Christopher Nemeth

According to our database, Christopher Nemeth authored at least 26 papers between 2012 and 2024.

Collaborative distances:

• Dijkstra number of four.
• Erdős number of four.

2024

Scalable Monte Carlo for Bayesian Learning.

Paul Fearnhead, Christopher Nemeth, Chris J. Oates, Chris Sherlock

CoRR, 2024

Stochastic Gradient Piecewise Deterministic Monte Carlo Samplers.

Paul Fearnhead, Sebastiano Grazzi, Christopher Nemeth, Gareth O. Roberts

CoRR, 2024

Diffusion Generative Modelling for Divide-and-Conquer MCMC.

C. Trojan, Paul Fearnhead, Christopher Nemeth

CoRR, 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, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A. Osborne, Tim G. J. Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang

CoRR, 2024

Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing Flows.

Alberto Cabezas, Louis Sharrock, Christopher Nemeth

Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 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, Theofanis Karaletsos, Mohammad Emtiyaz Khan, Agustinus Kristiadi, Yingzhen Li, Stephan Mandt, Christopher Nemeth, Michael A. Osborne, Tim G. J. Rudner, David Rügamer, Yee Whye Teh, Max Welling, Andrew Gordon Wilson, Ruqi Zhang

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Learning-Rate-Free Stochastic Optimization over Riemannian Manifolds.

Daniel Dodd, Louis Sharrock, Christopher Nemeth

Proceedings of the Forty-first International Conference on Machine Learning, 2024

Tuning-Free Maximum Likelihood Training of Latent Variable Models via Coin Betting.

Louis Sharrock, Daniel Dodd, Christopher Nemeth

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023

Efficient and generalizable tuning strategies for stochastic gradient MCMC.

Jeremie Coullon, Leah F. South, Christopher Nemeth

Stat. Comput., June, 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

CoinEM: Tuning-Free Particle-Based Variational Inference for Latent Variable Models.

Louis Sharrock, Daniel Dodd, Christopher Nemeth

CoRR, 2023

Learning Rate Free Bayesian Inference in Constrained Domains.

Louis Sharrock, Lester Mackey, Christopher Nemeth

Proceedings of the Advances in Neural Information Processing Systems 36: Annual Conference on Neural Information Processing Systems 2023, 2023

Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates.

Louis Sharrock, Christopher Nemeth

Proceedings of the International Conference on Machine Learning, 2023

Preferential Subsampling for Stochastic Gradient Langevin Dynamics.

Srshti Putcha, Christopher Nemeth, Paul Fearnhead

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Transport Elliptical Slice Sampling.

Alberto Cabezas, Christopher Nemeth

Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022

GaussianProcesses.jl: A Nonparametric Bayes Package for the <i>Julia</i> Language.

Jamie Fairbrother, Christopher Nemeth, Maxime Rischard, Johanni Brea, Thomas Pinder

J. Stat. Softw., 2022

SGMCMCJax: a lightweight JAX library for stochastic gradient Markov chain Monte Carlo algorithms.

Jeremie Coullon, Christopher Nemeth

J. Open Source Softw., 2022

2021

Gaussian Processes on Hypergraphs.

Thomas Pinder, Kathryn Turnbull, Christopher Nemeth, David Leslie

CoRR, 2021

2020

Stein Variational Gaussian Processes.

Thomas Pinder, Christopher Nemeth, David Leslie

CoRR, 2020

2019

Control variates for stochastic gradient MCMC.

Jack Baker, Paul Fearnhead, Emily B. Fox, Christopher Nemeth

Stat. Comput., 2019

Stochastic Gradient MCMC for Nonlinear State Space Models.

Christopher Aicher, Srshti Putcha, Christopher Nemeth, Paul Fearnhead, Emily B. Fox

CoRR, 2019

Pseudo-Extended Markov chain Monte Carlo.

Christopher Nemeth, Fredrik Lindsten, Maurizio Filippone, James Hensman

Proceedings of the Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, 2019

2018

Large-Scale Stochastic Sampling from the Probability Simplex.

Jack Baker, Paul Fearnhead, Emily B. Fox, Christopher Nemeth

Proceedings of the Advances in Neural Information Processing Systems 31: Annual Conference on Neural Information Processing Systems 2018, 2018

2014

Sequential Monte Carlo Methods for State and Parameter Estimation in Abruptly Changing Environments.

Christopher Nemeth, Paul Fearnhead, Lyudmila Mihaylova

IEEE Trans. Signal Process., 2014

2013

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

2012

Bearings-only tracking with particle filtering for joint parameter learning and state estimation.

Christopher Nemeth, Paul Fearnhead, Lyudmila Mihaylova, Dave Vorley

Proceedings of the 15th International Conference on Information Fusion, 2012