Tom Rainforth
TitleCited byYear
Auto-Encoding Sequential Monte Carlo
TA Le, M Igl, T Rainforth, T Jin, F Wood
International Conference on Learning Representations, 2018
352018
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
272018
Canonical correlation forests
T Rainforth, F Wood
arXiv preprint arXiv:1507.05444, 2015
272015
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
192016
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
18*2018
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
132016
Automating inference, learning, and design using probabilistic programming
TWG Rainforth
University of Oxford, 2017
102017
On the pitfalls of nested Monte Carlo
T Rainforth, R Cornish, H Yang, F Wood
arXiv preprint arXiv:1612.00951, 2016
82016
Nesting Probabilistic Programs
T Rainforth
Uncertainty in Artificial Intelligence (UAI), 2018
7*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
32018
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
32018
The DARC Toolbox: automated, flexible, and efficient delayed and risky choice experiments using Bayesian adaptive design
BT Vincent, T Rainforth
PsyArXiv. October 20, 2017
32017
Statistical verification of neural networks
S Webb, T Rainforth, YW Teh, MP Kumar
arXiv preprint arXiv:1811.07209, 2018
22018
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
22016
Hamiltonian Monte Carlo for Probabilistic Programs with Discontinuities
B Gram-Hansen, Y Zhou, T Kohn, T Rainforth, H Yang, F Wood
arXiv preprint arXiv:1804.03523, 2018
12018
Sampling and inference for discrete random probability measures in probabilistic programs
B Bloem-Reddy, E Mathieu, A Foster, T Rainforth, YW Teh, H Ge, ...
NIPS Workshop on Advances in Approximate Bayesian Inference, 2017
12017
Variational Estimators for Bayesian Optimal Experimental Design
A Foster, M Jankowiak, E Bingham, P Horsfall, YW Teh, T Rainforth, ...
arXiv preprint arXiv:1903.05480, 2019
2019
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
arXiv preprint arXiv:1903.02482, 2019
2019
A Statistical Approach to Assessing Neural Network Robustness
S Webb, T Rainforth, YW Teh, MP Kumar
International Conference on Learning Representations, 2019
2019
Disentangling Disentanglement in Variational Auto-Encoders
E Mathieu, T Rainforth, S Narayanaswamy, YW Teh
arXiv preprint arXiv:1812.02833, 2018
2018
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Articles 1–20