Papers:
David M. Blei, Alp Kucukelbir & Jon D. McAuliffe. Variational inference: a review for statisticians. JASA/arXiv, 2017+.
Yee Whye Teh, David Newman, and Max Welling. A collapsed variational Bayesian inference algortihm for latent Dirichlet allocation. NIPS 2006.
Havard Rue, Sara Martino, and Nicolas Chopin. Approximate Bayesian inference for latent Gaussian models by using integrated nested Laplace approximations, JRSS-B, 2009.
Matthew D. Hoffman, David M. Blei, Chong Wang, John Paisley. Stochastic Variational Inference. Journal of Machine Learning Research, 2013.
Chong Wang and David M. Blei. Variational Inference in Nonconjugate Models. Journal of Machine Learning Research, 2013.
Cheng Zhang, Judith Butepage, Hedvig Kjellstrom, Stephan Mandt. Advances in variational inference.
Abhijoy Saha, Karthik Bharath, and Sebastian Kurtek. A geometric variational approach to Bayesian inference. JASA, 2019.
**We will meet in Martin O-10. (Downstairs conference room) **
Tenative schedule:
Sept 3, 3-3:30pm: organizational meeting
Sept 17, 2:30-3:20pm: Blei, et. al. Variational inference: a review for statisticians. (Sections 1 and 2) Presenter: Deborah Kunkel
Sept 24: 2:30-3:20pm: Blei, et. al. Variational inference: a review for statisticians. (Sections 3-5)
Oct 8: 2:30 - 3:20pm: Chong Wang and David M. Blei. Variational Inference in Nonconjugate Models.
Oct 22: Workshop day
Nov 5: Hoffman et. al. Stochastic Variational inference.
Nov 19: