86 citations · 121 across the 27 of their papers we have counts for
31 papers
Stochastic Gradient Piecewise Deterministic Monte Carlo Samplers
Paul Fearnhead, Sebastiano Grazzi, Chris Nemeth +1
Recent work has suggested using Monte Carlo methods based on piecewise deterministic Markov processes (PDMPs) to sample from target distributions of interest. PDMPs are non-reversi…
Diffusion Generative Modelling for Divide-and-Conquer MCMC
C. Trojan, P. Fearnhead, C. Nemeth
Divide-and-conquer MCMC is a strategy for parallelising Markov Chain Monte Carlo sampling by running independent samplers on disjoint subsets of a dataset and merging their output.…
Learning Rate Free Sampling in Constrained Domains
Louis Sharrock, Lester Mackey, Christopher Nemeth
We introduce a suite of new particle-based algorithms for sampling in constrained domains which are entirely learning rate free. Our approach leverages coin betting ideas from conv…
Tuning-Free Maximum Likelihood Training of Latent Variable Models via Coin Betting
Louis Sharrock, Daniel Dodd, Christopher Nemeth
We introduce two new particle-based algorithms for learning latent variable models via marginal maximum likelihood estimation, including one which is entirely tuning-free. Our meth…
Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates
Louis Sharrock, Christopher Nemeth
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 in…
Preferential Subsampling for Stochastic Gradient Langevin Dynamics
Srshti Putcha, Christopher Nemeth, Paul Fearnhead
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, uniform…