5th Symposium on
Advances in Approximate Bayesian Inference

Sunday July 23rd, 2023 at the Ala Moana Hotel, 410 Atkinson Drive in Honolulu, Hawaii 96814 United States Co-located with ICML

List of papers

Accepted papers are available at OpenReview for archival purposes. This does not constitute a proceedings for the symposium.

Main Track

  1. Poster
  2. Quasi-Bayesian Density Estimation via Autoregressive Predictives
    Sahra Ghalebikesabi, Christopher C. Holmes, Edwin Fong, Brieuc Lehmann
  3. Poster
  4. Dimensionality Reduction as Probabilistic Inference
    Aditya Ravuri, Francisco Vargas, Vidhi Lalchand, Neil D Lawrence
  5. Poster
  6. SAMBA: Regularized Autoencoders perform Sharpness-Aware Minimization
    Patrik Reizinger, Ferenc Huszár
  7. Poster
  8. Robust Hybrid Learning With Expert Augmentation
    Antoine Wehenkel, Jens Behrmann, Hsiang Hsu, Guillermo Sapiro, Gilles Louppe, Joern-Henrik Jacobsen
  9. Poster
  10. Expressiveness Remarks for Denoising Diffusion Based Sampling
    Francisco Vargas, Teodora Reu, Anna Kerekes
  11. Poster
  12. Function-Space Regularization for Deep Bayesian Classification
    Jihao Andreas Lin, Joe Watson, Pascal Klink, Jan Peters
  13. Poster
  14. Attacking Bayes: Are Bayesian Neural Networks Inherently Robust?
    Yunzhen Feng, Tim G. J. Rudner, Nikolaos Tsilivis, Julia Kempe
  15. Poster
  16. An Information-Theoretic Perspective on Variance-Invariance-Covariance Regularization
    Ravid Shwartz-Ziv, Randall Balestriero, Kenji Kawaguchi, Tim G. J. Rudner, Yann LeCun
  17. Poster
  18. Variational Partitioning
    Thomas M. Sutter, Alain Ryser, Joram Liebeskind, Julia E Vogt
  19. Poster
  20. Sample Average Approximation for Black-Box VI
    Javier Burroni, Justin Domke, Daniel Sheldon
  21. Poster
  22. Balancing Simulation-based Inference for Conservative Posteriors
    Arnaud Delaunoy, Benjamin Kurt Miller, Patrick Forré, Christoph Weniger, Gilles Louppe
  23. Poster
  24. Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization
    Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Vincent Fortuin
  25. Poster
  26. Indirect Functional Bayesian Neural Networks
    Mengjing Wu, Junyu Xuan, Jie Lu
  27. Poster
  28. Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Models
    Jack Simons, Louis Sharrock, Song Liu, Mark Beaumont
  29. Poster
  30. Linearized Laplace Inference in Neural Additive Models
    Kouroche Bouchiat, Alexander Immer, Hugo Yèche, Vincent Fortuin
  31. Poster
  32. Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks
    Alexander Möllers, Alexander Immer, Elvin Isufi, Vincent Fortuin
  33. Poster
  34. Improving Continual Learning by Accurate Gradient Reconstructions of the Past
    Erik Daxberger, Siddharth Swaroop, Kazuki Osawa, Rio Yokota, Richard E Turner, José Miguel Hernández-Lobato, Mohammad Emtiyaz Khan
  35. Poster
  36. A Dual Control Variate for accelerated black-box variational inference
    Xi Wang, Tomas Geffner, Justin Domke
  37. Poster
  38. Individual Fairness in Bayesian Neural Networks
    Alice Doherty, Matthew Robert Wicker, Luca Laurenti, Andrea Patane
  39. Talk
  40. Variational Prediction
    Alexander A Alemi, Ben Poole
  41. Talk
  42. How to Train Your FALCON: Learning Log-Concave Densities with Energy-Based Neural Networks
    Alexander Lin, Demba E. Ba
  43. Poster
  44. A Study of Bayesian Neural Network Surrogates for Bayesian Optimization
    Yucen Lily Li, Tim G. J. Rudner, Andrew Gordon Wilson
  45. Poster
  46. Long-tailed Classification from a Bayesian-decision-theory Perspective
    Bolian Li, Ruqi Zhang
  47. Talk
  48. Automatically Marginalized MCMC in Probabilistic Programming
    Jinlin Lai, Javier Burroni, Hui Guan, Daniel Sheldon
  49. Poster
  50. Variational Bayesian Last Layers
    James Harrison, John Willes, Jasper Snoek
  51. Poster
  52. Refining Amortized Posterior Approximations using Gradient-Based Summary Statistics
    Rafael Orozco, Ali Siahkoohi, Mathias Louboutin, Felix Johan Herrmann
  53. Poster
  54. Approximate inference by broadening the support of the likelihood
    Michael Wojnowicz, Martin D Buck, Michael C Hughes
  55. Poster
  56. Visual Explanations of Image-Text Representations via Multi-Modal Information Bottleneck Attribution
    Ying Wang, Tim G. J. Rudner, Andrew Gordon Wilson
  57. Talk
  58. Neural Adaptive Smoothing via Twisting
    Michael Y. Li, Dieterich Lawson, Scott Linderman
  59. Poster
  60. Amortised Inference in Neural Networks for Small-Scale Probabilistic Meta-Learning
    Matthew Ashman, Tommy Rochussen, Adrian Weller
  61. Poster
  62. Clustering inconsistency for Pitman--Yor mixture models with a prior on the precision but fixed discount parameter
    Caroline Lawless, Julyan Arbel, Louise Alamichel, Guillaume KON KAM KING
  63. Poster
  64. Variational Bayes Made Easy
    Mohammad Emtiyaz Khan
  65. Poster
  66. Independent Mechanism Analysis in GPLVMs
    Patrik Reizinger, Han-Bo Li, Aditya Ravuri, Ferenc Huszár, Neil D Lawrence
  67. Talk
  68. Online Laplace Model Selection Revisited
    Jihao Andreas Lin, Javier Antoran, José Miguel Hernández-Lobato

