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Christopher Nemeth
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2020 – today
- 2024
- [c10]Louis Sharrock, Daniel Dodd, Christopher Nemeth:
Tuning-Free Maximum Likelihood Training of Latent Variable Models via Coin Betting. AISTATS 2024: 1810-1818 - [c9]Daniel Dodd, Louis Sharrock, Christopher Nemeth:
Learning-Rate-Free Stochastic Optimization over Riemannian Manifolds. ICML 2024 - [c8]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:
Position: Bayesian Deep Learning is Needed in the Age of Large-Scale AI. ICML 2024 - [i15]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:
Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI. CoRR abs/2402.00809 (2024) - [i14]Alberto Cabezas, Louis Sharrock, Christopher Nemeth:
Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing Flows. CoRR abs/2405.14392 (2024) - [i13]Daniel Dodd, Louis Sharrock, Christopher Nemeth:
Learning-Rate-Free Stochastic Optimization over Riemannian Manifolds. CoRR abs/2406.02296 (2024) - [i12]C. Trojan, Paul Fearnhead, Christopher Nemeth:
Diffusion Generative Modelling for Divide-and-Conquer MCMC. CoRR abs/2406.11664 (2024) - [i11]Paul Fearnhead, Sebastiano Grazzi, Christopher Nemeth, Gareth O. Roberts:
Stochastic Gradient Piecewise Deterministic Monte Carlo Samplers. CoRR abs/2406.19051 (2024) - [i10]Paul Fearnhead, Christopher Nemeth, Chris J. Oates, Chris Sherlock:
Scalable Monte Carlo for Bayesian Learning. CoRR abs/2407.12751 (2024) - 2023
- [j7]Kathryn Turnbull
, Christopher Nemeth, Matthew A. Nunes
Sequential estimation of temporally evolving latent space network models. Comput. Stat. Data Anal. 179: 107627 (2023) - [j6]Jeremie Coullon, Leah F. South, Christopher Nemeth:
Efficient and generalizable tuning strategies for stochastic gradient MCMC. Stat. Comput. 33(3): 66 (2023) - [c7]Alberto Cabezas, Christopher Nemeth:
Transport Elliptical Slice Sampling. AISTATS 2023: 3664-3676 - [c6]Srshti Putcha, Christopher Nemeth, Paul Fearnhead:
Preferential Subsampling for Stochastic Gradient Langevin Dynamics. AISTATS 2023: 8837-8856 - [c5]Louis Sharrock, Christopher Nemeth:
Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates. ICML 2023: 30850-30882 - [c4]Louis Sharrock, Lester Mackey, Christopher Nemeth:
Learning Rate Free Bayesian Inference in Constrained Domains. NeurIPS 2023 - [i9]Louis Sharrock, Christopher Nemeth:
Coin Sampling: Gradient-Based Bayesian Inference without Learning Rates. CoRR abs/2301.11294 (2023) - [i8]Louis Sharrock, Daniel Dodd, Christopher Nemeth:
CoinEM: Tuning-Free Particle-Based Variational Inference for Latent Variable Models. CoRR abs/2305.14916 (2023) - [i7]Louis Sharrock, Lester Mackey, Christopher Nemeth:
Learning Rate Free Bayesian Inference in Constrained Domains. CoRR abs/2305.14943 (2023) - 2022
- [j5]Jeremie Coullon
SGMCMCJax: a lightweight JAX library for stochastic gradient Markov chain Monte Carlo algorithms. J. Open Source Softw. 7(72): 4113 (2022) - [j4]Jamie Fairbrother, Christopher Nemeth, Maxime Rischard, Johanni Brea, Thomas Pinder:
GaussianProcesses.jl: A Nonparametric Bayes Package for the Julia Language. J. Stat. Softw. 102(1) (2022) - [i6]Srshti Putcha, Christopher Nemeth, Paul Fearnhead:
Preferential Subsampling for Stochastic Gradient Langevin Dynamics. CoRR abs/2210.16189 (2022) - 2021
- [i5]Thomas Pinder, Kathryn Turnbull
Gaussian Processes on Hypergraphs. CoRR abs/2106.01982 (2021) - 2020
- [i4]Thomas Pinder, Christopher Nemeth
Stein Variational Gaussian Processes. CoRR abs/2009.12141 (2020)
2010 – 2019
- 2019
- [j3]Jack Baker
Control variates for stochastic gradient MCMC. Stat. Comput. 29(3): 599-615 (2019) - [c3]Christopher Nemeth, Fredrik Lindsten, Maurizio Filippone, James Hensman:
Pseudo-Extended Markov chain Monte Carlo. NeurIPS 2019: 4314-4324 - [i3]Christopher Aicher, Srshti Putcha, Christopher Nemeth
Stochastic Gradient MCMC for Nonlinear State Space Models. CoRR abs/1901.10568 (2019) - 2018
- [c2]Jack Baker, Paul Fearnhead, Emily B. Fox, Christopher Nemeth:
Large-Scale Stochastic Sampling from the Probability Simplex. NeurIPS 2018: 6722-6732 - [i2]Jack Baker, Paul Fearnhead
Large-Scale Stochastic Sampling from the Probability Simplex. CoRR abs/1806.07137 (2018) - 2017
- [i1]Jack Baker, Paul Fearnhead
Control Variates for Stochastic Gradient MCMC. CoRR abs/1706.05439 (2017) - 2014
- [j2]Christopher Nemeth
Sequential Monte Carlo Methods for State and Parameter Estimation in Abruptly Changing Environments. IEEE Trans. Signal Process. 62(5): 1245-1255 (2014) - 2013
- [j1]Christopher Nemeth:
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. Cogn. Technol. Work. 15(1): 117-118 (2013) - 2012
- [c1]Christopher Nemeth, Paul Fearnhead, Lyudmila Mihaylova, Dave Vorley:
Bearings-only tracking with particle filtering for joint parameter learning and state estimation. FUSION 2012: 824-831
Coauthor Index
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