Search Results for author:
Found 19 papers, 11 papers with code
Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI
In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language datasets.
Tuning-Free Maximum Likelihood Training of Latent Variable Models via Coin Betting
Our methods are based on the perspective of marginal maximum likelihood estimation as an optimization problem: namely, as the minimization of a free energy functional.
Learning Rate Free Sampling in Constrained Domains
We introduce a suite of new particle-based algorithms for sampling in constrained domains which are entirely learning rate free.
Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates
In recent years, particle-based variational inference (ParVI) methods such as Stein variational gradient descent (SVGD) have grown in popularity as scalable methods for Bayesian inference.
Preferential Subsampling for Stochastic Gradient Langevin Dynamics
Stochastic gradient MCMC (SGMCMC) offers a scalable alternative to traditional MCMC, by constructing an unbiased estimate of the gradient of the log-posterior with a small, uniformly-weighted subsample of the data.
SwISS: A Scalable Markov chain Monte Carlo Divide-and-Conquer Strategy
Divide-and-conquer strategies for Monte Carlo algorithms are an increasingly popular approach to making Bayesian inference scalable to large data sets.
Gaussian Processes on Hypergraphs
We derive a Matern Gaussian process (GP) on the vertices of a hypergraph.
Efficient and Generalizable Tuning Strategies for Stochastic Gradient MCMC
Stochastic gradient Markov chain Monte Carlo (SGMCMC) is a popular class of algorithms for scalable Bayesian inference.
Stein Variational Gaussian Processes
We show how to use Stein variational gradient descent (SVGD) to carry out inference in Gaussian process (GP) models with non-Gaussian likelihoods and large data volumes.
Stochastic gradient Markov chain Monte Carlo
In this paper, we focus on a particular class of scalable Monte Carlo algorithms, stochastic gradient Markov chain Monte Carlo (SGMCMC) which utilises data subsampling techniques to reduce the per-iteration cost of MCMC.
Stochastic Gradient MCMC for Nonlinear State Space Models
We evaluate our proposed particle buffered stochastic gradient using stochastic gradient MCMC for inference on both long sequential synthetic and minute-resolution financial returns data, demonstrating the importance of this class of methods.
GaussianProcesses.jl: A Nonparametric Bayes package for the Julia Language
Gaussian processes are a class of flexible nonparametric Bayesian tools that are widely used across the sciences, and in industry, to model complex data sources.
Large-Scale Stochastic Sampling from the Probability Simplex
Unfortunately, many popular large-scale Bayesian models, such as network or topic models, require inference on sparse simplex spaces.
sgmcmc: An R Package for Stochastic Gradient Markov Chain Monte Carlo
To do this, the package uses the software library TensorFlow, which has a variety of statistical distributions and mathematical operations as standard, meaning a wide class of models can be built using this framework.
Pseudo-extended Markov chain Monte Carlo
In this paper, we introduce the pseudo-extended MCMC method as a simple approach for improving the mixing of the MCMC sampler for multi-modal posterior distributions.
Control Variates for Stochastic Gradient MCMC
These methods use a noisy estimate of the gradient of the log posterior, which reduces the per iteration computational cost of the algorithm.
Merging MCMC Subposteriors through Gaussian-Process Approximations
This approximation is exploited through three methodologies: firstly a Hamiltonian Monte Carlo algorithm targeting the expectation of the posterior density provides a sample from an approximation to the posterior; secondly, evaluating the true posterior at the sampled points leads to an importance sampler that, asymptotically, targets the true posterior expectations; finally, an alternative importance sampler uses the full Gaussian-process distribution of the approximation to the log-posterior density to re-weight any initial sample and provide both an estimate of the posterior expectation and a measure of the uncertainty in it.
Particle Metropolis-adjusted Langevin algorithms
This paper proposes a new sampling scheme based on Langevin dynamics that is applicable within pseudo-marginal and particle Markov chain Monte Carlo algorithms.
Particle approximations of the score and observed information matrix for parameter estimation in state space models with linear computational cost
This paper introduces an alternative approach for estimating these terms at a computational cost that is linear in the number of particles.
