Christopher Nemeth

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All published works

Action	Title	Year	Authors
+ PDF Chat	Probabilistic Inversion Modeling of Gas Emissions: A Gradient-Based MCMC Estimation of Gaussian Plume Parameters	2024	Thomas B. Newman Christopher Nemeth Matthew R. Jones Philip Jonathan
+ PDF Chat	Metropolis--Hastings with Scalable Subsampling	2024	Estevão Prado Christopher Nemeth Chris Sherlock
+ PDF Chat	Scalable Monte Carlo for Bayesian Learning	2024	Paul Fearnhead Christopher Nemeth Chris J. Oates Chris Sherlock
+ PDF Chat	Diffusion Generative Modelling for Divide-and-Conquer MCMC	2024	C. Trojan Paul Fearnhead Christopher Nemeth
+ PDF Chat	Robust Bayesian nonparametric variable selection for linear regression	2024	Alberto Cabezas Marco Battiston Christopher Nemeth
+ PDF Chat	Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing Flows	2024	Alberto Cabezas Louis Sharrock Christopher Nemeth
+ PDF Chat	Spatial Latent Gaussian Modelling with Change of Support	2024	Erick Chacón-Montalván Peter M. Atkinson Christopher Nemeth Benjamín M. Taylor Paula Moraga
+ PDF Chat	Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI	2024	Theodore Papamarkou Maria Skoularidou Konstantina Palla Laurence Aitchison Julyan Arbel David B. Dunson Maurizio Filippone Vincent Fortuin Philipp Hennig Aliaksandr Hubin
+ PDF Chat	Multivariate sensitivity analysis for a large-scale climate impact and adaptation model	2023	Oluwole Oyebamiji Christopher Nemeth Paula A. Harrison R. W. Dunford George Cojocaru
+ PDF Chat	Efficient and generalizable tuning strategies for stochastic gradient MCMC	2023	Jeremie Coullon Leah F. South Christopher Nemeth
+ PDF Chat	Stochastic Gradient MCMC for Nonlinear State Space Models	2023	Christopher Aicher Srshti Putcha Christopher Nemeth Paul Fearnhead Emily B. Fox
+	Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates	2023	Louis Sharrock Christopher Nemeth
+	CoinEM: Tuning-Free Particle-Based Variational Inference for Latent Variable Models	2023	Louis Sharrock Daniel Dodd Christopher Nemeth
+	Learning Rate Free Sampling in Constrained Domains	2023	Louis Sharrock Lester Mackey Christopher Nemeth
+ PDF Chat	SwISS: A scalable Markov chain Monte Carlo divide‐and‐conquer strategy	2022	Callum Vyner Christopher Nemeth Chris Sherlock
+	Sequential estimation of temporally evolving latent space network models	2022	Kathryn Turnbull Christopher Nemeth Matthew A. Nunes Tyler H. McCormick
+ PDF Chat	Efficient and Generalizable Tuning Strategies for Stochastic Gradient MCMC	2022	Jeremie Coullon Leah F. South Christopher Nemeth
+ PDF Chat	SGMCMCJax: a lightweight JAX library for stochastic gradient Markov chain Monte Carlo algorithms	2022	Jeremie Coullon Christopher Nemeth
+ PDF Chat	<b>GaussianProcesses.jl</b>: A Nonparametric Bayes Package for the <i>Julia</i> Language	2022	Jamie Fairbrother Christopher Nemeth Maxime Rischard Johanni Brea Thomas Pinder
+	Distances for Comparing Multisets and Sequences	2022	George Bolt Simón Lunagómez Christopher Nemeth
+	Modelling Populations of Interaction Networks via Distance Metrics	2022	George Bolt Simón Lunagómez Christopher Nemeth
