Advances in Approximate Bayesian Inference

NIPS 2015 Workshop; December 11, 2015
Room 513 ab, Palais des Congrès de Montréal, Montréal, Canada

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

1. A Deep Generative Model for Astronomical Images of Galaxies Poster
   Jeffrey Regier, Jon McAuliffe, and Prabhat
2. A Deflation method for Probabilistic PCA Poster
   Rajiv Khanna, Joydeep Ghosh, Russell Poldrack, and Oluwasanmi Koyejo
3. A Laplace Approximation for Approximate Bayesian Model Selection Poster
   Richard Golden, Shaurabh Nandy, Vishal Patel, and Pratibha Viraktamath
4. A Sampling Method Based on LDPC Codes Poster
   Xuhong Zhang and Gregory Wornell
5. Approximate Bayesian Inference via Rejection Filtering
   Nathan Wiebe, Christopher Granade, Ashish Kapoor, and Krysta M. Svore
6. Black-box α-divergence Minimization Poster Slides
   Jose Miguel Hernandez-Lobato, Yingzhen Li, Daniel Hernandez-Lobato, Thang Bui, and Richard Turner
7. Convergence of Proximal-Gradient Stochastic Variational Inference under Non-Decreasing Step-Size Sequence Poster
   Mohammad Emtiyaz Khan, Reza Babanezhad, Wu Lin, Mark Schmidt, and Masashi Sugiyama
8. Deep Kalman Filters Poster
   Rahul Krishnan, Uri Shalit, and David Sontag
9. Early Stopping as Nonparametric Variational Inference Poster
   David Duvenaud, Dougal Maclaurin, and Ryan Adams
10. Fast Laplace Approximation for Sparse Bayesian Spike and Slab Models Poster
    Syed Abbas Zilqurnain Naqvi, Shandian Zhe, Yuan Qi, and Jieping Ye
11. Finding New Malicious Domains Using Variational Bayes on Large-Scale Computer Network Data Poster
    Vojtech Letal, Vasek Smidl, Petr Somol, and Tomas Pevny
12. Hierarchical Variational Models Poster Slides
    Rajesh Ranganath, Dustin Tran, and David Blei
13. Improving Semi-Supervised Learning with Auxiliary Deep Generative Models Poster
    Lars Maaløe, Casper Kaae Sønderby, Søren Kaae Sønderby, and Ole Winther
14. Incremental Variational Inference for Latent Dirichlet Allocation Slides
    Cedric Archambeau and Beyza Ermis
15. Inference Networks for Graphical Models Poster Slides
    Brooks Paige and Frank Wood
16. Large Sample Asymptotic for Nonparametric Mixture Model with Count Data Poster
    Vu Nguyen, Dinh Phung, Trung Le, and Svetha Venkatesh
17. Mixing Rates for the Gibbs Sampler over Restricted Boltzmann Machines Poster
    Christopher Tosh
18. On Modern Deep Learning and Variational Inference Poster
    Yarin Gal and Zoubin Ghahramani
19. Perturbation Theory for Variational Inference Poster
    Manfred Opper, Marco Fraccaro, Ulrich Paquet, Alex Susemihl, and Ole Winther
20. Provable Nonparametric Bayesian Inference Poster
    Bo Dai, Niao He, Hanjun Dai, and Le Song
21. Rapid Prototyping of Probabilistic Models: Emerging Challenges in Variational Inference Poster
    Yarin Gal
22. Reinforced Variational Inference Poster Slides
    Theophane Weber, Nicolas Heess, S. M. Ali Eslami, John Schulman, David Wingate, and David Silver
23. Robust Inference with Variational Bayes Poster Slides
    Ryan Giordano, Tamara Broderick, and Michael Jordan
24. Scalable Reinforcement Learning via Trajectory Optimization and Approximate Gaussian Process Regression Poster
    Yunpeng Pan, Xinyan Yan, Evangelos Theodorou, and Byron Boots
25. Stochastic Collapsed Variational Inference for Sequential Data Poster
    Pengyu Wang and Phil Blunsom
26. Stochastic Expectation Propagation for Large Scale Gaussian Process Classification Poster Slides
    Daniel Hernandez-Lobato, Jose Miguel Hernandez-Lobato, Yingzhen Li, Thang Bui, and Richard Turner
27. The Variational Coupled Gaussian Process Dynamical Model Poster
    Dmytro Velychko and Dominik Endres
28. Training Deep Gaussian Processes using Stochastic Expectation Propagation and Probabilistic Backpropagation Poster
    Thang Bui, Jose Miguel Hernandez-Lobato, Daniel Hernandez-Lobato, Yingzhen Li, and Richard Turner
29. Training Deep Generative Models: Variations on a Theme Poster Slides
    Philip Bachman and Doina Precup
30. Variable Elimination in Fourier Domain
    Yexiang Xue, Stefano Ermon, Ronan Lebras, Carla Gomes, and Bart Selman
31. Variational Inference with Gradient Flows
    Nicholas Altieri and David Duvenaud