Chris Nemeth
Papers
Code
Software
Books
Blog
Group
2024
August
Gaussian Processes in Julia
Scalable Monte Carlo for Bayesian Learning
Stochastic Gradient MCMC in Jax
July
Metropolis-Hastings with Scalable Subsampling
Learning-Rate-Free Stochastic Optimization over Riemannian Manifolds
Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI
June
Stochastic Gradient Piecewise Deterministic Monte Carlo Samplers
Diffusion Generative Modelling for Divide-and-Conquer MCMC
May
Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing Flows
Robust Bayesian Nonparametric Variable Selection for Linear Regression
April
Tuning-Free Maximum Likelihood Training of Latent Variable Models via Coin Betting
March
Spatial Latent Gaussian Modelling with Change of Support
2023
December
Latent space modelling of hypergraph data
SwISS: A scalable Markov chain Monte Carlo divide-and-conquer strategy
November
Learning Rate Free Sampling in Constrained Domains
October
A changepoint approach to modelling non-stationary soil moisture dynamics
August
Characterising the ice sheet surface in Northeast Greenland using Sentinel-1 SAR data
July
Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates
June
Stochastic Gradient MCMC for Nonlinear State Space Models
April
Preferential Subsampling for Stochastic Gradient Langevin Dynamics
Transport Elliptical Slice Sampling
Multivariate sensitivity analysis for a large-scale climate impact and adaptation model
Efficient and generalizable tuning strategies for stochastic gradient MCMC
March
Sequential Estimation of Temporally Evolving Latent Space Network Models
2022
June
Modelling Populations of Interaction Networks via Distance Metrics
Distances for Comparing Multisets and Sequences
Semi-exact control functionals from Sard’s method
April
GaussianProcesses.jl: A Nonparametric Bayes Package for the Julia Language
SGMCMCJax: a lightweight JAX library for stochastic gradient Markov chain Monte Carlo algorithms
2021
June
Gaussian processes on hypergraphs
April
A Probabilistic Assessment of the COVID-19 Lockdown on Air Quality in the UK
January
Stochastic Gradient Markov Chain Monte Carlo
2020
September
Stein variational Gaussian processes
Bayesian calibration of firn densification models
2019
December
Pseudo-extended Markov chain Monte Carlo
October
sgmcmc: An R package for stochastic gradient Markov chain Monte Carlo
May
Control variates for stochastic gradient MCMC
2018
December
Large-Scale Stochastic Sampling from the Probability Simplex
June
Merging MCMC Subposteriors through Gaussian-Process Approximations
2016
October
Particle approximations of the score and observed information matrix for parameter estimation in state–space models with linear computational cost
August
Particle Metropolis-adjusted Langevin algorithms
2014
October
Parameter estimation for state space models using sequential Monte Carlo algorithms
April
Sequential Monte Carlo methods for state and parameter estimation in abruptly changing environments
2012
July
Bearings-only tracking with particle filtering for joint parameter learning and state estimation
January
Particle learning methods for state and parameter estimation