4th Symposium on
Advances in Approximate Bayesian Inference

Virtual Event, February 1st and 2nd, 2022

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. Embedded-model flows: Combining the inductive biases of model-free deep learning and explicit probabilistic modeling PDF Poster
    Gianluigi Silvestri, Emily Fertig, Dave Moore, Luca Ambrogioni
  2. Pathologies in Priors and Inference for Bayesian Transformers PDF Poster
    Tristan Cinquin, Alexander Immer, Max Horn, Vincent Fortuin
  3. Bootstrap Your Flow PDF Poster
    Laurence Illing Midgley, Vincent Stimper, Gregor N. C. Simm, José Miguel Hernández-Lobato
  4. Neural Variational Gradient Descent PDF Poster
    Lauro Langosco di Langosco, Vincent Fortuin, Heiko Strathmann
  5. On Disentanglement in Gaussian Process Variational Autoencoders PDF Poster
    Simon Bing, Vincent Fortuin, Gunnar Ratsch
  6. Generalized Kernel Thinning PDF Poster
    Raaz Dwivedi, Lester Mackey
  7. PAC-Bayesian matrix completion with a spectral scaled Student prior PDF Poster
    T Tien Mai
  8. Bayesian OOD detection with aleatoric uncertainty and outlier exposure PDF Poster
    Xi Wang, Laurence Aitchison
  9. Efficient Bayesian Inverse Reinforcement Learning via Conditional Kernel Density Estimation PDF Poster
    Aishwarya Mandyam, Didong Li, Diana Cai, Andrew Jones, Barbara Engelhardt
  10. Quantum Bayesian Neural Networks PDF Poster
    Noah Berner, Vincent Fortuin, Jonas Landman
  11. U-Statistics for Importance-Weighted Variational Inference PDF Poster
    Javier Burroni, Kenta Takatsu, Justin Domke, Daniel Sheldon
  12. Matrix Inversion free variational inference in Conditional Student's T Processes PDF Poster
    Sebastian Popescu, Ben Glocker, Mark van der Wilk
  13. Meta-learning richer priors for VAEs PDF Poster
    Marcello Massimo Negri, Vincent Fortuin, Jan Stuehmer
  14. Shooting Schrödinger’s Cat PDF Poster
    David Lopes Fernandes, Francisco Vargas, Carl Henrik Ek, Neill D. F. Campbell
  15. Probabilistic Deep Learning with Generalised Variational Inference PDF Poster
    Giorgos Felekis, Theo Damoulas, Brooks Paige
  16. Can Sequential Bayesian Inference Solve Continual Learning? PDF Poster
    Samuel Kessler, Adam D. Cobb, Stefan Zohren, Stephen J. Roberts
  17. Bayesian Learning via Neural Schrödinger-Föllmer Flows PDF Poster
    Francisco Vargas, Andrius Ovsianas, David Lopes Fernandes, Mark Girolami, Neil D Lawrence, Nikolas Nüsken
  18. Variational Likelihood-Free Gradient Descent PDF Poster
    Jack Simons, Song Liu, Mark Beaumont
  19. A Probabilistic Deep Image Prior over Image Space PDF Poster
    Riccardo Barbano, Javier Antoran, José Miguel Hernández-Lobato, Bangti Jin
  20. Sampling with Mirrored Stein Operators PDF Poster
    Jiaxin Shi, Chang Liu, Lester Mackey
  21. Learning Consistent Deep Generative Models from Sparsely Labeled Data PDF Poster
    Gabriel Hope, Madina Abdrakhmanova, Xiaoyin Chen, Michael C Hughes, Erik B. Sudderth
  22. Deep Reference Priors: What is the best way to pretrain a model? PDF Poster
    Yansong Gao, Rahul Ramesh, Pratik Chaudhari
  23. Metropolis Augmented Hamiltonian Monte Carlo PDF Poster
    Guangyao Zhou
  24. Improved Inverse-Free Variational Bounds for Sparse Gaussian Processes PDF Poster
    Mark van der Wilk, Artem Artemev, James Hensman
  25. Linearised Laplace Inference in Networks with Normalisation Layers and the Neural g-Prior PDF Poster
    Javier Antoran, James Urquhart Allingham, David Janz, Erik Daxberger, Eric Nalisnick, José Miguel Hernández-Lobato
  26. Structured Stochastic Gradient MCMC: a hybrid VI and MCMC approach PDF Poster
    Antonios Alexos, Alex James Boyd, Stephan Mandt
  27. Fast Finite Width Neural Tangent Kernel PDF Poster
    Roman Novak, Jascha Sohl-Dickstein, Samuel Stern Schoenholz
  28. Bounding Wasserstein distance with couplings PDF Poster
    Niloy Biswas, Lester Mackey
  29. Distribution Compression in Near-linear Time PDF Poster
    Abhishek Shetty, Raaz Dwivedi, Lester Mackey
  30. Double Control Variates for Gradient Estimation in Discrete Latent Variable Models PDF Poster
    Michalis Titsias, Jiaxin Shi
  31. Sliced Wasserstein Variational Inference PDF Poster
    Mingxuan Yi, Song Liu