Publications

2026

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    A Distributed Gaussian Process Model for Multi-Robot Mapping
    Seth Nabarro, Mark van der Wilk, and Andrew J. Davison
    In 2026 International Conference on Robotics and Automation (ICRA), 2026

2025

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    Towards Deep and Distributed Bayesian Learning
    Seth Nabarro
    2025
    PhD Thesis

2024

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    Learning in Deep Factor Graphs with Gaussian Belief Propagation
    Seth Nabarro, Mark van der Wilk, and Andrew J. Davison
    In Forty-first International Conference on Machine Learning, 2024

2022

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    Data augmentation in Bayesian neural networks and the cold posterior effect
    Seth Nabarro*, Stoil Ganev*, Adrià Garriga-Alonso, Vincent Fortuin, Mark van der Wilk, and Laurence Aitchison
    In Uncertainty in Artificial Intelligence, 2022
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    Hardware-accelerated simulation-based inference of stochastic epidemiology models for COVID-19
    Sourabh Kulkarni, Mario Michael Krell, Seth Nabarro, and Csaba Andras Moritz
    ACM Journal on Emerging Technologies in Computing Systems (JETC), 2022

2020

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    Parallel training of deep networks with local updates
    Michael Laskin, Luke Metz, Seth Nabarro, Mark Saroufim, Badreddine Noune, Carlo Luschi, Jascha Sohl-Dickstein, and Pieter Abbeel
    arXiv preprint arXiv:2012.03837, 2020

2018

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    Spatiotemporal Prediction of Ambulance Demand using Gaussian Process Regression
    Seth Nabarro, Tristan Fletcher, and John Shawe-Taylor
    arXiv preprint arXiv:1806.10873, 2018

2013

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    The role of vegetation in the CO 2 flux from a tropical urban neighbourhood
    Erik Velasco, Matthias Roth, Sok Huang Tan, Michelle Quak, Seth Nabarro, and Leslie Norford
    Atmospheric Chemistry and Physics, 2013