Christopher Nemeth

Orcid: 0000-0002-9084-3866

According to our database1, Christopher Nemeth authored at least 26 papers between 2012 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

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Links

Online presence:

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Bibliography

2024
Scalable Monte Carlo for Bayesian Learning.
CoRR, 2024

Stochastic Gradient Piecewise Deterministic Monte Carlo Samplers.
CoRR, 2024

Diffusion Generative Modelling for Divide-and-Conquer MCMC.
CoRR, 2024

Position Paper: Bayesian Deep Learning in the Age of Large-Scale AI.
CoRR, 2024

Markovian Flow Matching: Accelerating MCMC with Continuous Normalizing Flows.
Proceedings of the Advances in Neural Information Processing Systems 38: Annual Conference on Neural Information Processing Systems 2024, 2024


Learning-Rate-Free Stochastic Optimization over Riemannian Manifolds.
Proceedings of the Forty-first International Conference on Machine Learning, 2024

Tuning-Free Maximum Likelihood Training of Latent Variable Models via Coin Betting.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2024

2023
Efficient and generalizable tuning strategies for stochastic gradient MCMC.
Stat. Comput., June, 2023

Sequential estimation of temporally evolving latent space network models.
Comput. Stat. Data Anal., 2023

CoinEM: Tuning-Free Particle-Based Variational Inference for Latent Variable Models.
CoRR, 2023

Learning Rate Free Bayesian Inference in Constrained Domains.
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.
Proceedings of the International Conference on Machine Learning, 2023

Preferential Subsampling for Stochastic Gradient Langevin Dynamics.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

Transport Elliptical Slice Sampling.
Proceedings of the International Conference on Artificial Intelligence and Statistics, 2023

2022
GaussianProcesses.jl: A Nonparametric Bayes Package for the <i>Julia</i> Language.
J. Stat. Softw., 2022

SGMCMCJax: a lightweight JAX library for stochastic gradient Markov chain Monte Carlo algorithms.
J. Open Source Softw., 2022

2021
Gaussian Processes on Hypergraphs.
CoRR, 2021

2020
Stein Variational Gaussian Processes.
CoRR, 2020

2019
Control variates for stochastic gradient MCMC.
Stat. Comput., 2019

Stochastic Gradient MCMC for Nonlinear State Space Models.
CoRR, 2019

Pseudo-Extended Markov chain Monte Carlo.
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.
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.
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.
Cogn. Technol. Work., 2013

2012
Bearings-only tracking with particle filtering for joint parameter learning and state estimation.
Proceedings of the 15th International Conference on Information Fusion, 2012


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