Tom Rainforth

Tom Rainforth

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

I am a Florence Nightingale Bicentennial Fellow and Tutor in Statistics and Probability. Please see my personal website here for further details.

Publications

2023

  • F. Bickford Smith , A. Kirsch , S. Farquhar , Y. Gal , A. Foster , T. Rainforth , Prediction-oriented Bayesian active learning, International Conference on Artificial Intelligence and Statistics, 2023.
  • T. Rainforth , A. Foster , D. R. Ivanova , F. Bickford Smith , Modern Bayesian experimental design, Statistical Science (to appear), 2023.
  • T. Reichelt , L. Ong , T. Rainforth , Pitfalls of Full Bayesian Inference in Universal Probabilistic Programming, in POPL Workshop on Languages for Inference (LAFI), 2023.

2022

  • A. Campbell , J. Benton , V. De Bortoli , T. Rainforth , G. Deligiannidis , A. Doucet , A Continuous Time Framework for Discrete Denoising Models, arXiv preprint arXiv:2205.14987, 2022.
  • J. Kossen , S. Farquhar , Y. Gal , T. Rainforth , Active Surrogate Estimators: An Active Learning Approach to Label-Efficient Model Evaluation, in Advances in Neural Information Processing Systems, 2022.
  • T. Reichelt , L. Ong , T. Rainforth , Rethinking Variational Inference for Probabilistic Programs with Stochastic Support, in Advances in Neural Information Processing Systems, 2022.
  • T. Reichelt , A. Goliński , L. Ong , T. Rainforth , Expectation programming: Adapting probabilistic programming systems to estimate expectations efficiently, in Proceedings of the Thirty-Eighth Conference on Uncertainty in Artificial Intelligence, 2022.

2021

  • A. Foster , R. Pukdee , T. Rainforth , Improving Transformation Invariance in Contrastive Representation Learning, International Conference on Learning Representations (ICLR), 2021.
  • A. Foster , D. R. Ivanova , I. Malik , T. Rainforth , Deep Adaptive Design: Amortizing Sequential Bayesian Experimental Design, International Conference on Machine Learning (ICML, long presentation), 2021.
  • D. R. Ivanova , A. Foster , S. Kleinegesse , M. U. Gutmann , T. Rainforth , Implicit Deep Adaptive Design: Policy-Based Experimental Design without Likelihoods, 35th Conference on Neural Information Processing Systems (NeurIPS 2021), 2021.
  • J. Kossen , N. Band , C. Lyle , A. N. Gomez , T. Rainforth , Y. Gal , Self-Attention Between Datapoints: Going Beyond Individual Input-Output Pairs in Deep Learning, Advances in Neural Information Processing Systems, 2021.
  • J. Kossen , S. Farquhar , Y. Gal , T. Rainforth , Active Testing: Sample-Efficient Model Evaluation, International Conference on Machine Learning, 2021.
  • A. Campbell , Y. Shi , T. Rainforth , A. Doucet , Online Variational Filtering and Parameter Learning, in Advances in Neural Information Processing Systems, 2021.
  • M. Willetts , A. Camuto , T. Rainforth , S. Roberts , C. Holmes , Improving VAEs’ Robustness to Adversarial Attack, in International Conference on Learning Representations (ICLR), 2021.
  • A. Camuto , M. Willetts , S. Roberts , C. Holmes , T. Rainforth , Towards a Theoretical Understanding of the Robustness of Variational Autoencoders, in International Conference on Artificial Intelligence and Statistics (AISTATS), 2021.
  • B. Barrett , A. Camuto , M. Willetts , T. Rainforth , Certifiably Robust Variational Autoencoders , in arXiv preprint, 2021.
  • J. Xu , H. Kim , T. Rainforth , Y. W. Teh , Group Equivariant Subsampling, in Neural Information Processing Systems (NeurIPS), 2021.
    Project: tencent-lsml

2020

  • A. Foster , M. Jankowiak , M. O’Meara , Y. W. Teh , T. Rainforth , A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments, International Conference on Artificial Intelligence and Statistics (AISTATS), 2020.
    Project: tencent-lsml
  • T. Joy , S. M. Schmon , P. Torr , S. Narayanaswamy , T. Rainforth , Rethinking Semi–Supervised Learning in VAEs, https://arxiv.org/abs/2006.10102, 2020.

2019

  • S. Webb , T. Rainforth , Y. W. Teh , M. P. Kumar , A Statistical Approach to Assessing Neural Network Robustness, in International Conference on Learning Representations (ICLR), 2019.
    Project: bigbayes
  • B. Gram-Hansen , C. S. Witt , T. Rainforth , P. H. Torr , Y. W. Teh , A. G. Baydin , Hijacking Malaria Simulators with Probabilistic Programming, in International Conference on Machine Learning (ICML) AI for Social Good workshop (AI4SG), 2019.
  • E. Mathieu , T. Rainforth , N. Siddharth , Y. W. Teh , Disentangling Disentanglement in Variational Autoencoders, in Proceedings of the 36th International Conference on Machine Learning, Long Beach, California, USA, 2019, vol. 97, 4402–4412.
    Project: bigbayes
  • A. Foster , M. Jankowiak , E. Bingham , P. Horsfall , Y. W. Teh , T. Rainforth , N. Goodman , Variational Bayesian Optimal Experimental Design, Advances in Neural Information Processing Systems (NeurIPS, spotlight), 2019.
    Project: bigbayes
  • F. Locatello , G. Abbati , T. Rainforth , S. Bauer , B. Schölkopf , O. Bachem , On the Fairness of Disentangled Representations, Advances in Neural Information Processing Systems (NeurIPS, to appear), 2019.
    Project: bigbayes
  • A. Golinski , F. Wood , T. Rainforth , Amortized Monte Carlo Integration, International Conference on Machine Learning (ICML, Best Paper honorable mention), 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), 2019.
    Project: bigbayes
  • A. Golinski* , M. Lezcano-Casado* , T. Rainforth , Improving Normalizing Flows via Better Orthogonal Parameterizations, ICML Workshop on Invertible Neural Nets and Normalizing Flows, 2019.
    Project: bigbayes
  • Y. Zhou , B. Gram-Hansen , T. Kohn , T. Rainforth , H. Yang , F. Wood , LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models, in The 22nd International Conference on Artificial Intelligence and Statistics, 2019, 148–157.
    Project: bigbayes
  • Y. Zhou , H. Yang , Y. W. Teh , T. Rainforth , Divide, Conquer, and Combine: a New Inference Strategy for Probabilistic Programs with Stochastic Support, International Conference on Machine Learning (ICML, to appear), 2019.
    Project: bigbayes

2018

  • A. Golinski , Y. W. Teh , F. Wood , T. Rainforth , Amortized Monte Carlo Integration, in Symposium on Advances in Approximate Bayesian Inference, 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, in Advances in Neural Information Processing Systems (NeurIPS), 2018.
    Project: bigbayes
  • B. Gram-Hansen , Y. Zhou , T. Kohn , T. Rainforth , H. Yang , F. Wood , Hamiltonian Monte Carlo for Probabilistic Programs with Discontinuities, in International Conference on Probabilistic Programming, 2018.
  • 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
  • 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.

Software

2019

  • 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). 2019.
    Project: bigbayes