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

Wang, B., Webb, S. and Rainforth, T. (2021) “Statistically Robust Neural Network Classification”, in Proceedings of Machine Learning Research, pp. 1735–1745.
Rudner, T., Key, O., Gal, Y. and Rainforth, T. (2021) “On Signal-to-Noise Ratio Issues in Variational Inference for Deep Gaussian Processes”, in Proceedings of Machine Learning Research, pp. 9148–9156.
Wang, B., Webb, S. and Rainforth, T. (2021) “Statistically Robust Neural Network Classification”, in 37th Conference on Uncertainty in Artificial Intelligence, UAI 2021, pp. 1735–1745.
Xu, J., Kim, H., Rainforth, T. and Teh, Y. (2021) “Group Equivariant Subsampling”, in Advances in Neural Information Processing Systems, pp. 5934–5946.
Willetts, M., Camuto, A., Rainforth, T., Roberts, S. and Holmes, C. (2021) “IMPROVING VAES’ ROBUSTNESS TO ADVERSARIAL ATTACK”, in ICLR 2021 - 9th International Conference on Learning Representations.
Fitzsimons, J., Mantri, A., Pisarczyk, R., Rainforth, T. and Zhao, Z. (2020) “A note on blind contact tracing at scale with applications to the COVID-19 pandemic”, in. Association for Computing Machinery (ACM), pp. 1–6.
Zhou, Y., Yang, H., Teh, Y. and Rainforth, T. (2020) “Divide, conquer, and combine: a new inference strategy for probabilistic programs with stochastic support”, in ICML 2020. ICML Proceedings.
Foster, A., Jankowiak, M., O’Meara, M., Teh, Y. and Rainforth, T. (2020) “A unified stochastic gradient approach to designing Bayesian-optimal experiments”, in Proceedings of the Twenty Third International Conference on Artificial Intelligence and Statistics. PMLR, pp. 2959–2969.
Rainforth, T., Golinski, A., Wood, F. and Zaidi, S. (2020) “Target–aware Bayesian inference: how to beat optimal conventional estimators”, Journal of Machine Learning Research, 21(88), p. 1−54.
Zhou, Y., Yang, H., The, Y. and Rainforth, T. (2020) “Divide, conquer, and combine: A new inference strategy for Probabilistic Programs with Stochastic Support”, 37th International Conference on Machine Learning, ICML 2020, PartF168147-15, pp. 11471–11482.

Contact Details

Email: rainforth@stats.ox.ac.uk

Office: 1.06

Pronouns: He/Him

Research Groups

Computational Statistics and Machine Learning

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