+	SwISS: A Scalable Markov chain Monte Carlo Divide-and-Conquer Strategy	2022	Callum Vyner Christopher Nemeth Chris Sherlock
+	Transport Elliptical Slice Sampling	2022	Alberto Cabezas Christopher Nemeth
+	Preferential Subsampling for Stochastic Gradient Langevin Dynamics	2022	Srshti Putcha Christopher Nemeth Paul Fearnhead
+ PDF Chat	Semi-exact control functionals from Sard’s method	2021	Leah F. South Toni Karvonen Christopher Nemeth Mark Girolami Chris J. Oates
+	Gaussian Processes on Hypergraphs.	2021	Thomas Pinder Kathryn Turnbull Christopher Nemeth David S. Leslie
+	Stochastic Gradient MCMC with Multi-Armed Bandit Tuning	2021	Jeremie Coullon Leah F. South Christopher Nemeth
+	Stochastic Gradient Markov Chain Monte Carlo	2021	Christopher Nemeth Paul Fearnhead
+	A Probabilistic Assessment of the COVID-19 Lockdown on Air Quality in the UK	2021	Thomas Pinder Michael Hollaway Christopher Nemeth P. J. Young David S. Leslie
+	Sequential Estimation of Temporally Evolving Latent Space Network Models	2021	Kathryn Turnbull Christopher Nemeth Matthew A. Nunes Tyler H. McCormick
+	Gaussian Processes on Hypergraphs	2021	Thomas Pinder Kathryn Turnbull Christopher Nemeth David M. Leslie
+	Efficient and Generalizable Tuning Strategies for Stochastic Gradient MCMC	2021	Jeremie Coullon Leah F. South Christopher Nemeth
+	Robust Bayesian Nonparametric Variable Selection for Linear Regression	2021	Alberto Cabezas Marco Battiston Christopher Nemeth
+	Stochastic Gradient Markov Chain Monte Carlo	2021	Christopher Nemeth Paul Fearnhead
+ PDF Chat	Stochastic Gradient Markov Chain Monte Carlo	2020	Christopher Nemeth Paul Fearnhead
+	Stein Variational Gaussian Processes	2020	Thomas Pinder Christopher Nemeth David S. Leslie
+	Stein Variational Gaussian Processes	2020	Thomas Pinder Christopher Nemeth David M. Leslie
+	Stochastic gradient Markov chain Monte Carlo	2020	Christopher Nemeth Paul Fearnhead
+	Pseudo-Extended Markov Chain Monte Carlo	2019	Christopher Nemeth Fredrik Lindsten Maurizio Filippone James Hensman
+	Stochastic Gradient Markov Chain Monte Carlo [R package sgmcmc version 0.2.5]	2019	Jack Baker Christopher Nemeth Paul Fearnhead Emily B. Fox
+	Latent Space Representations of Hypergraphs	2019	Kathryn Turnbull Simón Lunagómez Christopher Nemeth Edoardo M. Airoldi
+ PDF Chat	<b>sgmcmc</b>: An <i>R</i> Package for Stochastic Gradient Markov Chain Monte Carlo	2019	Jack Baker Paul Fearnhead Emily B. Fox Christopher Nemeth
+	Latent Space Modelling of Hypergraph Data	2019	Kathryn Turnbull Simón Lunagómez Christopher Nemeth Edoardo M. Airoldi
+	Stochastic gradient Markov chain Monte Carlo	2019	Christopher Nemeth Paul Fearnhead
+	Stochastic Gradient MCMC for Nonlinear State Space Models	2019	Christopher Aicher Srshti Putcha Christopher Nemeth Paul Fearnhead Emily B. Fox
+	Large-Scale Stochastic Sampling from the Probability Simplex	2018	Jack Baker Paul Fearnhead Emily B. Fox Christopher Nemeth
+ PDF Chat	Control variates for stochastic gradient MCMC	2018	Jack Baker Paul Fearnhead Emily B. Fox Christopher Nemeth
+	Large-Scale Stochastic Sampling from the Probability Simplex	2018	Jack Baker Paul Fearnhead Emily B. Fox Christopher Nemeth
