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Bivariate Causal Discovery using Bayesian Model Selection

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Learning in Deep Factor Graphs with Gaussian Belief Propagation

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Variational Inference Failures Under Model Symmetries: Permutation Invariant Posteriors for Bayesian Neural Networks

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Actually Sparse Variational Gaussian Processes

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Learning Layer-wise Equivariances Automatically using Gradients

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Stochastic Marginal Likelihood Gradients using Neural Tangent Kernels

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Invariance Learning in Deep Neural Networks with Differentiable Laplace Approximations

Memory Safe Computations with XLA Compiler

Relaxing Equivariance Constraints with Non-stationary Continuous Filters

SnAKe: Bayesian Optimization with Pathwise Exploration