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Tom Rainforth
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Disentangling Disentanglement in Variational Autoencoders
E Mathieu, T Rainforth, N Siddharth, YW Teh
International Conference on Machine Learning, 4402-4412, 2019
3092019
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
2252019
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
2152018
Auto-Encoding Sequential Monte Carlo
TA Le, M Igl, T Rainforth, T Jin, F Wood
International Conference on Learning Representations, 2018
1772018
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
150*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
1282019
Canonical correlation forests
T Rainforth, F Wood
arXiv preprint arXiv:1507.05444, 2015
1102015
A Statistical Approach to Assessing Neural Network Robustness
S Webb, T Rainforth, YW Teh, MP Kumar
International Conference on Learning Representations, 2019
912019
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
892021
On statistical bias in active learning: How and when to fix it
S Farquhar, Y Gal, T Rainforth
International Conference on Learning Representations, 2021
882021
A Continuous Time Framework for Discrete Denoising Models
A Campbell, J Benton, V De Bortoli, T Rainforth, G Deligiannidis, ...
Advances in Neural Information Processing Systems, 2022
682022
Deep adaptive design: Amortizing sequential bayesian experimental design
A Foster, DR Ivanova, I Malik, T Rainforth
International conference on machine learning, 3384-3395, 2021
672021
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
602020
Automating inference, learning, and design using probabilistic programming
TWG Rainforth
University of Oxford, 2017
502017
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
492018
Capturing Label Characteristics in VAEs
T Joy, SM Schmon, PHS Torr, N Siddharth, T Rainforth
International Conference on Learning Representations, 2021
47*2021
Active testing: Sample-efficient model evaluation
J Kossen, S Farquhar, Y Gal, T Rainforth
International Conference on Machine Learning, 5753-5763, 2021
462021
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
402016
Implicit deep adaptive design: Policy-based experimental design without likelihoods
DR Ivanova, A Foster, S Kleinegesse, MU Gutmann, T Rainforth
Advances in Neural Information Processing Systems 34, 25785-25798, 2021
382021
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
342016
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