2nd Symposium on
Advances in Approximate Bayesian Inference

December 8, 2019
Pan Pacific Hotel
300 - 999 Canada Pl
Vancouver, BC V6C 3B5, Canada
Crystal Pavilion

Awards

  • Best student paper
    • Jakub Swiatkowski (University of Warsaw)
      The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
  • Best student paper run-ups
    • Jiaxin Shi (Tsinghua University)
      Sparse Orthogonal Variational Inference for Gaussian Processes
    • Anna Kuzina & Evgenii Egorov (Skoltech)
      BooVAE: A Scalable Framework for Continual VAE Learning under Boosting Approach
  • Best industry paper
    • Matthew Hoffman (Google)
      Langevin Dynamics as Nonparametric Variational Inference
  • Best industry paper run-ups
    • Iuliia Molchanova (Samsung)
      Structured semi-implicit variational inference
    • Roman Novak (Google Brain)
      Neural Tangents: Fast and Easy Infinite Neural Networks in Python

List of papers

Papers can be found in OpenReview, where they are available for archival purposes. This does not constitute a proceedings for the symposium.

  1. Variational Predictive Information Bottleneck
    Alexander A. Alemi
  2. Newtonian Monte Carlo: a second-order gradient method for speeding up MCMC
    Nimar S. Arora, Nazanin Khosravani Tehrani, Kinjal Shah, Michael Tingley
  3. Towards Characterizing the High-dimensional Bias of Kernel-based Particle Inference Algorithms
    Jimmy Ba, Murat Erdogdu, Taiji Suzuki, Shengyang Sun, Denny Wu, Tianzong Zhang
  4. GP-ALPS: Automatic Latent Process Selection for Multi-Output Gaussian Process Models
    Pavel Berkovich, Eric Perim, Wessel Bruinsma
  5. Latent Variable Session-Based Recommendation
    Stephen Bonner, David Rohde
  6. Arbitrarily-conditioned Data Imputation
    Micael Carvalho, Thibaut Durand, Jiawei He, Nazanin Mehrasa, Greg Mori
  7. Exchangeable Variational Autoencoders with Applications to Genomic Data
    Jeffrey Chan, Jeffrey Spence, and Yun Song
  8. MMD-Bayes: Robust Bayesian Estimation via Maximum Mean Discrepancy
    Badr-Eddine Chérief-Abdellatif, Pierre Alquier
  9. Bijectors.jl: Flexible transformations of distributions
    Tor Erlend Fjelde, Kai Xu, Mohamed Tarek, Sharan Yalburgi, Hong Ge
  10. Deep Amortized Variational Inference for Multivariate Time Series Imputation with Latent Gaussian Process Models
    Vincent Fortuin, Dmitry Baranchuk, Gunnar Rätsch, Stephan Mandt
  11. Graph Tracking in Dynamic Probabilistic Programs via Source Transformations
    Philipp Gabler, Martin Trapp, Hong Ge, Franz Pernkopf
  12. Variationally Inferred Sampling Through a Refined Bound
    Victor Gallego, David Rios Insua
  13. Masking schemes for universal marginalisers
    Divya Gautam, Maria Lomeli, Kostis Gourgoulias, Daniel H. Thompson, Saurabh Johri
  14. Automatically Trading off Time and Variance when Selecting Gradient Estimators
    Tomas Geffner, Justin Domke
  15. Variational Selective Autoencoder
    Yu Gong, Hossein Hajimirsadeghi, Jiawei He, Megha Nawhal, Thibaut Durand, Greg Mori
  16. Efficient Bayesian Inference for Nested Simulators
    Bradley Gram-Hansen, Christian Schroeder de Witt, Robert Zinkov, Saeid Naderiparizi, Adam Scibior, Andreas Munk, Frank Wood, Mehrdad Ghadiri, Philip Torr, Yee Whye Te, Atilim Gunes Baydin, Tom Rainforth
