Tom Rainforth
Tom Rainforth
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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
Disentangling Disentanglement in Variational Autoencoders
E Mathieu, T Rainforth, N Siddharth, YW Teh
International Conference on Machine Learning, 4402-4412, 2019
On the fairness of disentangled representations
F Locatello, G Abbati, T Rainforth, S Bauer, B Schölkopf, O Bachem
Advances in Neural Information Processing Systems, 2019
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
Canonical correlation forests
T Rainforth, F Wood
arXiv preprint arXiv:1507.05444, 2015
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
A Statistical Approach to Assessing Neural Network Robustness
S Webb, T Rainforth, YW Teh, MP Kumar
International Conference on Learning Representations, 2019
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
Variational bayesian optimal experimental design
A Foster, M Jankowiak, E Bingham, P Horsfall, YW Teh, T Rainforth, ...
arXiv preprint arXiv:1903.05480, 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
Nesting Probabilistic Programs
T Rainforth
Uncertainty in Artificial Intelligence (UAI), 2018
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
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
Proceedings of the 15th International Conference on Availability …, 2020
Improving normalizing flows via better orthogonal parameterizations
A Golinski, M Lezcano-Casado, T Rainforth
ICML Workshop on Invertible Neural Networks and Normalizing Flows, 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
A unified stochastic gradient approach to designing bayesian-optimal experiments
A Foster, M Jankowiak, M O’Meara, YW Teh, T Rainforth
International Conference on Artificial Intelligence and Statistics, 2959-2969, 2020
The DARC Toolbox: automated, flexible, and efficient delayed and risky choice experiments using Bayesian adaptive design
BT Vincent, T Rainforth
PsyArXiv. October 20, 2017
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