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01/13/2023
Laser: Latent Set Representations for 3D Generative Modeling
NeRF provides unparalleled fidelity of novel view synthesis: rendering a...
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08/16/2022
Score-Based Diffusion meets Annealed Importance Sampling
More than twenty years after its introduction, Annealed Importance Sampl...
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07/22/2021
Neural Variational Gradient Descent
Particle-based approximate Bayesian inference approaches such as Stein V...
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04/01/2021
NeRF-VAE: A Geometry Aware 3D Scene Generative Model
We propose NeRF-VAE, a 3D scene generative model that incorporates geome...
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03/01/2021
Persistent Message Passing
Graph neural networks (GNNs) are a powerful inductive bias for modelling...
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11/20/2018
Learning deep kernels for exponential family densities
The kernel exponential family is a rich class of distributions,which can...
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10/24/2018
Scalable Gaussian Processes on Discrete Domains
Kernel methods on discrete domains have shown great promise for many cha...
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06/06/2018
Deep Self-Organization: Interpretable Discrete Representation Learning on Time Series
Human professionals are often required to make decisions based on comple...
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09/11/2017
A determinant-free method to simulate the parameters of large Gaussian fields
We propose a determinant-free approach for simulation-based Bayesian inf...
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05/23/2017
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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11/14/2016
Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy
We propose a method to optimize the representation and distinguishabilit...
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02/09/2016
A Kernel Test of Goodness of Fit
We propose a nonparametric statistical test for goodness-of-fit: given a...
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10/11/2015
Kernel Sequential Monte Carlo
We propose kernel sequential Monte Carlo (KSMC), a framework for samplin...
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06/08/2015
Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families
We propose Kernel Hamiltonian Monte Carlo (KMC), a gradient-free adaptiv...
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01/14/2015
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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07/19/2013
Kernel Adaptive Metropolis-Hastings
A Kernel Adaptive Metropolis-Hastings algorithm is introduced, for the p...
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06/17/2013