. Blei, Andrew Y. Ng, and Michael I. Jordan. (2003). “Latent Dirichlet Allocation.” In: The Journal of Machine Learning Research, 3. doi:10.1162/jmlr.2003.3.4-5.993

2003b

(Blei, Jordan & Ng, 2003) ⇒ David M. Blei, Michael I. Jordan, and Andrew Y. Ng. (2003). “Hierarchical Bayesian Models for Applications in Information Retrieval.” In: Bayesian Statistics, 7. ISBN:0198526156

2003c

(Blei & Jordan, 2003) ⇒ David M. Blei, and Michael I. Jordan. (2003). “Modeling Annotated Data.” In: Proceedings of the 26th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2003).

2003d

(Blei, Griffiths & al) ⇒ David M. Blei, Thomas L. Griffiths, Michael I. Jordan, and Joshua B. Tenenbaum. (2003). “Hierarchical topic models and the nested Chinese restaurant process.” In: Neural Information Processing Systems 16 (NIPS 2003).

2001

(Blei, Ng & Jordan, 2001) ⇒ David M. Blei, Andrew Y. Ng, and Michael I. Jordan. (2001). “Latent Dirichlet Allocation.” In: Advances in Neural Information Processing Systems 14 (NIPS 2001).

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