[1] Asuncion, A., Welling, M., Smyth, P., Teh, Y.W.: On smoothing and inference for topic models. In: Proceedings of the Twenty-Fifth Conference on Uncertainty in Artificial Intelligence, pp. 27–34 (2009)

[2] Liu, B., Liu, L., Tsykin, A., Goodall, G.J., Green, J.E., Zhu, M., Kim, C.H., Li, J.: Identifying functional miRNA-mRNA regulatory modules with correspondence latent Dirichlet allocation. Bioinformatics 26(24), 3105 (2010)

[3] Sontag, D., Roy, D.M.: Complexity of inference in latent Dirichlet allocation. In: Advances in Neural Information Processing Systems (NIPS) (2011)

[4] Blei, D.M., Ng, A.Y., Jordan, M.I.: Latent Dirichlet allocation. J. Mach. Learn. Res. 3(3), 993–1022 (2003)

[5] Blei, D.M.: Probabilistic topic models. Commun. ACM 55(4), 77–84 (2012)

[6] Mimno, D.: Computational historiography: data mining in a century of classics journals. J. Comput. Cult. Heritage 5(1), 3 (2012)

[7] Mimno, D., Hoffman, M.D., Blei, D.M.: Sparse stochastic inference for latent Dirichlet allocation. In: Proceedings of the 29th Annual International Conference on Machine Learning (2012)

[8] Schwartz, H.A., Eichstaedt, J.C., Dziurzynski, L., Kern, M.L., Blanco, E., Kosinski, M., Stillwell, D., Seligman, M.E.P., Ungar, L.H.: Toward personality insights from language exploration in social media. In: AAAI Spring Symposium Series (2013)

[9] Pritchard, J.K., Stephens, M., Donnelly, P.: Inference of population structure using multilocus genotype data. Genetics 155(2), 945–959 (2000)

[10] Mairal, J.: Stochastic majorization-minimization algorithms for large-scale optimization. In: Advances in Neural Information Processing Systems, pp. 2283–2291 (2013)

[11] Lau, J.H., Newman, D., Baldwin, T.: Machine reading tea leaves: automatically evaluating topic coherence and topic model quality. In: Proceedings of the Association for Computational Linguistics, pp. 530–539 (2014)

[12] Grimmer, J.: A Bayesian hierarchical topic model for political texts: measuring expressed agendas in senate press releases. Polit. Anal. 18(1), p1–35 (2010)

[13] Than, K., Doan, T.: Guaranteed algorithms for inference in topic models (2015). arXiv preprint arXiv: 1512.03308

[14] Hoffman, M.D., Blei, D.M., Wang, C., Paisley, J.: Stochastic variational inference. J. Mach. Learn. Res. 14(1), 1303–1347 (2013)

[15] Aletras, N., Stevenson, M.: Evaluating topic coherence using distributional semantics. In: Proceedings of the 10th International Conference on Computational Semantics, pp. 13–22 (2013)

[16] Ghadimi, S., Lan, G.: Stochastic first-and zeroth-order methods for nonconvex stochastic programming. SIAM J. Optim. 23(4), 2341–2368 (2013)

[17] Gerrish, S., Blei, D.: How they vote: issue-adjusted models of legislative behavior. Adv. Neural Inf. Process. Syst. 25, 2762–2770 (2012)

[18] Griffiths, T.L., Steyvers, M.: Finding scientific topics. In: Proceedings of the National Academy of Sciences of the United States of America (2004)

[19] Teh, Y.W., Newman, D.M., Welling, A.: Collapsed variational Bayesian inference algorithm for latent Dirichlet allocation. In: Advances in Neural Information Processing Systems, vol. 19, pp. 13–53 (2007)