
Neural Variational Gradient Descent
Particlebased approximate Bayesian inference approaches such as Stein V...
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NeRFVAE: A Geometry Aware 3D Scene Generative Model
We propose NeRFVAE, a 3D scene generative model that incorporates geome...
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Persistent Message Passing
Graph neural networks (GNNs) are a powerful inductive bias for modelling...
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Learning deep kernels for exponential family densities
The kernel exponential family is a rich class of distributions,which can...
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Scalable Gaussian Processes on Discrete Domains
Kernel methods on discrete domains have shown great promise for many cha...
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Deep SelfOrganization: Interpretable Discrete Representation Learning on Time Series
Human professionals are often required to make decisions based on comple...
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A determinantfree method to simulate the parameters of large Gaussian fields
We propose a determinantfree approach for simulationbased Bayesian inf...
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Efficient and principled score estimation with Nyström kernel exponential families
We propose a fast method with statistical guarantees for learning an exp...
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Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy
We propose a method to optimize the representation and distinguishabilit...
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A Kernel Test of Goodness of Fit
We propose a nonparametric statistical test for goodnessoffit: given a...
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Kernel Sequential Monte Carlo
We propose kernel sequential Monte Carlo (KSMC), a framework for samplin...
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Gradientfree Hamiltonian Monte Carlo with Efficient Kernel Exponential Families
We propose Kernel Hamiltonian Monte Carlo (KMC), a gradientfree adaptiv...
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Unbiased Bayes for Big Data: Paths of Partial Posteriors
A key quantity of interest in Bayesian inference are expectations of fun...
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Kernel Adaptive MetropolisHastings
A Kernel Adaptive MetropolisHastings algorithm is introduced, for the p...
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On Russian Roulette Estimates for Bayesian Inference with DoublyIntractable Likelihoods
A large number of statistical models are "doublyintractable": the likel...
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Heiko Strathmann
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