Spatial prediction is a critically important task in data analyses that involve spatially or spatio-temporally-referenced data. It is commonly carried out using hierarchical models, which comprise a process model describing the latent process of interest, and a data model describing how the process is observed. The tar...
Andrew Zammit-Mangion, L. Astfalck, Raphael Huser et al.· 0 citations
Reinforcement learning with verifiable rewards (RLVR) has substantially improved LLM reasoning, yet growing evidence suggests it mainly performs distribution sharpening: concentrating probability mass on reasoning paths the base model already produces. Recent work therefore seeks RLVR's gains without needing reward sig...
Abiel Malkani Talwar, Christopher Nemeth, Henry B. Moss· 0 citations
CUTS-GPR enables Bayesian modelling of high-dimensional PESs - a longstanding challenge in computational chemistry by running full GPR calculations, including hyperparameter optimization, on synthetic datasets with up to 447 data points and 500 dimensions.
Mads Greisen Ho̷jlund, August Lykke-Møller, Henry B. Moss et al.· arXiv.org· 0 citations
Outside the linear-Gaussian regime, conditional sampling from Gaussian processes (GPs) is challenging. Recent methods such as FlowGP (Moss et al., (2026)) can condition on arbitrary non-linear and non-Gaussian statements, but at considerable cost: an expensive iterative and high-dimensional diffusion that requires hand...
T. Cowperthwaite, Louis Sharrock, L. Astfalck et al.· 0 citations
LatentFlow is introduced, a single framework for conditioning stochastic processes, with no learned neural approximations and no training, that enables conditional sampling in seconds on a single desktop CPU across model classes that have never shared a scalable method.
Louis Sharrock, L. Astfalck, Henry B. Moss· arXiv.org· 0 citations
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