Symposium on Advances in Approximate Bayesian Inference

December 2, 2018
Le 1000 Conference Center
1000 Rue de la Gauchetière Ouest
Montréal, QC H3B 0A2, Canada

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

1. An Exploration of Acquisition and Mean Functions in Variational Bayesian Monte Carlo
   Luigi Acerbi
2. Scalable GAM using sparse variational Gaussian Processes
   Vincent Adam, Nicolas Durrande, St John
3. Multivariate Mutually Regressive Point Processes
   Ifigeneia Apostolopoulou, Scott Linderman, Kyle Miller, Artur Dubrawski
4. Geometry of Friston's active inference
   Martin Biehl
5. Explicit Rates of Convergence for Sparse Variational Inference in Gaussian Process Regression
   David Burt, Carl Rasmussen, Mark van der Wilk
6. Informed Priors for Deep Representation Learning
   Judith Butepage, Jiawei He, Cheng Zhang, Leonid Sigal, Greg Mori, Stephan Mandt
7. Consistency of ELBO maximization for model selection
   Badr-Eddine Chérief-Abdellatif
8. On the Importance of Learning Aggregate Posteriors in Multimodal Variational Autoencoders
   Chris Cremer, Nate Kushman
9. Scalable Multi-Class Gaussian Process Classification via Data Augmentation
   Théo Galy-Fajou, Florian Wenzel, Christian Donner, Manfred Opper
10. Amortized Monte Carlo Integration
    Adam Golinski, Yee Whye Teh, Frank Wood, Tom Rainforth
11. Automatic Reparameterisation in Probabilistic Programming
    Maria I. Gorinova, Dave Moore, Matthew D. Hoffman
12. Scalable Reversible Generative Models with Free-form Continuous Dynamics
    Will Grathwohl, Ricky Chen, Jesse Bettencourt, David Duvenaud
13. Disentangled Dynamic Representations from Unordered Data Poster
    Leonhard Helminger, Abdelaziz Djelouah, Romann Weber, Markus Gross
14. NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport
    Matthew Hoffman, Pavel Sountsov, Joshua Dillon, Ian Langmore, Dustin Tran, Srinivas Vasudevan
15. Bayesian Learning of Conditional Kernel Mean Embeddings for Automatic Likelihood-Free Inference
    Kelvin Hsu, Fabio Ramos
16. Non-Factorised Variational Inference in Dynamical Systems
    Alessandro Davide Ialongo, Mark van der Wilk, James Hensman, Carl Edward Rasmussen
17. MISSO: Minimization by Incremental Stochastic Surrogate for large-scale nonconvex Optimization
    Belhal Karimi, Eric Moulines
18. Bayesian Learning of Non-Negative Matrix/Tensor Factorizations by Simulating Polya Urns Poster
    Mehmet Burak Kurutmaz, Ali Taylan Cemgil, Umut Şimşekli, Melih Barsbey, Sinan Yıldırım
19. Learning Sparse Representative Subsets
    Si Kai Lee, Mohammad Emtiyaz Khan
20. Fast and Simple Natural-Gradient Variational Inference with Mixture of Exponential-family Approximations Poster
    Wu Lin, Mohammad Emtiyaz Khan, Mark Schmidt
21. Likelihood-free inference with emulator networks
    Jan-Matthis Lueckmann, Giacomo Bassetto, Theofanis Karaletsos, Jakob H. Macke
22. Mini-batch Replica-exchange Monte Carlo for Approximate Multimodal Posterior Sampling Poster
    Rui Luo, Qiang Zhang, Yaodong Yang, Yuanyuan Liu
23. Bayesian leave-one-out cross-validation for large data sets
    Måns Magnusson, Michael Riis Andersen, Aki Vehtari
24. Dereversibilizing Metropolis-Hastings: simple implementation of non-reversible MCMC methods Poster
    Florian Maire
25. Sensitivity of Bayesian Inference to Data Perturbations Poster
    Lorenzo Masoero, William T. Stephenson, Tamara Broderick
26. Normalized Random Measure Mixture Models in Variational Autoencoders
    Rahul Mehta, Hui Lu
27. Comparing Interpretable Inference Models for Videos of Physical Motion
    Michael Pearce, Silvia Chiappa, Ulrich Paquet
28. Antithetic Sampling with Hamiltonian Monte Carlo
    Dan Piponi, Matthew Hoffman
29. Goodness-of-Fit Tests for High-Dimensional Discrete Distributions with Application to Convergence Diagnostics in Approximate Bayesian Inference
    Feras Saad, Cameron Freer, Nathanael Ackerman, Vikash Mansinghka
30. Probabilistic Knowledge Graph Embeddings
    Farnood Salehi, Robert Bamler, Stephan Mandt
31. A Variational Inference Approach for Locally Private Inference of Poisson Factorization Models
    Alexandra Schofield, Aaron Schein, Zhiwei Steven Wu, Hanna Wallach
32. EP Structured Variational Autoencoders
    Jonathan So, James Townsend, Benoit Gaujac
33. Unbiased Implicit Variational Inference Poster
    Michalis K. Titsias, Francisco J. R. Ruiz
34. Neural network ensembles and variational inference revisited
    Marcin Tomczak, Siddharth Swaroop, Richard Turner
35. Approximate Bayesian inference for robotic mapping
    Anthony Tompkins, Ransalu Senanayake, Fabio Ramos
36. Fast Bayesian Inference in GLMs with Low Rank Data Approximations Poster
    Brian Trippe, Jonathan Huggins, Tamara Broderick
37. Doubly Reparameterized Gradient Estimators for Monte Carlo Objectives
    George Tucker, Dieterich Lawson, Shixiang Gu, Chris J. Maddison
38. Sequential Monte Carlo for Dynamic Softmax Bandits
    Iñigo Urteaga, Chris Wiggins
39. The LORACs prior for VAEs: Letting the Trees Speak for the Data
    Sharad Vikram, Matthew Hoffman, Matthew Johnson
40. Bayesian neural networks increasingly sparsify their units with depth Poster
    Mariia Vladimirova, Julyan Arbel, Pablo Mesejo