Bearings-only tracking with particle filtering for joint parameter learning and state estimation. - Christopher Nemeth; Paul Fearnhead; Lyudmila Mihaylova; Dave Vorley (FUSION, 2012)

Erik Hollnagel: FRAM: The functional resonance analysis method, modeling complex socio-technical systems - 2012, Ashgate, ISBN 978-1-4094-4551-7, Paperback, £20.00, ISBN 978-1-4094-4552-4, Hardcover, £65.00. - Christopher Nemeth (Cogn. Technol. Work., 2013)

Sequential Monte Carlo Methods for State and Parameter Estimation in Abruptly Changing Environments. - Christopher Nemeth; Paul Fearnhead; Lyudmila Mihaylova (IEEE Trans. Signal Process., 2014)

Large-Scale Stochastic Sampling from the Probability Simplex. - Jack Baker; Paul Fearnhead; Emily B. Fox; Christopher Nemeth (NeurIPS, 2018)

Pseudo-Extended Markov chain Monte Carlo. - Christopher Nemeth; Fredrik Lindsten; Maurizio Filippone; James Hensman (NeurIPS, 2019)

Stochastic Gradient MCMC for Nonlinear State Space Models. - Christopher Aicher; Srshti Putcha; Christopher Nemeth; Paul Fearnhead; Emily B. Fox (CoRR, 2019)

Control variates for stochastic gradient MCMC. - Jack Baker; Paul Fearnhead; Emily B. Fox; Christopher Nemeth (Stat. Comput., 2019)

Stein Variational Gaussian Processes. - Thomas Pinder; Christopher Nemeth; David Leslie (CoRR, 2020)

Gaussian Processes on Hypergraphs. - Thomas Pinder; Kathryn Turnbull; Christopher Nemeth; David Leslie (CoRR, 2021)

SGMCMCJax: a lightweight JAX library for stochastic gradient Markov chain Monte Carlo algorithms. - Jeremie Coullon; Christopher Nemeth (J. Open Source Softw., 2022)

GaussianProcesses.jl: A Nonparametric Bayes Package for the Julia Language. - Jamie Fairbrother; Christopher Nemeth; Maxime Rischard; Johanni Brea; Thomas Pinder (J. Stat. Softw., 2022)

Transport Elliptical Slice Sampling. - Alberto Cabezas; Christopher Nemeth (AISTATS, 2023)

Preferential Subsampling for Stochastic Gradient Langevin Dynamics. - Srshti Putcha; Christopher Nemeth; Paul Fearnhead (AISTATS, 2023)

Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates. - Louis Sharrock; Christopher Nemeth (ICML, 2023)

Learning Rate Free Bayesian Inference in Constrained Domains. - Louis Sharrock; Lester Mackey; Christopher Nemeth (NeurIPS, 2023)

CoinEM: Tuning-Free Particle-Based Variational Inference for Latent Variable Models. - Louis Sharrock; Daniel Dodd; Christopher Nemeth (CoRR, 2023)

Sequential estimation of temporally evolving latent space network models. - Kathryn Turnbull; Christopher Nemeth; Matthew A. Nunes; Tyler H. McCormick (Comput. Stat. Data Anal., 2023)

Efficient and generalizable tuning strategies for stochastic gradient MCMC. - Jeremie Coullon; Leah F. South; Christopher Nemeth (Stat. Comput., 2023)

Tuning-Free Maximum Likelihood Training of Latent Variable Models via Coin Betting. - Louis Sharrock; Daniel Dodd; Christopher Nemeth (AISTATS, 2024)

Learning-Rate-Free Stochastic Optimization over Riemannian Manifolds. - Daniel Dodd; Louis Sharrock; Christopher Nemeth (ICML, 2024)

Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. - Theodore Papamarkou; Maria Skoularidou; Konstantina Palla; Laurence Aitchison; Julyan Arbel; David B. Dunson; Maurizio Filippone; Vincent Fortuin; Philipp Hennig; José Miguel Hernández-Lobato; Aliaksandr Hubin; Alexander Immer; et al. (ICML, 2024)

Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing Flows. - Alberto Cabezas; Louis Sharrock; Christopher Nemeth (NeurIPS, 2024)

Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. - Theodore Papamarkou; Maria Skoularidou; Konstantina Palla; Laurence Aitchison; Julyan Arbel; David B. Dunson; Maurizio Filippone; Vincent Fortuin; Philipp Hennig; José Miguel Hernández-Lobato; Aliaksandr Hubin; Alexander Immer; et al. (CoRR, 2024)

Diffusion Generative Modelling for Divide-and-Conquer MCMC. - C. Trojan; Paul Fearnhead; Christopher Nemeth (CoRR, 2024)

Stochastic Gradient Piecewise Deterministic Monte Carlo Samplers. - Paul Fearnhead; Sebastiano Grazzi; Christopher Nemeth; Gareth O. Roberts (CoRR, 2024)

Scalable Monte Carlo for Bayesian Learning. - Paul Fearnhead; Christopher Nemeth; Chris J. Oates; Chris Sherlock (CoRR, 2024)

Deep Learning Surrogates for Real-Time Gas Emission Inversion. - Thomas Newman; Christopher Nemeth; Matthew Jones; Philip Jonathan (CoRR, 2025)