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
562018
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
552018
Canonical correlation forests
T Rainforth, F Wood
arXiv preprint arXiv:1507.05444, 2015
372015
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
31*2018
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
212016
Automating inference, learning, and design using probabilistic programming
TWG Rainforth
University of Oxford, 2017
172017
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
162016
Disentangling Disentanglement in Variational Autoencoders
E Mathieu, T Rainforth, N Siddharth, YW Teh
International Conference on Machine Learning, 4402-4412, 2019
10*2019
Nesting Probabilistic Programs
T Rainforth
Uncertainty in Artificial Intelligence (UAI), 2018
10*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
82018
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
82018
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
5*2019
A Statistical Approach to Assessing Neural Network Robustness
S Webb, T Rainforth, YW Teh, MP Kumar
International Conference on Learning Representations, 2019
5*2019
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
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
32016
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
2*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
22018
On the Fairness of Disentangled Representations
F Locatello, G Abbati, T Rainforth, S Bauer, B Schölkopf, O Bachem
arXiv preprint arXiv:1905.13662, 2019
12019
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
A Unified Stochastic Gradient Approach to Designing Bayesian-Optimal Experiments
A Foster, M Jankowiak, M O'Meara, YW Teh, T Rainforth
arXiv preprint arXiv:1911.00294, 2019
2019
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