+	GaussianProcesses.jl: A Nonparametric Bayes package for the Julia Language	2018	Jamie Fairbrother Christopher Nemeth Maxime Rischard Johanni Brea Thomas Pinder
+	sgmcmc: An R Package for Stochastic Gradient Markov Chain Monte Carlo	2017	Jack Baker Paul Fearnhead Emily B. Fox Christopher Nemeth
+	Pseudo-extended Markov chain Monte Carlo	2017	Christopher Nemeth Fredrik Lindsten Maurizio Filippone James Hensman
+ PDF Chat	Merging MCMC Subposteriors through Gaussian-Process Approximations	2017	Christopher Nemeth Chris Sherlock
+	Control Variates for Stochastic Gradient MCMC	2017	Jack Baker Paul Fearnhead Emily B. Fox Christopher Nemeth
+	Control Variates for Stochastic Gradient MCMC	2017	Jack W. Baker Paul Fearnhead Emily B. Fox Christopher Nemeth
+	Pseudo-extended Markov chain Monte Carlo	2017	Christopher Nemeth Fredrik Lindsten Maurizio Filippone James Hensman
+	sgmcmc: An R Package for Stochastic Gradient Markov Chain Monte Carlo	2017	Jack W. Baker Paul Fearnhead Emily B. Fox Christopher Nemeth
+ PDF Chat	Particle Metropolis-adjusted Langevin algorithms	2016	Christopher Nemeth Chris Sherlock Paul Fearnhead
+	Merging MCMC Subposteriors through Gaussian-Process Approximations	2016	Christopher Nemeth Chris Sherlock
+	Merging MCMC Subposteriors through Gaussian-Process Approximations	2016	Christopher Nemeth Chris Sherlock
+ PDF Chat	Particle Approximations of the Score and Observed Information Matrix for Parameter Estimation in State–Space Models With Linear Computational Cost	2015	Christopher Nemeth Paul Fearnhead Lyudmila Mihaylova
+	Particle Metropolis-adjusted Langevin algorithms	2014	Christopher Nemeth Chris Sherlock Paul Fearnhead
+ PDF Chat	Sequential Monte Carlo Methods for State and Parameter Estimation in Abruptly Changing Environments	2014	Christopher Nemeth Paul Fearnhead Lyudmila Mihaylova
+	Particle Metropolis adjusted Langevin algorithms for state space models	2014	Christopher Nemeth Paul Fearnhead
+	Particle Metropolis-adjusted Langevin algorithms	2014	Christopher Nemeth Chris Sherlock Paul Fearnhead
+	Particle approximations of the score and observed information matrix for parameter estimation in state space models with linear computational cost	2013	Christopher Nemeth Paul Fearnhead Lyudmila Mihaylova

Common Coauthors

Coauthor	Papers Together
Paul Fearnhead	26
Emily B. Fox	11
Chris Sherlock	10
Jack Baker	7
Kathryn Turnbull	6
Jeremie Coullon	5
Leah F. South	5
Alberto Cabezas	4
Maurizio Filippone	4
Simón Lunagómez	4
Louis Sharrock	4
Lyudmila Mihaylova	3
Srshti Putcha	3
Thomas Pinder	3
Fredrik Lindsten	3
David S. Leslie	3
James Hensman	3
George Bolt	2
Thomas Pinder	2
Christopher Aicher	2
Johanni Brea	2
Tyler H. McCormick	2
Callum Vyner	2
Edoardo M. Airoldi	2
Maxime Rischard	2
Matthew A. Nunes	2
Jack W. Baker	2
Marco Battiston	2
Thomas Pinder	2
Chris J. Oates	2
Jamie Fairbrother	2
David M. Leslie	2
P. J. Young	1
Lester Mackey	1
Mark Girolami	1
Paula Moraga	1
Ruqi Zhang	1
C. Trojan	1
Philip Jonathan	1
Mohammad Emtiyaz Khan	1
Julyan Arbel	1
Erick Chacón-Montalván	1
Andrew Gordon Wilson	1
Michael A. Osborne	1
Laurence Aitchison	1
David B. Dunson	1
