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