Sledovat
Tom Rainforth
Tom Rainforth
Associate Professor, University of Oxford
E-mailová adresa ověřena na: stats.ox.ac.uk - Domovská stránka
Název
Citace
Citace
Rok
Disentangling Disentanglement in Variational Autoencoders
E Mathieu, T Rainforth, N Siddharth, YW Teh
International Conference on Machine Learning, 4402-4412, 2019
3562019
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
2572019
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
2392018
Auto-Encoding Sequential Monte Carlo
TA Le, M Igl, T Rainforth, T Jin, F Wood
International Conference on Learning Representations, 2018
1982018
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
177*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
1702019
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
1532022
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
1482021
Canonical correlation forests
T Rainforth, F Wood
arXiv preprint arXiv:1507.05444, 2015
1212015
A Statistical Approach to Assessing Neural Network Robustness
S Webb, T Rainforth, YW Teh, MP Kumar
International Conference on Learning Representations, 2019
1092019
On statistical bias in active learning: How and when to fix it
S Farquhar, Y Gal, T Rainforth
International Conference on Learning Representations, 2021
1062021
Modern Bayesian experimental design
T Rainforth, A Foster, DR Ivanova, F Bickford Smith
Statistical Science 39 (1), 100-114, 2024
1052024
Selfcheck: Using LLMs to zero-shot check their own step-by-step reasoning
N Miao, YW Teh, T Rainforth
International Conference on Learning Representations, 2024
1002024
Deep adaptive design: Amortizing sequential bayesian experimental design
A Foster, DR Ivanova, I Malik, T Rainforth
International conference on machine learning, 3384-3395, 2021
1002021
Generative flows on discrete state-spaces: Enabling multimodal flows with applications to protein co-design
A Campbell, J Yim, R Barzilay, T Rainforth, T Jaakkola
International Conference on Machine Learning, 2024
882024
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
812020
Active testing: Sample-efficient model evaluation
J Kossen, S Farquhar, Y Gal, T Rainforth
International Conference on Machine Learning, 5753-5763, 2021
682021
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
632021
Do Bayesian Neural Networks Need To Be Fully Stochastic?
M Sharma, S Farquhar, E Nalisnick, T Rainforth
International Conference on Artificial Intelligence and Statistics, 2023
612023
Capturing Label Characteristics in VAEs
T Joy, SM Schmon, PHS Torr, N Siddharth, T Rainforth
International Conference on Learning Representations, 2021
60*2021
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Články 1–20