Yee Whye Teh	1
Theofanis Karaletsos	1
Paula A. Harrison	1
R. W. Dunford	1

Commonly Cited References

Action	Title	Year	Authors	# of times referenced
+ PDF Chat	Optimal Scaling of Discrete Approximations to Langevin Diffusions	1998	Gareth O. Roberts Jeffrey S. Rosenthal	12
+ PDF Chat	Exponential Convergence of Langevin Distributions and Their Discrete Approximations	1996	Gareth O. Roberts Richard L. Tweedie	8
+	Bayesian Sampling Using Stochastic Gradient Thermostats	2014	Nan Ding Youhan Fang Ryan Babbush Changyou Chen Robert D. Skeel Hartmut Neven	8
+	Bayesian Learning via Stochastic Gradient Langevin Dynamics	2011	Max Welling Yee Whye Teh	8
+ PDF Chat	<i>Stan</i>: A Probabilistic Programming Language	2017	Bob Carpenter Andrew Gelman Matthew D. Hoffman Daniel C. Lee Ben Goodrich Michael Betancourt Marcus A. Brubaker Jiqiang Guo Peter Li Allen Riddell	7
+	Spatial Point Processes	2011	Mark Huber	7
+ PDF Chat	Particle Markov Chain Monte Carlo Methods	2010	Christophe Andrieu Arnaud Doucet Roman Holenstein	7
+	Stochastic Gradient Riemannian Langevin Dynamics on the Probability Simplex	2013	Sam Patterson Yee Whye Teh	7
+ PDF Chat	Weak convergence and optimal scaling of random walk Metropolis algorithms	1997	Susan A. Gelman Walter R. Gilks Gareth O. Roberts	6
+ PDF Chat	Riemann Manifold Langevin and Hamiltonian Monte Carlo Methods	2011	Mark Girolami Ben Calderhead	6
+ PDF Chat	<b>sgmcmc</b>: An <i>R</i> Package for Stochastic Gradient Markov Chain Monte Carlo	2019	Jack Baker Paul Fearnhead Emily B. Fox Christopher Nemeth	6
+	None	2000	Arnaud Doucet Simon Godsill Christophe Andrieu	6
+ PDF Chat	Control variates for stochastic gradient MCMC	2018	Jack Baker Paul Fearnhead Emily B. Fox Christopher Nemeth	6
+	Variance Reduction in Stochastic Gradient Langevin Dynamics.	2016	Kumar Avinava Dubey Sashank J. Reddi Sinead A. Williamson Barnabás Póczos Alexander J. Smola Eric P. Xing	6
+	Stochastic Gradient Hamiltonian Monte Carlo	2014	Tianqi Chen Emily B. Fox Carlos Guestrin	5
+	Optimal scaling for various Metropolis-Hastings algorithms	2001	Gareth O. Roberts Jeffrey S. Rosenthal	5
+	Novel approach to nonlinear/non-Gaussian Bayesian state estimation	1993	Neil Gordon David Salmond A. F. M. Smith	5
+	Maximum Likelihood from Incomplete Data Via the <i>EM</i> Algorithm	1977	A. P. Dempster N. M. Laird Donald B. Rubin	5
+ PDF Chat	Handbook of Markov Chain Monte Carlo	2011	Steve Brooks Andrew Gelman Galin L. Jones Xiao‐Li Meng	5
+	The True Cost of Stochastic Gradient Langevin Dynamics	2017	Tigran Nagapetyan A. Duncan Leonard Hasenclever Sebastian J. Vollmer Łukasz Szpruch Konstantinos C. Zygalakis	5
+	A tutorial on adaptive MCMC	2008	Christophe Andrieu Johannes Thoms	5
+	Consistency and fluctuations for stochastic gradient Langevin dynamics	2016	Yee Whye Teh Alexandre H. Thiéry Sebastian J. Vollmer	5
+	Exploration of the (non-)asymptotic bias and variance of stochastic gradient langevin dynamics	2016	Sebastian J. Vollmer Konstantinos C. Zygalakis Yee Whye Teh	5
+	Scalable MCMC for Mixed Membership Stochastic Blockmodels	2016	Wenzhe Li Sungjin Ahn Max Welling	4
+	Measuring Sample Quality with Kernels	2017	Jackson Gorham Lester Mackey	4
