Tom Rainforth

Tom Rainforth

Machine learning, Bayesian inference, Probabilistic programming, Deep generative models

I am postdoc working in Yee Whye Teh’s group at the department of statistics in Oxford. Please see my personal website here for further details.

Publications

2019

  • S. Webb , T. Rainforth , Y. W. Teh , M. P. Kumar , A Statistical Approach to Assessing Neural Network Robustness, International Conference on Learning Representations (ICLR, to appear), Apr. 2019.
    Project: bigbayes
  • Y. Zhou , B. Gram-Hansen , T. Kohn , T. Rainforth , H. Yang , F. Wood , A Low-Level Probabilistic Programming Language for Non-Differentiable Models, International Conference on Artificial Intelligence and Statistics (AISTATS, to appear), 2019.
    Project: bigbayes

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 (NeurIPS), 2018.
    Project: bigbayes
  • 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, in International Conference on Machine Learning (ICML), 2018.
    Project: bigbayes
  • A. Foster , M. Jankowiak , E. Bingham , Y. W. Teh , T. Rainforth , N. Goodman , Variational Optimal Experiment Design: Efficient Automation of Adaptive Experiments, NeurIPS Workshop on Bayesian Deep Learning, 2018.
    Project: bigbayes
  • S. Webb , A. Golinski , R. Zinkov , N. Siddharth , T. Rainforth , Y. W. Teh , F. Wood , Faithful Inversion of Generative Models for Effective Amortized Inference, Advances in Neural Information Processing Systems (NeurIPS), 2018.
    Project: bigbayes
  • A. Golinski , Y. W. Teh , F. Wood , T. Rainforth , Amortized Monte Carlo Integration, Symposium on Advances in Approximate Bayesian Inference, 2018.
    Project: bigbayes
  • E. Mathieu , T. Rainforth , S. Narayanaswamy , Y. W. Teh , Disentangling Disentanglement, NeurIPS Workshop on Bayesian Deep Learning, 2018.
    Project: bigbayes
  • T. Rainforth , Y. Zhou , X. Lu , Y. W. Teh , F. Wood , H. Yang , J. Meent , Inference Trees: Adaptive Inference with Exploration, arXiv preprint arXiv:1806.09550, 2018.
    Project: bigbayes
  • X. Lu , T. Rainforth , Y. Zhou , J. Meent , Y. W. Teh , On Exploration, Exploitation and Learning in Adaptive Importance Sampling, arXiv preprint arXiv:1810.13296, 2018.
    Project: bigbayes
  • A. Golinski , Y. W. Teh , F. Wood , T. Rainforth , Amortized Monte Carlo Integration, Symposium on Advances in Approximate Bayesian Inference, 2018.
    Project: bigbayes
  • T. Rainforth , Nesting Probabilistic Programs, Conference on Uncertainty in Artificial Intelligence (UAI), 2018.
    Project: bigbayes
  • T. Rainforth , R. Cornish , H. Yang , A. Warrington , F. Wood , On Nesting Monte Carlo Estimators, International Conference on Machine Learning (ICML), 2018.
    Project: bigbayes
  • 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.
  • 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.
    Project: bigbayes

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.