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Tom Rainforth
Tom Rainforth
Associate Professor, University of Oxford
Bestätigte E-Mail-Adresse bei stats.ox.ac.uk - Startseite
Titel
Zitiert von
Zitiert von
Jahr
Disentangling Disentanglement in Variational Autoencoders
E Mathieu, T Rainforth, N Siddharth, YW Teh
International Conference on Machine Learning, 4402-4412, 2019
3312019
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
2412019
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
2252018
Auto-Encoding Sequential Monte Carlo
TA Le, M Igl, T Rainforth, T Jin, F Wood
International Conference on Learning Representations, 2018
1862018
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
160*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
1422019
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
1212021
Canonical correlation forests
T Rainforth, F Wood
arXiv preprint arXiv:1507.05444, 2015
1172015
A Statistical Approach to Assessing Neural Network Robustness
S Webb, T Rainforth, YW Teh, MP Kumar
International Conference on Learning Representations, 2019
1002019
On statistical bias in active learning: How and when to fix it
S Farquhar, Y Gal, T Rainforth
International Conference on Learning Representations, 2021
992021
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
972022
Deep adaptive design: Amortizing sequential bayesian experimental design
A Foster, DR Ivanova, I Malik, T Rainforth
International conference on machine learning, 3384-3395, 2021
792021
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
692020
Modern Bayesian experimental design
T Rainforth, A Foster, DR Ivanova, F Bickford Smith
Statistical Science 39 (1), 100-114, 2024
582024
Capturing Label Characteristics in VAEs
T Joy, SM Schmon, PHS Torr, N Siddharth, T Rainforth
International Conference on Learning Representations, 2021
56*2021
Active testing: Sample-efficient model evaluation
J Kossen, S Farquhar, Y Gal, T Rainforth
International Conference on Machine Learning, 5753-5763, 2021
552021
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
532018
Automating inference, learning, and design using probabilistic programming
TWG Rainforth
University of Oxford, 2017
532017
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
502024
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
482021
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