+	Bayesian Posterior Sampling via Stochastic Gradient Fisher Scoring	2012	Sungjin Ahn Anoop Korattikara Max Welling	4
+	Approximating Posterior Distributions by Mixtures	1993	Mike West	4
+ PDF Chat	Bayes and big data: the consensus Monte Carlo algorithm	2016	Steven L. Scott Alexander W. Blocker Fernando V. Bonassi Hugh Chipman Edward I. George Robert E. McCulloch	4
+	JAGS: A program for analysis of Bayesian graphical models using Gibbs sampling	2003	Martyn Plummer	4
+	The No-U-turn sampler: adaptively setting path lengths in Hamiltonian Monte Carlo	2014	Matthew D. Homan Andrew Gelman	4
+	None	2000	David J. Lunn Andrew C. Thomas Nicky Best David J. Spiegelhalter	4
+ PDF Chat	Particle Approximations of the Score and Observed Information Matrix for Parameter Estimation in State–Space Models With Linear Computational Cost	2015	Christopher Nemeth Paul Fearnhead Lyudmila Mihaylova	4
+	A complete recipe for stochastic gradient MCMC	2015	Yi-An Ma Tianqi Chen Emily B. Fox	4
+	Edward: A library for probabilistic modeling, inference, and criticism	2016	Dustin Tran Alp Kucukelbir Adji B. Dieng Maja Rudolph Dawen Liang David M. Blei	4
+ PDF Chat	Stochastic Gradient Markov Chain Monte Carlo	2020	Christopher Nemeth Paul Fearnhead	4
+ PDF Chat	Particle Filters for Partially Observed Diffusions	2008	Paul Fearnhead Omiros Papaspiliopoulos Gareth O. Roberts	3
+ PDF Chat	General Methods for Monitoring Convergence of Iterative Simulations	1998	Stephen P. Brooks Andrew Gelman	3
+	On the Theory of Variance Reduction for Stochastic Gradient Monte Carlo	2018	Niladri S. Chatterji Nicolas Flammarion Yi-An Ma Peter L. Bartlett Michael I. Jordan	3
+	The pseudo-marginal approach for efficient Monte Carlo computations	2009	Christophe Andrieu Gareth O. Roberts	3
+ PDF Chat	Sequential Monte Carlo smoothing with application to parameter estimation in nonlinear state space models	2008	Jimmy Olsson Olivier Cappé Randal Douc Éric Moulines	3
+	The promises and pitfalls of Stochastic Gradient Langevin Dynamics	2018	Nicolas Brosse Alain Durmus Éric Moulines	3
+ PDF Chat	Regret Analysis of Stochastic and Nonstochastic Multi-armed Bandit Problems	2012	Sébastien Bubeck Nicolò Cesa‐Bianchi	3
+ PDF Chat	Large-Scale Distributed Bayesian Matrix Factorization using Stochastic Gradient MCMC	2015	Sungjin Ahn Anoop Korattikara Nathan Liu Suju Rajan Max Welling	3
+ PDF Chat	Exploiting Multi-Core Architectures for Reduced-Variance Estimation with Intractable Likelihoods	2015	Nial Friel Antonietta Mira Chris J. Oates	3
+ PDF Chat	Particle metropolis hastings using Langevin dynamics	2013	Johan Dahlin Fredrik Lindsten Thomas B. Schön	3
+	Control Functionals for Monte Carlo Integration	2016	Chris J. Oates Mark Girolami Nicolás Chopin	3
+	A kernelized stein discrepancy for goodness-of-fit tests	2016	Qiang Liu Jason D. Lee Michael I. Jordan	3
+	Smooth particle filters for likelihood evaluation and maximisation	2002	M. Pitt	3
+	On Markov chain Monte Carlo methods for tall data	2015	Rémi Bardenet Arnaud Doucet Chris Holmes	3
+	Combined Parameter and State Estimation in Simulation-Based Filtering	2001	Jane Liu West Mike	3