Tom Rainforth
Cited by
Cited by
Tighter Variational Bounds are Not Necessarily Better
T Rainforth, AR Kosiorek, TA Le, CJ Maddison, M Igl, F Wood, YW Teh
Proceedings of the 35rd International Conference on Machine Learning 80 …, 2018
Auto-Encoding Sequential Monte Carlo
TA Le, M Igl, T Rainforth, T Jin, F Wood
International Conference on Learning Representations, 2018
Canonical correlation forests
T Rainforth, F Wood
arXiv preprint arXiv:1507.05444, 2015
On Nesting Monte Carlo Estimators
T Rainforth, R Cornish, H Yang, A Warrington, F Wood
Proceedings of the 35th International Conference on Machine Learning 80 …, 2018
Disentangling Disentanglement in Variational Autoencoders
E Mathieu, T Rainforth, N Siddharth, YW Teh
International Conference on Machine Learning, 4402-4412, 2019
Bayesian optimization for probabilistic programs
T Rainforth, TA Le, JW van de Meent, MA Osborne, F Wood
Advances in Neural Information Processing Systems, 280-288, 2016
Interacting Particle Markov Chain Monte Carlo
T Rainforth, CA Naesseth, F Lindsten, B Paige, JW van de Meent, ...
Proceedings of the 33rd International Conference on Machine Learning 48 …, 2016
Automating inference, learning, and design using probabilistic programming
TWG Rainforth
University of Oxford, 2017
On the fairness of disentangled representations
F Locatello, G Abbati, T Rainforth, S Bauer, B Schölkopf, O Bachem
Advances in Neural Information Processing Systems, 14611-14624, 2019
Faithful Inversion of Generative Models for Effective Amortized Inference
S Webb, A Golinski, R Zinkov, S Narayanaswamy, T Rainforth, YW Teh, ...
Advances in Neural Information Processing Systems, 3073-3083, 2018
LF-PPL: A Low-Level First Order Probabilistic Programming Language for Non-Differentiable Models
Y Zhou, BJ Gram-Hansen, T Kohn, T Rainforth, H Yang, F Wood
The 22nd International Conference on Artificial Intelligence and Statistics …, 2019
A Statistical Approach to Assessing Neural Network Robustness
S Webb, T Rainforth, YW Teh, MP Kumar
International Conference on Learning Representations, 2019
Inference Trees: Adaptive Inference with Exploration
T Rainforth, Y Zhou, X Lu, YW Teh, F Wood, H Yang, JW van de Meent
arXiv preprint arXiv:1806.09550, 2018
Nesting Probabilistic Programs
T Rainforth
Uncertainty in Artificial Intelligence (UAI), 2018
Variational Bayesian optimal experimental design
A Foster, M Jankowiak, E Bingham, P Horsfall, YW Teh, T Rainforth, ...
Advances in Neural Information Processing Systems, 14036-14047, 2019
On exploration, exploitation and learning in adaptive importance sampling
X Lu, T Rainforth, Y Zhou, JW van de Meent, YW Teh
arXiv preprint arXiv:1810.13296, 2018
The DARC Toolbox: automated, flexible, and efficient delayed and risky choice experiments using Bayesian adaptive design
BT Vincent, T Rainforth
PsyArXiv. October 20, 2017
Probabilistic structure discovery in time series data
D Janz, B Paige, T Rainforth, JW van de Meent, F Wood
arXiv preprint arXiv:1611.06863, 2016
Hijacking malaria simulators with probabilistic programming
B Gram-Hansen, CS de Witt, T Rainforth, PHS Torr, YW Teh, AG Baydin
arXiv preprint arXiv:1905.12432, 2019
A note on blind contact tracing at scale with applications to the COVID-19 pandemic
JK Fitzsimons, A Mantri, R Pisarczyk, T Rainforth, Z Zhao
arXiv preprint arXiv:2004.05116, 2020
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