| Variational continual learning CV Nguyen, Y Li, TD Bui, RE Turner International Conference on Learning Representations, 2018 | 983 | 2018 |
| Gaussian process behaviour in wide deep neural networks Matthews, J Hron, M Rowland, RE Turner, Z Ghahramani International Conference on Learning Representations 4, 2018 | 528 | 2018 |
| Nonlinear ICA using auxiliary variables and generalized contrastive learning A Hyvarinen, H Sasaki, R Turner The 22nd international conference on artificial intelligence and statistics …, 2019 | 486 | 2019 |
| Invariant models for causal transfer learning M Rojas-Carulla, B Schölkopf, R Turner, J Peters Journal of Machine Learning Research 19 (36), 1-34, 2018 | 477 | 2018 |
| Q-prop: Sample-efficient policy gradient with an off-policy critic S Gu, T Lillicrap, Z Ghahramani, RE Turner, S Levine arXiv preprint arXiv:1611.02247, 2016 | 453 | 2016 |
| Two problems with variational expectation maximisation for time-series models RE Turner, M Sahani | 445* | 2011 |
| Rényi divergence variational inference Y Li, RE Turner Advances in neural information processing systems 29, 2016 | 419 | 2016 |
| Meta-learning probabilistic inference for prediction J Gordon, J Bronskill, M Bauer, S Nowozin, RE Turner International Conference on Learning Representations, 2019 | 365 | 2019 |
| Practical deep learning with Bayesian principles K Osawa, S Swaroop, ME Khan, A Jain, R Eschenhagen, RE Turner, ... Advances in neural information processing systems 32, 2019 | 351 | 2019 |
| A foundation model for the Earth system C Bodnar, WP Bruinsma, A Lucic, M Stanley, A Allen, J Brandstetter, ... Nature, 1-8, 2025 | 331* | 2025 |
| Fast and flexible multi-task classification using conditional neural adaptive processes J Requeima, J Gordon, J Bronskill, S Nowozin, RE Turner Advances in neural information processing systems 32, 2019 | 330 | 2019 |
| Black-box alpha divergence minimization J Hernandez-Lobato, Y Li, M Rowland, T Bui, D Hernández-Lobato, ... International conference on machine learning, 1511-1520, 2016 | 309 | 2016 |
| Deep Gaussian processes for regression using approximate expectation propagation T Bui, D Hernández-Lobato, J Hernandez-Lobato, Y Li, R Turner International conference on machine learning, 1472-1481, 2016 | 304 | 2016 |
| The processing and perception of size information in speech sounds DRR Smith, RD Patterson, R Turner, H Kawahara, T Irino The Journal of the Acoustical Society of America 117 (1), 305-318, 2005 | 298 | 2005 |
| Deterministic variational inference for robust bayesian neural networks A Wu, S Nowozin, E Meeds, RE Turner, JM Hernandez-Lobato, AL Gaunt International Conference on Learning Representations, 2019 | 275 | 2019 |
| Sequence tutor: Conservative fine-tuning of sequence generation models with kl-control N Jaques, S Gu, D Bahdanau, JM Hernández-Lobato, RE Turner, D Eck International Conference on Machine Learning, 1645-1654, 2017 | 256 | 2017 |
| On sparse variational methods and the Kullback-Leibler divergence between stochastic processes RETZG Alexander G. Matthews, James Hensman Proceedings of the 19th International Conference on Artificial Intelligence …, 2016 | 240* | 2016 |
| Convolutional conditional neural processes J Gordon, WP Bruinsma, AYK Foong, J Requeima, Y Dubois, RE Turner International Conference on Learning Representations, 2020 | 228 | 2020 |
| A unifying framework for Gaussian process pseudo-point approximations using power expectation propagation TD Bui, J Yan, RE Turner Journal of Machine Learning Research 18 (104), 1-72, 2017 | 219 | 2017 |
| Interpolated policy gradient: Merging on-policy and off-policy gradient estimation for deep reinforcement learning SS Gu, T Lillicrap, RE Turner, Z Ghahramani, B Schölkopf, S Levine Advances in neural information processing systems 30, 2017 | 217 | 2017 |
José Miguel Hernández-LobatoProfessor of Machine Learning, University of CambridgeVerified email at cam.ac.uk
