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.

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Main Track

Poster	Quasi-Bayesian Density Estimation via Autoregressive Predictives Sahra Ghalebikesabi, Christopher C. Holmes, Edwin Fong, Brieuc Lehmann
Poster	Dimensionality Reduction as Probabilistic Inference Aditya Ravuri, Francisco Vargas, Vidhi Lalchand, Neil D Lawrence
Poster	SAMBA: Regularized Autoencoders perform Sharpness-Aware Minimization Patrik Reizinger, Ferenc Huszár
Poster	Robust Hybrid Learning With Expert Augmentation Antoine Wehenkel, Jens Behrmann, Hsiang Hsu, Guillermo Sapiro, Gilles Louppe, Joern-Henrik Jacobsen
Poster	Expressiveness Remarks for Denoising Diffusion Based Sampling Francisco Vargas, Teodora Reu, Anna Kerekes
Poster	Function-Space Regularization for Deep Bayesian Classification Jihao Andreas Lin, Joe Watson, Pascal Klink, Jan Peters
Poster	Attacking Bayes: Are Bayesian Neural Networks Inherently Robust? Yunzhen Feng, Tim G. J. Rudner, Nikolaos Tsilivis, Julia Kempe
Poster	An Information-Theoretic Perspective on Variance-Invariance-Covariance Regularization Ravid Shwartz-Ziv, Randall Balestriero, Kenji Kawaguchi, Tim G. J. Rudner, Yann LeCun
Poster	Variational Partitioning Thomas M. Sutter, Alain Ryser, Joram Liebeskind, Julia E Vogt
Poster	Sample Average Approximation for Black-Box VI Javier Burroni, Justin Domke, Daniel Sheldon
Poster	Balancing Simulation-based Inference for Conservative Posteriors Arnaud Delaunoy, Benjamin Kurt Miller, Patrick Forré, Christoph Weniger, Gilles Louppe
Poster	Promises and Pitfalls of the Linearized Laplace in Bayesian Optimization Agustinus Kristiadi, Alexander Immer, Runa Eschenhagen, Vincent Fortuin
Poster	Indirect Functional Bayesian Neural Networks Mengjing Wu, Junyu Xuan, Jie Lu
Poster	Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Models Jack Simons, Louis Sharrock, Song Liu, Mark Beaumont
Poster	Linearized Laplace Inference in Neural Additive Models Kouroche Bouchiat, Alexander Immer, Hugo Yèche, Vincent Fortuin
Poster	Uncertainty in Graph Contrastive Learning with Bayesian Neural Networks Alexander Möllers, Alexander Immer, Elvin Isufi, Vincent Fortuin
Poster	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
Poster	A Dual Control Variate for accelerated black-box variational inference Xi Wang, Tomas Geffner, Justin Domke
Poster	Individual Fairness in Bayesian Neural Networks Alice Doherty, Matthew Robert Wicker, Luca Laurenti, Andrea Patane
Talk	Variational Prediction Alexander A Alemi, Ben Poole
Talk	How to Train Your FALCON: Learning Log-Concave Densities with Energy-Based Neural Networks Alexander Lin, Demba E. Ba
Poster	A Study of Bayesian Neural Network Surrogates for Bayesian Optimization Yucen Lily Li, Tim G. J. Rudner, Andrew Gordon Wilson
Poster	Long-tailed Classification from a Bayesian-decision-theory Perspective Bolian Li, Ruqi Zhang
Talk	Automatically Marginalized MCMC in Probabilistic Programming Jinlin Lai, Javier Burroni, Hui Guan, Daniel Sheldon
Poster	Variational Bayesian Last Layers James Harrison, John Willes, Jasper Snoek
Poster	Refining Amortized Posterior Approximations using Gradient-Based Summary Statistics Rafael Orozco, Ali Siahkoohi, Mathias Louboutin, Felix Johan Herrmann
Poster	Approximate inference by broadening the support of the likelihood Michael Wojnowicz, Martin D Buck, Michael C Hughes
Poster	Visual Explanations of Image-Text Representations via Multi-Modal Information Bottleneck Attribution Ying Wang, Tim G. J. Rudner, Andrew Gordon Wilson
Talk	Neural Adaptive Smoothing via Twisting Michael Y. Li, Dieterich Lawson, Scott Linderman
Poster	Amortised Inference in Neural Networks for Small-Scale Probabilistic Meta-Learning Matthew Ashman, Tommy Rochussen, Adrian Weller
Poster	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
Poster	Variational Bayes Made Easy Mohammad Emtiyaz Khan
Poster	Independent Mechanism Analysis in GPLVMs Patrik Reizinger, Han-Bo Li, Aditya Ravuri, Ferenc Huszár, Neil D Lawrence
Talk	Online Laplace Model Selection Revisited Jihao Andreas Lin, Javier Antoran, José Miguel Hernández-Lobato

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Fast Track

Poster	Optimally-weighted Estimators of the Maximum Mean Discrepancy for Likelihood-Free Inference Ayush Bharti, Masha Naslidnyk, Oscar Key, Samuel Kaski, Francois-Xavier Briol
Poster	Graphically Structured Diffusion Models Christian Dietrich Weilbach, William Harvey, Frank Wood
Poster	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
Poster	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
Poster	Logit-Based Ensemble Distribution Distillation for Robust Autoregressive Sequence Uncertainties Yassir Fathullah, Guoxuan Xia, Mark Gales
Poster	Learning Group Importance using the Differentiable Hypergeometric Distribution Thomas M. Sutter, Laura Manduchi, Alain Ryser, Julia E Vogt
Poster	Massively Scaling Heteroscedastic Classifiers Mark Collier, Rodolphe Jenatton, Basil Mustafa, Neil Houlsby, Jesse Berent, Efi Kokiopoulou
Poster	Robust and Scalable Bayesian Online Changepoint Detection Matias Altamirano, Francois-Xavier Briol, Jeremias Knoblauch
Poster	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
Poster	Function-Space Regularization in Neural Networks: A Probabilistic Perspective Tim G. J. Rudner, Sanyam Kapoor, Shikai Qiu, Andrew Gordon Wilson
Poster	Approximately Bayes-Optimal Pseudo Label Selection Julian Rodemann, Jann Goschenhofer, Emilio Dorigatti, Thomas Nagler, Thomas Augustin
Poster	Learning Expressive Priors for Generalization and Uncertainty Estimation in Neural Networks Dominik Schnaus, Jongseok Lee, Daniel Cremers, Rudolph Triebel
Poster	Fully Bayesian Autoencoders with Latent Sparse Gaussian Processes Ba-Hien Tran, Babak Shahbaba, Stephan Mandt, Maurizio Filippone
Poster	Free-Form Variational Inference for Gaussian Process State-Space Models Xuhui Fan, Edwin V. Bonilla, Terence O'kane, Scott A Sisson
Poster	Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels Alexander Immer, Tycho F. A. van der Ouderaa, Mark van der Wilk, Gunnar Ratsch, Bernhard Schölkopf
Poster	MMVAE+: Enhancing the Generative Quality of Multimodal VAEs without Compromises Emanuele Palumbo, Imant Daunhawer, Julia E Vogt