Dr Tom Rainforth

Senior Researcher in Statistical Machine Learning

Biographical sketch

I am currently a Senior Researcher in Statistical Machine Learning and Leader of the RainML (rainml.uk) Research Lab.  Prior to this, I was a Junior Research Fellow in Computer Science at Christ Church College, and before that I was a postdoc working with Yee Whye Teh.  I originally studied Mechanical Engineering (MEng) at the University of Cambridge, while I did my DPhil in Oxford under the supervision of Frank Wood and Michael Osborne, focusing mostly on probabilistic programming and Monte Carlo methods.  I also had a stint working in the Ferrari Formula 1 team in between the two periods of study.

Personal website: https://www.robots.ox.ac.uk/~twgr/

Research Interests

My research covers a wide range of topics in and around statistical machine learning and experimental design, with areas of particular interest including: 

  • Bayesian experimental design
  • Probabilistic and data-efficient approaches to machine learning
  • Active learning
  • Deep learning, with a particular focus on probabilistic approaches, deep representation learning, and deep generative models
  • Probabilistic programming
  • Approximate inference and Monte Carlo methods

Please see my Google Scholar page for an up-to-date list of publications.

Publications

Bickford Smith, F., Foster, A. and Rainforth, T. (2024) “Making better use of unlabelled data in Bayesian Active learning”, in.
Reichelt, T., Ong, L. and Rainforth, T. (2024) “Beyond Bayesian model averaging over paths in probabilistic programs with stochastic support”, in.
Miao, N., TEH, Y. and RAINFORTH, T. (2024) “SelfCheck: Using LLMs to zero-shot check their own step-by-step reasoning”, in.
Kossen, J., GAL, Y. and RAINFORTH, T. (2024) “In-context learning learns label relationships but is not conventional learning”, in.
Dhillon, G., Deligiannidis, G. and Rainforth, T. (2023) “On the expected size of conformal prediction sets”, in. Journal of Machine Learning Research.
Campbell, A., Benton, J., De Bortoli, V., Rainforth, T., Deligiannidis, G. and Doucet, A. (2023) “A continuous time framework for discrete denoising models”, in Advances in Neural Information Processing Systems 35 (NeurIPS 2022). Curran Associates, pp. 28266–28279.
Miao, N., Rainforth, T., Mathieu, E., Dubois, Y., Teh, Y., Foster, A. and Kim, H. (2023) “Learning Instance-Specific Augmentations by Capturing Local Invariances”, in Proceedings of Machine Learning Research, pp. 24720–24736.
Smith, F., Kirsch, A., Farquhar, S., Gal, Y., Foster, A. and Rainforth, T. (2023) “Prediction-Oriented Bayesian Active Learning”, in Proceedings of Machine Learning Research, pp. 7331–7348.

Contact Details

Email: rainforth@stats.ox.ac.uk

Office: 1.06

Pronouns: He/Him

Graduate Students

Alex Forster
Angus Phillips
Freddie Bickford Smith

Guneet Singh Dhillon
Andrew Campbell
Desi Ivanova
Jannik Kossen
Ning Miao
Tim Reichelt
Mrinank Sharma
Yuyang Shi
Shahine Bouabid
Jin Xu