Combinatorial Topic Models using Small-Variance Asymptotics

@InProceedings{pmlr-v54-jiang17a,
  title = 	 {{Combinatorial Topic Models using Small-Variance Asymptotics}},
  author = 	 {Jiang, Ke and Sra, Suvrit and Kulis, Brian},
  booktitle = 	 {Proceedings of the 20th International Conference on Artificial Intelligence and Statistics},
  pages = 	 {421--429},
  year = 	 {2017},
  editor = 	 {Singh, Aarti and Zhu, Jerry},
  volume = 	 {54},
  series = 	 {Proceedings of Machine Learning Research},
  month = 	 {20--22 Apr},
  publisher =    {PMLR},
  pdf = 	 {http://proceedings.mlr.press/v54/jiang17a/jiang17a.pdf},
  url = 	 {https://proceedings.mlr.press/v54/jiang17a.html},
  abstract = 	 {Modern topic models typically have a probabilistic formulation, and derive their inference algorithms based on Latent Dirichlet Allocation (LDA) and its variants. In contrast, we approach topic modeling via combinatorial optimization, and take a small-variance limit of LDA to derive a new objective function. We minimize this objective by using ideas from combinatorial optimization, obtaining a new, fast, and high-quality topic modeling algorithm.  In particular, we show that our results are not only significantly better than traditional SVA algorithms, but also truly competitive with popular LDA-based approaches; we also discuss the (dis)similarities between our approach and its probabilistic counterparts.}
}