  17. Langevin Dynamics as Nonparametric Variational Inference
    Matthew D. Hoffman, Yian Ma
  18. Variational Bayesian Methods for Stochastically Constrained System Design Problems
    Prateek Jaiswal, Harsha Honnappa, Vinayak A. Rao
  19. Batch simulations and uncertainty quantification in Gaussian process surrogate-based approximate Bayesian computation
    Marko Järvenpää, Aki Vehtari, Pekka Marttinen
  20. Sequential Learning for Dirichlet Process Mixtures
    Chunlin Ji, Bin Liu, Yingkai Jiang, Ke Deng
  21. Stein Variational Gradient Descent for Approximate Bayesian Computation
    Chunlin Ji, Jiangsheng Yi, Wanchuang Zhu
  22. Normalizing Constant Estimation with Optimal Bridge Sampling and Normalizing Flows
    He Jia, Uros Seljak
  23. Spectral Mixture Kernel Approximation Using Reparameteriztaion Random Fourier Feature
    Yohan Jung, Jinkyoo Park
  24. Gaussian Process Meta-Representations For Hierarchical Neural Network Weight Priors
    Theofanis Karaletsos, Thang D. Bui
  25. Approximate Importance Sampling
    Alec Koppel*, Amrit Singh Bedi*, Brian M. Sadler, and Victor Elvira
  26. Bayesian Model Selection for Identifying Markov Equivalent Causal Graphs
    Mehmet Burak Kurutmaz, Melih Barsbey, Ali Taylan Cemgil, Sinan Yıldırım, Umut Şimşekli
  27. BooVAE: A Scalable Framework for Continual VAE Learning under Boosting Approach
    Anna Kuzina, Evgenii Egorov, Evgeny Burnaev
  28. Approximate Inference for Fully Bayesian Gaussian Process Regression
    Vidhi Lalchand, Carl Edward Rasmussen
  29. Learning undirected models via query training
    Miguel Lázaro Gredilla, Wolfgang Lehrach, Dileep George
  30. Scalable Gradients and Variational Inference for Stochastic Differential Equations
    Xuechen Li, Ting-Kam Leonard Wong, Ricky T. Q. Chen, David K. Duvenaud
  31. Towards Hierarchical Discrete Variational Autoencoders
    Valentin Liévin, Andrea Dittadi, Lars Maaløe, Ole Winther
  32. SUMO: Unbiased Estimation of Log Marginal Probability for Latent Variable Models
    Yucen Luo, Alex Beatson, Mohammad Norouzi, Jun Zhu, David Duvenaud, Ryan P. Adams, Ricky T. Q. Chen
  33. HM-VAEs: a Deep Generative Model for Real-valued Data with Heterogeneous Marginals
    Chao Ma, Sebastian Tschiatschek, Yingzhen Li, Richard Turner, Jose Miguel Hernandez-Lobato, Cheng Zhang
  34. Improving Sequential Latent Variable Models with Autoregressive Flows
    Joseph Marino, Lei Chen, Jiawei He, Stephan Mandt
  35. Genomic variety prediction via Bayesian nonparametrics
    Lorenzo Masoero, Federico Camerlenghi, Stefano Favaro, Tamara Broderick
  36. Sinkhorn Permutation Variational Marginal Inference
    Gonzalo Mena, Erdem Varol, Amin Nejatbakhsh, Eviatar Yemini, Liam Paninski
  37. Interpretable User Models via Decision-rule Gaussian Processes
    Danial Mohseni-Taheri, Selvaprabu Nadarajah, Theja Tulabandhula
  38. Structured Semi-Implicit Variational Inference
    Iuliia Molchanova, Dmitry Molchanov, Novi Quadrianto, Dmitry Vetrov
  39. Non-reversibly updating a uniform [0,1] value for accept/reject decisions
    Radford M. Neal
  40. Neural Tangents: Fast and Easy Infinite Neural Networks in Python
