Advances in Variational Inference

NIPS 2014 Workshop

13 December 2014  Level 5  Room 510 a

Convention and Exhibition Center, Montreal, Canada

Accepted Papers
Papers listed here are for archival purposes and do not constitute a proceedings for this workshop.

1. Active Oracle Variational Inference, (Poster
    James Edward McInerney, Rajesh Ranganath, and David M. Blei

2. Asymptotically Exact Inference for Latent Variable Models and Its Application to Bayesian PCA, (Poster) 
    Kohei Hayashi and Ryohei Fujimaki

3. Averaged Collapsed Variational Bayes Inference and its application to Infinite Relational Model, (Poster
    Katsuhiko Ishiguro, Issei Sato, and Naonori Ueda

4. Beta Process Non-negative Matrix Factorization with Stochastic Structured Mean-Field Variational Inference, (Poster
    Dawen Liang and Matthew D. Hoffmann

5. Bumping variational inference, (Talk, Poster) 
    Alp Kucukelbir and David Blei

6. Celeste: Scalable variational inference for a generative model of astronomical images, (Spotlight, Poster
    Jeffrey Regier, Brenton Partridge, Jon McAuliffe, Ryan Adams, Matt Hoffman, Dustin Lang, David Schlegel and Prabhat

7. Comparing lower bounds on the entropy of mixture distributions for use in variational inference, (Spotlight, Poster) 
    Alex Matthews, James Hensman and Zoubin Gharamani

8. Covariance Matrices for Mean Field Variational Bayes, (Spotlight, Poster
    Ryan Giordano and Tamara Broderick

9. Discrete Particle Variational Inference(Talk, Poster) 
    Supplementary material.
    Tejas Kulkarni, Ardavan Saeedi, Vikash Mansinghka and Samuel Gershman

10. Efficient Variational Inference for Gaussian Process Structured Prediction, (Poster
     Srijith Prabhakaran Nair Kusumam, P. Balamurugan and Shirish Shevade

11. Embarrassingly parallel variational inference, (Poster
      Willie Neiswanger, Chong Wang and Eric Xing

12. Expectation Propagation for DP mixture models, (Poster
      Supplementary material.
      Vinayak Rao, Eric Sudderth and Yee Whye Teh

13. Probit Regression with Correlated Label Noise: An EM-EP Approach (Poster) 
     Stephan Mandt, Florian Wenzel, John Cunningham and Marius Kloft

14. Gamma Processes, Stick-Breaking, and Variational Inference, (Spotlight, Poster
      Anirban Roychowdhury and Brian Kulis

15. Global solution and performance analysis of variational Bayesian learning: A short summary, (Poster
      Shinichi Nakajima and Masahi Sugiyama

16. Latent Gaussian Processes for Distribution Estimation of Multivariate Categorical Data, (Spotlight, Poster
      Yarin Gal, Yutian Chen and Zoubin Ghahramani

17. Learning Stochastic Recurrent Networks, (Poster
      Justin Bayer and Christian Osendorfer

18. Markov Chain Monte Carlo and Variational Inference: Bridging the Gap, (Talk, Poster
      Tim Salimans, Diederik. P. Kinga and Max Welling

19. Message Passing for Variational Inference in Bayesian Submodular Models, (Talk [pdf, pptx], Poster
      Josip Djolonga and Andreas Krause

20. Necessary evil or first choice? Non-conjugate priors and Poisson community models, (Poster
      David Mimno, Prem Gopalan and David Blei

21. Nested Variational compression in deep GPs, (Poster
      James Hensman and Neil Lawrence

22. On the Convergence of Stochastic Variational Inference in Bayesian Networks, (Poster) 
      Ulrich Paquet

23. On the Strong Convexity of Variational Inference, (Spotlight, Poster
      Ben London, Bert Huang and Lise Getoor

24. Online adaptor grammars with online inference, (Poster) 
      Ke Zhai, Jordan Boyd-Graber and Shay B. Cohen

25. Passing Expectation Propagation Messages with Kernel Methods, (Poster
      Wittawat Jitkrittum, Arthur Gretton and Nicholas Heess

26. Resampled belief networks for variational inference, (Poster) 
      Ramki Gummadi

27. Sandwich Covariance Estimation for Variational Inference, (Poster
      Theodore Westling and Tyler McCormick

28. Sparse Latent Dirichlet Allocation with Collapsed Factorized Asymptotic Bayesian Inference, (Poster
      Supplementary material
      Yusuke Muraoka, Ryohei Fujimaki and Issei Sato

29. Streaming variational inference for normalised random measure mixture models, (Poster) 
      Alex Tank, Nick Foti and Emily Fox

30. Streaming, Massively Parallel Inference for Bayesian Nonparametrics, (Poster
      Trevor Campbell and Jonathan How

31. True Natural Gradient of Collapsed Variational Bayes, (Poster
      Francisco J. R. Ruiz, Neil Lawrence, James Hensman

32. Variational Bayes for Merging Noisy Databases, (Poster
      Rebecca Steorts and Tamara Broderick

33. Variational Inference for dynamic matrix factorization, (Poster) 
      San Gultekin and John Paisley

34. Variational Inference for Latent Variable Modelling of Correlation Structure, (Poster) 
      Mark van der Wilk, Andrew G. Wilson, Carl Rasmussen