From the 1 of 9 linked papers with an AI index.
9 papers
LatentFlow: A General Framework for Conditioning Stochastic Processes
Louis Sharrock, Lachlan Astfalck, Henry Moss
The paper presents LatentFlow, a training‑free framework that conditions stochastic processes by mapping them to a tractable latent space and performing guided probability flow, al…
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…