Freddie Bickford Smith

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Deep Learning, Uncertainty Estimation

I’m a DPhil student working with Tom Rainforth and Adam Foster. My research focuses on principled data acquisition for machine learning. I have also worked on an approximate-inference scheme to allow neural networks to learn more effectively across sequences of tasks. Previously, during my MSc at UCL, I collaborated with Brad Love, Brett Roads and Ed Grefenstette on a project aimed at understanding neural networks from a cognitive-science perspective. As an undergraduate I studied mechanical engineering at Bristol, working on carbon-fibre composites with Fabrizio Scarpa and on 3D printing with Ben Hicks.

Publications

2023

• F. Bickford Smith , A. Kirsch , S. Farquhar , Y. Gal , A. Foster , T. Rainforth , Prediction-oriented Bayesian active learning, International Conference on Artificial Intelligence and Statistics, 2023.
  

  @article{bickfordsmith2023prediction,
    author = {Bickford Smith, Freddie and Kirsch, Andreas and Farquhar, Sebastian and Gal, Yarin and Foster, Adam and Rainforth, Tom},
    year = {2023},
    title = {Prediction-oriented {Bayesian} active learning},
    journal = {International Conference on Artificial Intelligence and Statistics}
  }
  

• T. Rainforth , A. Foster , D. R. Ivanova , F. Bickford Smith , Modern Bayesian experimental design, Statistical Science (to appear), 2023.
  

  @article{rainforth2023modern,
    author = {Rainforth, Tom and Foster, Adam and Ivanova, Desi R. and Bickford Smith, Freddie},
    year = {2023},
    title = {Modern {Bayesian} experimental design},
    journal = {Statistical Science (to appear)}
  }
  

2022

• T. G. J. Rudner , F. Bickford Smith , Q. Feng , Y. W. Teh , Y. Gal , Continual learning via sequential function-space variational inference, International Conference on Machine Learning, 2022.
  

  @article{rudner2022continual,
    author = {Rudner, Tim G. J. and Bickford Smith, Freddie and Feng, Qixuan and Teh, Yee Whye and Gal, Yarin},
    year = {2022},
    title = {Continual learning via sequential function-space variational inference},
    journal = {International Conference on Machine Learning}
  }