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

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.
Sharma, M., Farquhar, S., Nalisnick, E. and Rainforth, T. (2023) “Do Bayesian Neural Networks Need To Be Fully Stochastic?”, in Proceedings of Machine Learning Research, pp. 7694–7722.
Campbell, A., Shi, Y., Rainforth, T. and Doucet, A. (2022) “Online variational filtering and parameter learning”, in Advances in Neural Information Processing Systems 34 (NeurIPS 2021). Curran Associates, pp. 18633–18645.
Naderiparizi, S., Scibior, A., Munk, A., Ghadiri, M., Baydin, A., Gram-Hansen, B., Schroeder, C., Zinkov, R., Torr, P., Rainforth, T., Teh, Y. and Wood, F. (2022) “Amortized rejection sampling in universal probabilistic programming”, in Proceedings of the 25th International Conference on Artificial Intelligence and Statistics (AISTATS 2022). Journal of Machine Learning Research, pp. 8392–8412.
Joy, T., Shi, Y., Torr, P., Rainforth, T., Schmon, S. and Siddharth, N. (2022) “Learning multimodal VAEs through mutual supervision”, in International Conference on Learning Representations. OpenReview.
Miao, N., Mathieu, E., Siddharth, N., Teh, Y. and Rainforth, T. (2022) “ON INCORPORATING INDUCTIVE BIASES INTO VAES”, in ICLR 2022 - 10th International Conference on Learning Representations.
Kossen, J., Farquhar, S., Gal, Y. and Rainforth, T. (2022) “Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation”, in Advances in Neural Information Processing Systems.
Reichelt, T., Ong, L. and Rainforth, T. (2022) “Rethinking Variational Inference for Probabilistic Programs with Stochastic Support”, in Advances in Neural Information Processing Systems.
Reichelt, T., Goliński, A., Ong, L. and Rainforth, T. (2022) “Expectation Programming: Adapting Probabilistic Programming Systems to Estimate Expectations Efficiently”, in Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence, UAI 2022, pp. 1676–1685.
Barrett, B., Camuto, A., Willetts, M. and Rainforth, T. (2022) “Certifiably Robust Variational Autoencoders”, in Proceedings of Machine Learning Research, pp. 3663–3683.

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