10 citations · 34 across the 17 of their papers we have counts for
20 papers
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…
Markov chain Monte Carlo for predictively oriented posteriors
Yann McLatchie, Louis Sharrock, David T. Frazier +1
The predictively oriented posterior offers principled uncertainty quantification, even under model misspecification. However, it does not admit an explicit density and therefore ca…
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…
A Gradient Flow Perspective on Minimum MMD Estimation
Sophia Seulkee Kang, Louis Sharrock, Xiaoyuan Cheng +2
Minimum maximum mean discrepancy (MMD) estimation has emerged as a robust and likelihood-free alternative to maximum likelihood estimation for parameter estimation. Yet, despite it…
Recursive Maximum Likelihood Estimation for Interacting Particle Systems using Virtual Particles
Louis Sharrock, Nikolas Kantas, Grigorios A. Pavliotis
We study recursive maximum likelihood estimation for stochastic interacting particle systems based on continuous observation of a single particle. In this regime, consistent estima…
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…
Efficient Online Learning in Interacting Particle Systems
Louis Sharrock, Nikolas Kantas, Grigorios A. Pavliotis
We introduce a new method for online parameter estimation in stochastic interacting particle systems, based on continuous observation of a small number of particles from the system…
Tuning-Free Sampling via Optimization on the Space of Probability Measures
Louis Sharrock, Christopher Nemeth
We introduce adaptive, tuning-free step size schedules for gradient-based sampling algorithms obtained as time-discretizations of Wasserstein gradient flows. The result is a suite…
Learning-Rate-Free Stochastic Optimization over Riemannian Manifolds
Daniel Dodd, Louis Sharrock, Christopher Nemeth
In recent years, interest in gradient-based optimization over Riemannian manifolds has surged. However, a significant challenge lies in the reliance on hyperparameters, especially…
Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing Flows
Alberto Cabezas, Louis Sharrock, Christopher Nemeth
Continuous normalizing flows (CNFs) learn the probability path between a reference distribution and a target distribution by modeling the vector field generating said path using ne…
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…
Two-Timescale Stochastic Approximation for Bilevel Optimisation Problems in Continuous-Time Models
Louis Sharrock
We analyse the asymptotic properties of a continuous-time, two-timescale stochastic approximation algorithm designed for stochastic bilevel optimisation problems in continuous-time…
F-Divergences and Cost Function Locality in Generative Modelling with Quantum Circuits
Chiara Leadbeater, Louis Sharrock, Brian Coyle +1
Generative modelling is an important unsupervised task in machine learning. In this work, we study a hybrid quantum-classical approach to this task, based on the use of a quantum c…
Parameter Estimation for the McKean-Vlasov Stochastic Differential Equation
Louis Sharrock, Nikolas Kantas, Panos Parpas +1
We consider the problem of parameter estimation for a stochastic McKean-Vlasov equation, and the associated system of weakly interacting particles. We study two cases: one in which…
Joint Online Parameter Estimation and Optimal Sensor Placement for the Partially Observed Stochastic Advection-Diffusion Equation
Louis Sharrock, Nikolas Kantas
In this paper, we consider the problem of jointly performing online parameter estimation and optimal sensor placement for a partially observed infinite dimensional linear diffusion…
Two-Timescale Stochastic Gradient Descent in Continuous Time with Applications to Joint Online Parameter Estimation and Optimal Sensor Placement
Louis Sharrock, Nikolas Kantas
In this paper, we establish the almost sure convergence of two-timescale stochastic gradient descent algorithms in continuous time under general noise and stability conditions, ext…