4 citations · 6 across the 8 of their papers we have counts for
9 papers
Cytoarchitecture in Words: Weakly Supervised Vision-Language Modeling for Human Brain Microscopy
Matthew Sutton, Katrin Amunts, Timo Dickscheid +1
Vision foundation models increasingly support interactive scientific workflows, but natural-language interaction requires coupling visual representations to language. Curated image…
Scalable Vertical Federated Learning via Data Augmentation and Amortized Inference
Conor Hassan, Matthew Sutton, Antonietta Mira +1
Vertical federated learning (VFL) has emerged as a paradigm for collaborative model estimation across multiple clients, each holding a distinct set of covariates. This paper introd…
Debiasing Piecewise Deterministic Markov Process samplers using couplings
Adrien Corenflos, Matthew Sutton, Nicolas Chopin
Monte Carlo methods -- such as Markov chain Monte Carlo (MCMC) and piecewise deterministic Markov process (PDMP) samplers -- provide asymptotically exact estimators of expectations…
Transport Reversible Jump Proposals
Laurence Davies, Robert Salomone, Matthew Sutton +1
Reversible jump Markov chain Monte Carlo (RJMCMC) proposals that achieve reasonable acceptance rates and mixing are notoriously difficult to design in most applications. Inspired b…
Continuously-Tempered PDMP Samplers
Matthew Sutton, Robert Salomone, Augustin Chevallier +1
New sampling algorithms based on simulating continuous-time stochastic processes called piece-wise deterministic Markov processes (PDMPs) have shown considerable promise. However,…
Concave-Convex PDMP-based sampling
Matthew Sutton, Paul Fearnhead
Recently non-reversible samplers based on simulating piecewise deterministic Markov processes (PDMPs) have shown potential for efficient sampling in Bayesian inference problems. Ho…
Bayesian Detectability of Induced Polarisation in Airborne Electromagnetic Data using Reversible Jump Sequential Monte Carlo
Laurence Davies, Alan Yusen Ley-Cooper, Matthew Sutton +1
Detection of induced polarisation (IP) effects in airborne electromagnetic (AEM) measurements does not yet have an established methodology. This contribution develops a Bayesian ap…
Reversible Jump PDMP Samplers for Variable Selection
Augustin Chevallier, Paul Fearnhead, Matthew Sutton
A new class of Markov chain Monte Carlo (MCMC) algorithms, based on simulating piecewise deterministic Markov processes (PDMPs), have recently shown great promise: they are non-rev…
A Unified Parallel Algorithm for Regularized Group PLS Scalable to Big Data
Pierre Lafaye de Micheaux, Benoit Liquet, Matthew Sutton
Partial Least Squares (PLS) methods have been heavily exploited to analyse the association between two blocs of data. These powerful approaches can be applied to data sets where th…