10 citations · 34 across the 17 of their papers we have counts for
8 papers · 1 filter
On the Optimisation Landscape of Overparameterised Weighted MMD
Tharrshann Jayan Logarajah, Jeremias Knoblauch, Louis Sharrock
We study the optimisation landscape of the maximum mean discrepancy (MMD) over weighted atomic measures. For the one-dimensional Gaussian kernel, we show that, for any compactly su…
Predictively Oriented Gaussian Process Posteriors
Callum Lau, Jeremias Knoblauch, Louis Sharrock
Gaussian Processes (GPs) are a powerful tool for modelling and quantifying uncertainty in functional relationships. However, they require practitioners to make a number of design d…
LatentFlow: A General Framework for Conditioning Stochastic Processes
Louis Sharrock, Lachlan Astfalck, Henry Moss
Stochastic-process models are, as a rule, far easier to simulate than to condition. Non-linear observations, non-Gaussian likelihoods, black-box information, and global constraints…
Wasserstein Gradient Flows for Batch Bayesian Optimal Experimental Design
Louis Sharrock
Bayesian optimal experimental design (BOED) provides a powerful, decision-theoretic framework for selecting experiments so as to maximise the expected utility of the data to be col…
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…
Sequential Neural Score Estimation: Likelihood-Free Inference with Conditional Score Based Diffusion Models
Louis Sharrock, Jack Simons, Song Liu +1
We introduce Sequential Neural Posterior Score Estimation (SNPSE), a score-based method for Bayesian inference in simulator-based models. Our method, inspired by the remarkable suc…