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

Foster, A., Jankowiak, M., Bingham, E., Horsfall, P., Teh, Y., Rainforth, T. and Goodman, N. (2019) “Variational Bayesian optimal experimental design”, in Advances in Neural Information Processing Systems 32 (NIPS 2019). Conference on Neural Information Processing Systems.
Locatello, F., Abbati, G., Rainforth, T., Bauer, S., Schölkopf, B. and Bachem, O. (2019) “On the fairness of disentangled representations”, in Advances in Neural Information Processing Systems. NeurIPS.
Foster, A., Jankowiak, M., Bingham, E., Horsfall, P., TEH, Y., RAINFORTH, T. and Goodman, N. (2019) “Variational Bayesian Optimal Experimental Design”, in.
Goliński, A., Wood, F. and Rainforth, T. (2019) “Amortized Monte Carlo integration”, in Proceedings of Machine Learning Research. Proceedings of Machine Learning Research, pp. 2309–2318.
Zhou, Y., Gram-Hansen, B., Kohn, T., Rainforth, T., Yang, H. and Wood, F. (2019) “LF-PPL: A low-level first order probabilistic programming language for non-differentiable models”, in Proceedings of Machine Learning Research. ML Research Press, pp. 148–157.
Webb, S., Rainforth, T., Teh, Y. and Mudigonda, P. (2019) “A statistical approach to assessing neural network robustness”, in Seventh International Conference on Learning Representations (ICLR 2019). International Conferences on Learning Representations.
Rainforth, T., Cornish, R., Yang, H., Warrington, A. and Wood, F. (2019) “On nesting Monte Carlo estimators”, in 35th International Conference on Machine Learning, ICML 2018, Stockholmsmässan, Stockholm Sweden, 10th - 15th July 2018. Proceedings of Machine Learning Research, pp. 4267–4276.
Mathieu, E., Rainforth, T., Siddharth, N. and Teh, Y. (2019) “Disentangling disentanglement in variational autoencoders”, in 36th International Conference on Machine Learning, ICML 2019, pp. 7744–7754.
Goliński, A., Wood, F. and Rainforth, T. (2019) “Amortized Monte Carlo integration”, in 36th International Conference on Machine Learning, ICML 2019, pp. 4163–4172.
Webb, S., Pawan Kumar, M., Rainforth, T. and Teh, Y. (2019) “A statistical approach to assessing neural network robustness”, in 7th International Conference on Learning Representations, ICLR 2019.

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