1 citations · 1 across the 9 of their papers we have counts for
10 papers
Spatial Prediction with Guided Diffusion
Andrew Zammit-Mangion, Lachlan Astfalck, Raphaël Huser +2
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…
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…
Hypergraph Generation via Structured Stochastic Diffusion
Christopher Nemeth
Hypergraphs model higher-order interactions, but realistic hypergraph generation remains difficult because incidence, hyperedge-size heterogeneity, and overlap structure are not fa…
Generator-based Graph Generation via Heat Diffusion
Anthony Stephenson, Ian Gallagher, Christopher Nemeth
Graph generative modelling has become an essential task due to the wide range of applications in chemistry, biology, social networks, and knowledge representation. In this work, we…
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…
Control Variate-based Stochastic Sampling from the Probability Simplex
Francesco Barile, Christopher Nemeth
This paper presents a control variate-based Markov chain Monte Carlo algorithm for efficient sampling from the probability simplex, with a focus on applications in large-scale Baye…
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…
Spatial Latent Gaussian Modelling with Change of Support
Erick A. Chacón-Montalván, Peter M. Atkinson, Christopher Nemeth +2
Spatial data are often derived from multiple sources (e.g. satellites, in-situ sensors, survey samples) with different supports, but associated with the same properties of a spatia…
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI
Theodore Papamarkou, Maria Skoularidou, Konstantina Palla +22
In the current landscape of deep learning research, there is a predominant emphasis on achieving high predictive accuracy in supervised tasks involving large image and language dat…