| Disentangling Disentanglement in Variational Autoencoders E Mathieu, T Rainforth, N Siddharth, YW Teh International Conference on Machine Learning, 4402-4412, 2019 | 317 | 2019 |
| 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 | 228 | 2019 |
| 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 | 222 | 2018 |
| Auto-Encoding Sequential Monte Carlo TA Le, M Igl, T Rainforth, T Jin, F Wood International Conference on Learning Representations, 2018 | 178 | 2018 |
| 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 | 153* | 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 | 133 | 2019 |
| Canonical correlation forests T Rainforth, F Wood arXiv preprint arXiv:1507.05444, 2015 | 114 | 2015 |
| 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 | 106 | 2021 |
| A Statistical Approach to Assessing Neural Network Robustness S Webb, T Rainforth, YW Teh, MP Kumar International Conference on Learning Representations, 2019 | 95 | 2019 |
| On statistical bias in active learning: How and when to fix it S Farquhar, Y Gal, T Rainforth International Conference on Learning Representations, 2021 | 93 | 2021 |
| 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 | 82 | 2022 |
| Deep adaptive design: Amortizing sequential bayesian experimental design A Foster, DR Ivanova, I Malik, T Rainforth International conference on machine learning, 3384-3395, 2021 | 71 | 2021 |
| 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 | 62 | 2020 |
| Capturing Label Characteristics in VAEs T Joy, SM Schmon, PHS Torr, N Siddharth, T Rainforth International Conference on Learning Representations, 2021 | 51* | 2021 |
| 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 | 51 | 2018 |
| Automating inference, learning, and design using probabilistic programming TWG Rainforth University of Oxford, 2017 | 51 | 2017 |
| Active testing: Sample-efficient model evaluation J Kossen, S Farquhar, Y Gal, T Rainforth International Conference on Machine Learning, 5753-5763, 2021 | 49 | 2021 |
| Modern Bayesian experimental design T Rainforth, A Foster, DR Ivanova, F Bickford Smith Statistical Science 39 (1), 100-114, 2024 | 42 | 2024 |
| 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 | 40 | 2016 |
| 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 | 39 | 2021 |
Yee Whye TehProfessor of Statistical Machine Learning, Oxford, Research Scientist, DeepMind在 stats.ox.ac.uk 的电子邮件经过验证