Fast Track

  1. Poster
  2. Optimally-weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference
    Ayush Bharti, Masha Naslidnyk, Oscar Key, Samuel Kaski, Francois-Xavier Briol
  3. Poster
  4. Graphically Structured Diffusion Models
    Christian Dietrich Weilbach, William Harvey, Frank Wood
  5. Poster
  6. Group Distributionally Robust Reinforcement Learning with Hierarchical Latent Variables
    Mengdi Xu, Peide Huang, Yaru Niu, Visak Kumar, Jielin Qiu, Chao Fang, Kuan-Hui Lee, Xuewei Qi, Henry Lam, Bo Li, Ding Zhao
  7. Poster
  8. Sampling-based inference for large linear models with application to linearised Laplace
    Javier Antoran, Shreyas Padhy, Riccardo Barbano, Eric Nalisnick, David Janz, José Miguel Hernández-Lobato
  9. Poster
  10. Logit-Based Ensemble Distribution Distillation for Robust Autoregressive Sequence Uncertainties
    Yassir Fathullah, Guoxuan Xia, Mark Gales
  11. Poster
  12. Learning Group Importance using the Differentiable Hypergeometric Distribution
    Thomas M. Sutter, Laura Manduchi, Alain Ryser, Julia E Vogt
  13. Poster
  14. Massively Scaling Heteroscedastic Classifiers
    Mark Collier, Rodolphe Jenatton, Basil Mustafa, Neil Houlsby, Jesse Berent, Efi Kokiopoulou
  15. Poster
  16. Robust and Scalable Bayesian Online Changepoint Detection
    Matias Altamirano, Francois-Xavier Briol, Jeremias Knoblauch
  17. Poster
  18. Drug Discovery under Covariate Shift with Domain-Informed Prior Distributions over Functions
    Leo Klarner, Tim G. J. Rudner, Michael Reutlinger, Torsten Schindler, Garrett M Morris, Charlotte Deane, Yee Whye Teh
  19. Poster
  20. Function-Space Regularization in Neural Networks: A Probabilistic Perspective
    Tim G. J. Rudner, Sanyam Kapoor, Shikai Qiu, Andrew Gordon Wilson
  21. Poster
  22. Approximately Bayes-Optimal Pseudo Label Selection
    Julian Rodemann, Jann Goschenhofer, Emilio Dorigatti, Thomas Nagler, Thomas Augustin
  23. Poster
  24. Learning Expressive Priors for Generalization and Uncertainty Estimation in Neural Networks
    Dominik Schnaus, Jongseok Lee, Daniel Cremers, Rudolph Triebel
  25. Poster
  26. Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes
    Ba-Hien Tran, Babak Shahbaba, Stephan Mandt, Maurizio Filippone
  27. Poster
  28. Free-Form Variational Inference for Gaussian Process State-Space Models
    Xuhui Fan, Edwin V. Bonilla, Terence O'kane, Scott A Sisson
  29. Poster
  30. Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels
    Alexander Immer, Tycho F. A. van der Ouderaa, Mark van der Wilk, Gunnar Ratsch, Bernhard Schölkopf
  31. Poster
  32. MMVAE+: Enhancing the Generative Quality of Multimodal VAEs without Compromises
    Emanuele Palumbo, Imant Daunhawer, Julia E Vogt