From the 1 of 11 linked papers with an AI index.
11 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…
Dynamic Gaussian Processes and the Vanilla-SPDE Exchange
Rui-Yang Zhang, Lachlan Astfalck, Edward Cripps +2
Gaussian process inference is often limited by cubic computational costs, a challenge that becomes more pronounced in spatio-temporal settings where posterior inference is required…
BALLAST: Bayesian Active Learning with Look-ahead Amendment for Sea-drifter Trajectories under Spatio-Temporal Vector Fields
Rui-Yang Zhang, Lachlan Astfalck, Edward Cripps +2
We introduce a formal active learning methodology for guiding the placement of Lagrangian observers to infer time-dependent vector fields -- a key task in oceanography, marine scie…
Conditioning Gaussian Processes on Almost Anything
Henry Moss, Lachlan Astfalck, Thomas Cowperthwaite +5
Gaussian processes (GPs) offer a principled probabilistic model over functions, but exact inference is restricted to the linear-Gaussian regime. We establish an explicit equivalenc…
Posterior Projection for Inference in Constrained Spaces
Lachlan Astfalck, Deborshee Sen, Sayan Patra +2
Estimation of parameters that obey specific constraints is crucial in statistics and machine learning; for example, when parameters are required to satisfy boundedness, monotonicit…
Hybrid physics-data driven spectral forecasts of semisubmersible response
Ian Milne, Lachlan Astfalck, Matthew Zed +2
A framework for probabilistic forecasting of vessel motion is developed and validated for a semisubmersible operating in long period swell. Bayesian statistical methods are applied…