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. Making Multi-Class Gaussian Process Classification Conjugate: Efficient Inference 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
    Leonhard Helminger, Abdelaziz Djelouah, Romann Weber, Markus Gross
  14. NeuTra-lizing Bad Geometry in Hamiltonian Monte Carlo Using Neural Transport
    Matthew Hoffman, Pavel Sountsov
  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
    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
    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
    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
    Florian Maire
  25. Sensitivity of Bayesian Inference to Data Perturbations
    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
    Michalis K. Titsias, Francisco J. R. Ruiz
  34. Neural network ensembles and variational inference revisited
    Marcin Tomczak, Siddharth Swaroop, Richard Turner
  35. Probabilistic programming for robotic mapping
    Anthony Tompkins, Ransalu Senanayake, Fabio Ramos
  36. Fast Bayesian Inference in GLMs with Low Rank Data Approximations
    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
    Mariia Vladimirova, Julyan Arbel, Pablo Mesejo