162 citations · 579 across the 17 of their papers we have counts for
11 papers · 1 filter
Score-Based Diffusion meets Annealed Importance Sampling
Arnaud Doucet, Will Grathwohl, Alexander G. D. G. Matthews +1
More than twenty years after its introduction, Annealed Importance Sampling (AIS) remains one of the most effective methods for marginal likelihood estimation. It relies on a seque…
NeRF-VAE: A Geometry Aware 3D Scene Generative Model
Adam R. Kosiorek, Heiko Strathmann, Daniel Zoran +4
We propose NeRF-VAE, a 3D scene generative model that incorporates geometric structure via NeRF and differentiable volume rendering. In contrast to NeRF, our model takes into accou…
Meta-Learning Mean Functions for Gaussian Processes
Vincent Fortuin, Heiko Strathmann, Gunnar Rätsch
When fitting Bayesian machine learning models on scarce data, the main challenge is to obtain suitable prior knowledge and encode it into the model. Recent advances in meta-learnin…
Learning deep kernels for exponential family densities
Li Wenliang, Danica J. Sutherland, Heiko Strathmann +1
The kernel exponential family is a rich class of distributions, which can be fit efficiently and with statistical guarantees by score matching. Being required to choose a priori a…
Scalable Gaussian Processes on Discrete Domains
Vincent Fortuin, Gideon Dresdner, Heiko Strathmann +1
Kernel methods on discrete domains have shown great promise for many challenging data types, for instance, biological sequence data and molecular structure data. Scalable kernel me…
Efficient and principled score estimation with Nyström kernel exponential families
Danica J. Sutherland, Heiko Strathmann, Michael Arbel +1
We propose a fast method with statistical guarantees for learning an exponential family density model where the natural parameter is in a reproducing kernel Hilbert space, and may…
Generative Models and Model Criticism via Optimized Maximum Mean Discrepancy
Danica J. Sutherland, Hsiao-Yu Tung, Heiko Strathmann +4
We propose a method to optimize the representation and distinguishability of samples from two probability distributions, by maximizing the estimated power of a statistical test bas…
A Kernel Test of Goodness of Fit
Kacper Chwialkowski, Heiko Strathmann, Arthur Gretton
We propose a nonparametric statistical test for goodness-of-fit: given a set of samples, the test determines how likely it is that these were generated from a target density functi…
Gradient-free Hamiltonian Monte Carlo with Efficient Kernel Exponential Families
Heiko Strathmann, Dino Sejdinovic, Samuel Livingstone +2
We propose Kernel Hamiltonian Monte Carlo (KMC), a gradient-free adaptive MCMC algorithm based on Hamiltonian Monte Carlo (HMC). On target densities where classical HMC is not an o…
Unbiased Bayes for Big Data: Paths of Partial Posteriors
Heiko Strathmann, Dino Sejdinovic, Mark Girolami
A key quantity of interest in Bayesian inference are expectations of functions with respect to a posterior distribution. Markov Chain Monte Carlo is a fundamental tool to consisten…
Kernel Adaptive Metropolis-Hastings
Dino Sejdinovic, Heiko Strathmann, Maria Lomeli Garcia +2
A Kernel Adaptive Metropolis-Hastings algorithm is introduced, for the purpose of sampling from a target distribution with strongly nonlinear support. The algorithm embeds the traj…