1 citations · 1 across the 9 of their papers we have counts for
5 papers · 1 filter
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…