Tom Rainforth

Tom Rainforth

Bayesian inference, probabilistic programming, Monte Carlo methods

I am postdoc working in Yee Whye Teh’s group at the department of statistics in Oxford. I have a broad range of interest varying from Monte Carlo methods and probabilistic programming to random forests and variational auto-encoders.

Publications

2018

  • S. Webb , A. Golinski , R. Zinkov , N. Siddharth , T. Rainforth , Y. W. Teh , F. Wood , Faithful Inversion of Generative Models for Effective Amortized Inference, in Advances in Neural Information Processing Systems (NIPS), 2018.
  • A. Golinski , Y. W. Teh , F. Wood , T. Rainforth , Amortized Monte Carlo Integration, UAI 2018 Workshop on Uncertainty in Deep Learning, 2018.
  • T. Rainforth , Nesting Probabilistic Programs, arXiv preprint arXiv:1803.06328, 2018.
  • T. Rainforth , A. R. Kosiorek , T. A. Le , C. J. Maddison , M. Igl , F. Wood , Y. W. Teh , Tighter Variational Bounds are Not Necessarily Better, arXiv preprint arXiv:1802.04537, 2018.
  • T. A. Le , M. Igl , T. Rainforth , T. Jin , F. Wood , Auto-Encoding Sequential Monte Carlo, in International Conference on Learning Representations, 2018.

2017

  • T. Rainforth , Automating Inference, Learning, and Design using Probabilistic Programming, PhD thesis, University of Oxford, 2017.
  • B. T. Vincent , T. Rainforth , The DARC Toolbox: automated, flexible, and efficient delayed and risky choice experiments using Bayesian adaptive design, 2017.
  • T. Rainforth , R. Cornish , H. Yang , A. Warrington , F. Wood , On the Opportunities and Pitfalls of Nesting Monte Carlo Estimators, arXiv preprint arXiv:1709.06181, 2017.
  • T. Rainforth , Y. Zhou , X. Lu , Y. W. Teh , F. Wood , H. Yang , J. Meent , Inference Trees: Adaptive Inference with Exploration [Workshop Version], NIPS Workshop on Advances in Approximate Bayesian Inference, 2017.
  • B. Bloem-Reddy , E. Mathieu , A. Foster , T. Rainforth , H. Ge , M. Lomelí , Z. Ghahramani , Y. W. Teh , Sampling and inference for discrete random probability measures in probabilistic programs, NIPS Workshop on Advances in Approximate Bayesian Inference, 2017.
  • X. Lu , T. Rainforth , Y. Zhou , Y. W. Teh , F. Wood , H. Yang , J. Meent , On Exploration, Exploitation and Learning in Adaptive Importance Sampling, NIPS Workshop on Advances in Approximate Bayesian Inference, 2017.

2016

  • T. Rainforth , T. A. Le , J. Meent , M. A. Osborne , F. Wood , Bayesian Optimization for Probabilistic Programs, in Advances in Neural Information Processing Systems, 2016, 280–288.
  • T. Rainforth , R. Cornish , H. Yang , F. Wood , On the Pitfalls of Nested Monte Carlo, NIPS Workshop on Advances in Approximate Bayesian Inference, 2016.
  • D. Janz , B. Paige , T. Rainforth , J. Meent , F. Wood , Probabilistic Structure Discovery in Time Series Data, NIPS Workshop on Artificial Intelligence for Data Science, 2016.
  • T. Rainforth , C. A. Naesseth , F. Lindsten , B. Paige , J. Meent , A. Doucet , F. Wood , Interacting Particle Markov Chain Monte Carlo, in Proceedings of the 33rd International Conference on Machine Learning, 2016, vol. 48.

2015

  • T. Rainforth , F. Wood , Canonical Correlation Forests, arXiv preprint arXiv:1507.05444, 2015.