Tom Rainforth
Tom Rainforth
Postdoc, Department of Statistics, University of Oxford
Verified email at robots.ox.ac.uk - Homepage
TitleCited byYear
Auto-Encoding Sequential Monte Carlo
TA Le, M Igl, T Rainforth, T Jin, F Wood
International Conference on Learning Representations, 2018
282018
Canonical correlation forests
T Rainforth, F Wood
arXiv preprint arXiv:1507.05444, 2015
232015
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
182016
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
132018
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
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
12*2018
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
62018
Automating Inference, Learning, and Design using Probabilistic Programming
T Rainforth
http://www.robots.ox.ac.uk/~twgr/assets/pdf/rainforth2017thesis.pdf, 2017
62017
The DARC Toolbox: automated, flexible, and efficient delayed and risky choice experiments using Bayesian adaptive design
BT Vincent, T Rainforth
PsyArXiv, 2017
42017
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
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
12018
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
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
Statistical Verification of Neural Networks
S Webb, T Rainforth, YW Teh, MP Kumar
arXiv preprint arXiv:1811.07209, 2018
2018
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
2018
Amortized Monte Carlo Integration
A Golinski, YW Teh, F Wood, T Rainforth
UAI 2018 Workshop on Uncertainty in Deep Learning, 2018
2018
Interacting particle Markov chain Monte Carlo
A Doucet, T Rainforth, CA Naesseth, F Lindsten, B Paige, F Wood, ...
2016
Interacting Particle Markov Chain Monte Carlo - Supplementary Material
T Rainforth, CA Naesseth, F Lindsten, B Paige, JW van de Meent, ...
2016
On Nesting Monte Carlo Estimators–Supplementary Material
T Rainforth, R Cornish, HYA Warrington, F Wood
The system can't perform the operation now. Try again later.
Articles 1–20