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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
2042019
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
1792018
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
1602019
Auto-Encoding Sequential Monte Carlo
TA Le, M Igl, T Rainforth, T Jin, F Wood
International Conference on Learning Representations, 2018
1502018
Canonical correlation forests
T Rainforth, F Wood
arXiv preprint arXiv:1507.05444, 2015
1012015
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
100*2018
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
802019
A Statistical Approach to Assessing Neural Network Robustness
S Webb, T Rainforth, YW Teh, MP Kumar
International Conference on Learning Representations, 2019
652019
On statistical bias in active learning: How and when to fix it
S Farquhar, Y Gal, T Rainforth
International Conference on Learning Representations, 2021
402021
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
372017
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
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
342018
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
292020
Capturing Label Characteristics in VAEs
T Joy, SM Schmon, PHS Torr, N Siddharth, T Rainforth
International Conference on Learning Representations, 2021
28*2021
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
272021
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
Deep adaptive design: Amortizing sequential bayesian experimental design
A Foster, DR Ivanova, I Malik, T Rainforth
International Conference on Machine Learning, 3384-3395, 2021
262021
Nesting Probabilistic Programs
T Rainforth
Uncertainty in Artificial Intelligence (UAI), 2018
192018
Improving VAEs' Robustness to Adversarial Attack
M Willetts, A Camuto, T Rainforth, S Roberts, C Holmes
International Conference on Learning Representations, 2021
18*2021
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