    Roman Novak, Lechao Xiao, Jiri Hron, Jaehoon Lee, Jascha Sohl-Dickstein, Samuel S. Schoenholz
  41. Benchmarking the Neural Linear Model for Regression
    Sebastian W. Ober, Carl E. Rasmussen
  42. Distributional Bayesian optimisation for variational inference on black-box simulators
    Rafael Oliveira, Lionel Ott, Fabio Ramos
  43. Neural Permutation Processes
    Ari Pakman, Yueqi Wang, Liam Paninski
  44. The Gaussian Process Prior VAE for Interpretable Latent Dynamics
    Michael Arthur Leopold Pearce
  45. MultiVerse: Causal Reasoning using Importance Sampling in Probabilistic Programming
    Yura Perov*, Logan Graham*, Kostis Gourgoulias, Jonathan Richens, Ciarán Lee, Adam Baker, Saurabh Johri
  46. Seeing the whole picture instead of a single point: Self-supervised likelihood learning for deep generative models
    Petra Poklukar, Judith Bütepage, Danica Kragic
  47. Challenges in Computing and Optimizing Upper Bounds of Marginal Likelihood based on Chi-Square Divergences
    Melanie F. Pradier, Michael C. Hughes, Finale Doshi-Velez
  48. Pseudo-Bayesian Learning via Direct Loss Minimization with Applications to Sparse Gaussian Process Models
    Rishit Sheth, Roni Khardon
  49. Sparse Orthogonal Variational Inference for Gaussian Processes
    Jiaxin Shi, Michalis Titsias, Andriy Mnih
  50. Information in Infinite Ensembles of Infinitely-Wide Neural Networks
    Ravid Shwartz-Ziv, Alexander A. Alemi
  51. The k-tied Normal Distribution: A Compact Parameterization of Gaussian Mean Field Posteriors in Bayesian Neural Networks
    Jakub Świątkowski, Kevin Roth, Bastiaan S. Veeling, Linh Tran, Joshua V. Dillon, Jasper Snoek, Stephan Mandt, Tim Salimans, Rodolphe Jenatton, Sebastian Nowozin
  52. Optimal Transport for Distribution Adaptation in Bayesian Hilbert Maps
    Anthony Tompkins, Ransalu Senanayake, Fabio Ramos
  53. Efficient Inference Amortization in Graphical Models using Structured Continuous Conditional Normalizing Flows
    Christian Weilbach, Boyan Beronov, William Harvey, Frank Wood
  54. AdvancedHMC.jl: A modular implementation of Stan's No-U-Turn sampler in Julia
    Kai Xu, Hong Ge, Will Tebbutt, Mohamed Tarek, Martin Trapp, Zoubin Ghahramani
  55. Characterizing and Avoiding Problematic Global Optima of Variational Autoencoders
    Yaniv Yacoby, Weiwei Pan, Finale Doshi-Velez
  56. A Wasserstein Minimum Velocity Approach to Learning Unnormalized Models
    Ziyu Wang, Shuyu Cheng, Yueru Li, Jun Zhu, Bo Zhang
  57. Coping With Simulators That Don’t Always Return
    Andrew Warrington, Saied Naderiparizi, Frank Wood
  58. Variational Gaussian Process Models without Matrix Inverses
    Mark van der Wilk, ST John, Artem Artemev, James Hensman
  59. Global Approximate Inference via Local Linearisation for Temporal Gaussian Processes
    William J. Wilkinson, Paul E. Chang, Michael Riis Andersen, Arno Solin
  60. Rapid Model Comparison by Amortizing Across Models
    Lily H. Zhang, Michael C. Hughes
  61. Meta-Learning for Variational Inference
    Ruqi Zhang, Yingzhen Li, Chris De Sa, Sam Devlin, Cheng Zhang
  62. Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support
    Yuan Zhou, Hongseok Yang, Yee Whye Teh, Tom Rainforth