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Tom Rainforth
Tom Rainforth
Florence Nightingale Bicentennial Fellow, University of Oxford
Verified email at stats.ox.ac.uk - Homepage
Title
Cited by
Cited by
Year
Disentangling Disentanglement in Variational Autoencoders
E Mathieu, T Rainforth, N Siddharth, YW Teh
International Conference on Machine Learning, 4402-4412, 2019
2142019
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
1832018
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
1692019
Auto-Encoding Sequential Monte Carlo
TA Le, M Igl, T Rainforth, T Jin, F Wood
International Conference on Learning Representations, 2018
1542018
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
105*2018
Canonical correlation forests
T Rainforth, F Wood
arXiv preprint arXiv:1507.05444, 2015
1012015
Variational Bayesian optimal experimental design
A Foster, M Jankowiak, E Bingham, P Horsfall, YW Teh, T Rainforth, ...
Advances in Neural Information Processing Systems 32, 2019
862019
A Statistical Approach to Assessing Neural Network Robustness
S Webb, T Rainforth, YW Teh, MP Kumar
International Conference on Learning Representations, 2019
682019
On statistical bias in active learning: How and when to fix it
S Farquhar, Y Gal, T Rainforth
International Conference on Learning Representations, 2021
432021
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
392016
Automating inference, learning, and design using probabilistic programming
TWG Rainforth
University of Oxford, 2017
382017
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
372018
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
352016
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
332020
Deep adaptive design: Amortizing sequential bayesian experimental design
A Foster, DR Ivanova, I Malik, T Rainforth
International Conference on Machine Learning, 3384-3395, 2021
322021
Self-attention between datapoints: Going beyond individual input-output pairs in deep learning
J Kossen, N Band, C Lyle, AN Gomez, T Rainforth, Y Gal
Advances in Neural Information Processing Systems 34, 28742-28756, 2021
312021
Capturing Label Characteristics in VAEs
T Joy, SM Schmon, PHS Torr, N Siddharth, T Rainforth
International Conference on Learning Representations, 2021
31*2021
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
27*2019
Active testing: Sample-efficient model evaluation
J Kossen, S Farquhar, Y Gal, T Rainforth
International Conference on Machine Learning, 5753-5763, 2021
192021
Nesting Probabilistic Programs
T Rainforth
Uncertainty in Artificial Intelligence (UAI), 2018
192018
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